Agent architecture mental models
Layered breakdowns of what an agent is: brain/goal/system prompt, tools, memory, the perceive-think-act loop, and how those pieces compose into reliable systems.
70%
Best tweets about AI Agents
50 Best Tweets About AI Agents (2026)
Browse the best tweets about AI agents, agentic workflows, autonomous systems, tool use, memory, and production lessons. Updated every two weeks.
Builders sharing concrete agent architectures, evaluations, failures, deployment lessons, and useful demonstrations.
- Creators
- 43
- Updated
Original Xholic analysis
What 50 top AI Agents posts reveal
This conversation of 50 posts from 43 creators skews cautionary in stance (56% cautionary vs 44% supportive) while remaining positive in tone (64% positive, 32% neutral, 4% negative) — builders are upbeat but emphasize pitfalls and lessons. Engagement is highly concentrated: five outlier posts score 74x–184x the all-time median (median all-time score 11.38), led by a post by @cherry_mx_reds reporting that Anthropic now recommends removing unnecessary subagents from AI systems. Tutorials are the highest-median format (median all-time score 38.3, vs 10.885 for announcements and 10.387 for lists), and posts with media (88% of the sample) outperform text-only posts (media median 14.79 vs 2.26)..
- Dominant tone
- Positive
- Median score
- 11.4
- Leading format
- List
- Recent posts
- 16%
64% of posts
All-time engagement
54% of posts
Published in 90 days
Conversation map
The themes creators return to
Memory and context engineering
Working/semantic/procedural/episodic memory types, context compression, CLAUDE.md / AGENTS.md as onboarding docs, knowledge bases and 'company brain' building.
40%
Multi-agent coordination patterns
Design patterns for connecting agents — orchestrator, agent-to-agent, subagents-as-API, role-based teams, graph workflows, durable/long-running swarms — and when not to use multiple agents.
32%
Evaluation and testing
How to evaluate agents: outcome-based graders vs step-checking, code/model/human graders, test-case building from real failures, running evals before and after deployment.
30%
Frameworks and platforms
Practical coverage of agent frameworks and stacks: LangChain/LangGraph, CrewAI, AutoGen, Strands SDK, n8n/low-code, Claude Code as an agent development platform, Microsoft Agent Lightning.
30%
Tools, MCP, and protocols
Tool and schema design, MCP as the tool-use and interoperability layer (X's hosted MCP server, A2A), keeping logic out of the LLM and in tools.
16%
Self-improving and auto-tuned agents
Agents that build, score, and improve themselves in a loop: AutoAgent's meta-agent, meta-agent failure-coaching, RL-based optimization, recursive self-improvement from task feedback, workflow evolution frameworks like Mimosa (also covered:
10%
Simplicity beats complexity
Recurring lesson that simpler agents outperform complex ones: Anthropic recommending removing subagents, tool design over prompt design, one agent when one is enough.
10%
Tone and stance
SentimentPositive leads
Author postureCautionary leads
Performance benchmark
- Median likes
- 37
- Median reposts
- 6
- Median replies
- 7
- Median views
- 2.5K
Posts with media make up 88% of this collection. Their median all-time score is 14.8, compared with 2.26 for text-only posts.
Format mix
- List54% · score 10.4
- Announcement26% · score 10.9
- Tutorial20% · score 38.3
Consensus and debate
Where creators agree, and where they do not
Shared view
Simpler agents beat complex ones
Multiple builders converge on the lesson that simpler agents outperform complex multi-agent systems: a post by @cherry_mx_reds reports Anthropic finding a single agent with skills frequently outperforms complex multi-agent setups; a post by @CodeByPoonam summarizing an Anthropic engineer states 'the best agents are simple. Complexity kills reliability'; and @hrswatigupta's rule is 'start with one agent' and add more.
Shared view
Agents are model + loop + tools + memory/context
Posts repeatedly define agents the same layered way: a reasoning model driving a perceive-think-act loop, acting through tools, with memory and context as distinct components.
Shared view
Evaluation is critical and should test outcomes, not steps
Evaluation recurs as both under-practiced and decisive: Anthropic's playbook (summarized by @akshay_pachaar) says to test final outputs rather than intermediate steps, a post by @CodeByPoonam calls evaluation 'the hardest and most ignored part,' and @pauliusztin_ advises always evaluating on your own data.
Shared view
Memory and context engineering is a key differentiator
Builders treat structured memory and context (working/semantic/procedural/episodic layers, CLAUDE.md/AGENTS.md onboarding docs, persistent 'company brain' knowledge bases) as what separates production agents from demos, with several posts noting memory and testing are 'where most agents die in production.'
Open debate
When multi-agent systems are worth it
There is a live tension between posts promoting specialized multi-agent teams — @ _vmlops relaying Andrew Ng's framework of reflection, tool use, planning, and multi-agent specialization, and @xelebofficial's seven-layer architecture with supervisor/worker/critic roles — and posts arguing for consolidation, such as Anthropic's reported push to fold subagents into a single agent and @hrswatigupta's warning that 'more'
Open debate
What actually counts as an agent
Definitions conflict: @vicky_grok argues most 'agents' shared online are orchestrated workflows following predefined sequences, not true agents; @lennysan highlights how overloaded the term has become, spanning 6-week n8n automations to 6-month ML-team bets; while other posts apply the label broadly to any tool-calling loop.
Open debate
Autonomy claims vs supervision reality
Some posts describe increasingly autonomous and self-improving agents (e.g., @ihteshamali on AutoAgent's self-improving loop, @AIwithArsalan on recursive self-improvement), while @pascal_bornet's 'Supervision Trap' post argues most organizations claiming near-autonomous agents actually operate at lower autonomy levels with a hidden human layer keeping systems alive, and @rohanpaul_ai relays an autonomy-ladder, human-
What performs
Patterns behind standout posts
Engagement is extremely top-heavy
Five outlier posts score 74x–184x the all-time median score of 11.38: tweetId 2113969070118666670 (184.36x, on Anthropic's subagent-removal advice), 2034052610664116550 (138.52x, an agents-101 masterclass), 2036724091143463120 (131.03x, agent-economy infrastructure stack), 2038143522000589241 (76.84x, Microsoft Agent Lightning), and 2087703542979014958 (74.41x, an AI-workforce playbook).
Media and tutorials outperform
88% of posts include media; the median all-time score for media posts is 14.79 vs 2.26 for text-only posts. Among formats, tutorials have the highest median (38.3), well above lists (10.387) and announcements (10.885).
Theme medians favor simplicity and tools/MCP — on small samples
Theme median all-time scores are led by simplicity-over-complexity (42.275, n=5) and tools/MCP/protocols (30.59, n=8), followed by self-improving agents (26.41, n=5) and frameworks/platforms (24.892, n=15); agent-architecture mental models and evaluation themes sit lower (11.006 and 9.709). Note the top two leaders rest on only 5–8 posts each.
Baseline engagement and freshness
The typical post earns modest engagement (medians: 37 likes, 2,455 views, 7 replies, 6 reposts), making the outliers' multiples stand out further. Freshness is limited: median post age is 162.6 days and only 8 of 50 posts (16%) were published within the last 90 days.
Statistical standouts
- View standout post 1Score 2098.0 · 184.36× median
- View standout post 2Score 1576.4 · 138.52× median
- View standout post 3Score 1491.1 · 131.03× median
- View standout post 4Score 874.5 · 76.84× median
- View standout post 5Score 846.8 · 74.41× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
1. Vaishnavi
@_vmlops
2 posts
2. GREG ISENBERG
@gregisenberg
2 posts
3. HackerNoon | Learn Any Technology
@hackernoon
2 posts
4. Pascal Bornet
@pascal_bornet
2 posts
5. Paul Iusztin
@pauliusztin_
2 posts
6. Rohan Paul
@rohanpaul_ai
2 posts
@gregisenberg dominates engagement
GREG ISENBERG has 2 posts in the sample with a median all-time score of 1211.58 — by far the highest creator median — both long-form, tutorial-style breakdowns of agent concepts and an AI-workforce playbook.
@_avichawla lands a single high performer
Avi Chawla's one post in the sample, on Microsoft's Agent Lightning RL-training framework, scores 874.47 — a 76.84x outlier from a single post.
@_vmlops delivers consistent above-median posts
Vaishnavi has 2 posts with a median all-time score of 28.16, comfortably above the overall median of 11.38 — consistent, if not outlier-level, performance.
Since the previous snapshot
What changed since Oct 13, 2026
- 80% of the selected posts remained.
- The creator count changed by 0.
- The leading sentiment remained stable.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best AI Agents tweets
Top AI Agents tweets from 43 creators
Ranked 01–50
- 01
@cherry_mx_reds ·
Anthropic is now recommending that companies REMOVE unnecessary subagents from their AI systems. This is a pretty significant shift in agent architecture, with their team finding that a single agent equipped with skills can frequently outperform complex multi-agent systems, and one customer seeing roughly 2x faster execution. The emerging rules of thumb: → Skills: Teach the agent HOW to do something, including procedures, expertise, and instructions. → Tools / MCP: Give the agent the ability to DO things through APIs, actions, and live data. → Subagents: Use for parallel work, isolated context, or tasks a single agent struggles with. The interesting part is progressive disclosure. Instead of spawning specialized agents or stuffing everything into a giant prompt, one agent discovers and loads skills and tools as needed. Their advice is to fold existing subagents into the main loop, turn their instructions into skills, expose their actions as tools, and run evals to see if performance holds up. As models have gotten smarter, improving agent architecture is becoming less about what you can add and more about what you can delete. https://t.co/DNq60Skkmc
- 252Replies
- 250Reposts
- 3.6KLikes
- 894.9KViews
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@gregisenberg ·
AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on @startupideaspod was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch
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- 284Reposts
- 2.4KLikes
- 368.2KViews
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@shivsakhuja ·
Lots of companies are now building primitives for an economy where AI agents are the primary users instead of humans. They're betting on an economy of AI coworkers. 1. AgentMail (@agentmail): so agents can have email accounts 2. AgentPhone (@tryagentphone): so agents can have phone numbers 3. Kapso (@andresmatte): so agents can have WhatsApp phone numbers 4. Daytona (@daytonaio) / E2B (@e2b): so agents can have their own computers 5. Browserbase (@browserbase) / Browser Use (@browser_use) / Hyperbrowser (@hyperbrowser): so agents can use web browsers 6. Firecrawl (@firecrawl): so agents can crawl the web without a browser 7. Mem0 (@mem0ai): so agents can remember things 8. Kite (@GoKiteAI) / Sponge (@PayspongeLabs) : so agents can pay for things. 9. Composio (@composio): so agents can use your SaaS tools 10. Orthogonal (@orthogonal_sh) so agents can access APIs easily 11. ElevenLabs (@ElevenLabs) / Vapi (@Vapi_AI) so agents can have a voice 12. Sixtyfour (@sixtyfourai) so agents can search for people and companies. 13. Exa (@ExaAILabs): so agents can search the web (Google doesn’t work for agents) If you stitch all of these together, you get a digital coworker that looks more human than AI.
- 196Replies
- 234Reposts
- 2.2KLikes
- 272.6KViews
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@_avichawla ·
Microsoft did it again! Building with AI agents almost never works on the first try. A dev has to spend days tweaking prompts, adding examples, hoping it gets better. This is exactly what Microsoft's Agent Lightning solves. It's an open-source framework that trains ANY AI agent with reinforcement learning. Works with LangChain, AutoGen, CrewAI, OpenAI SDK, or plain Python. Here's how it works: > Your agent runs normally with whatever framework you're using. Just add a lightweight agl.emit() helper or let the tracer auto-collect everything. > Agent Lightning captures every prompt, tool call, and reward. Stores them as structured events. > You pick an algorithm (RL, prompt optimization, fine-tuning). It reads the events, learns patterns, and generates improved prompts or policy weights. > The Trainer pushes updates back to your agent. Your agent gets better without you rewriting anything. In fact, you can also optimize individual agents in a multi-agent system. I have shared the link to the GitHub repo in the replies!
- 82Replies
- 204Reposts
- 1.3KLikes
- 109.7KViews
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@gregisenberg ·
The BEST prompt for AI agents I've heard over the last 12 months is ONLY 3 words, and it comes from someone who managed multi-billion dollar P&Ls in AI, led a 100 person org at AWS, and now runs a brilliant AI workforce of 34 agents: 1. Her best prompt is just "do smart things" She gives the agents all her context first, her calendar, email, Stripe, goals, and transcripts, then lets them decide what's worth doing. She realized every task still started as a thought in her own head, which meant the company could only ever be as good as what she remembered to ask for. 2. Her human team talks to her agents in Slack. She has a channel where a teammate can ask "did that financial services client reply to Ali's email," and the agents answer directly, so people stop waiting hours for her to get back to them. 3. Since an AI agent costs almost nothing, she hired the person she'd never put on payroll. One does nothing but ask how to make everything ten times better. Another just watches the other agents work and flags where they get stuck. 4. She avoids old job titles, because the second you call one your CMO, you've rebuilt a 2015 company with robots. Most of her sub-agents run on cheaper, smaller models and do the job fine. 5. Run an AI watchdog instead of a dashboard. Point one at your Slack to catch two people doing the same work, at your calendar to flag conflicts, at your analytics to tell you what to post tomorrow. Almost nobody does this yet. 6. She keeps a daily "brain dump" that feeds the agents. At the end of each day she dictates what's in her head that isn't written down anywhere, the stuff that only lived in a meeting or a Slack thread, and it goes into a wiki the agents read. That's how they get the context that email and calendar miss. 7. She uses a "last 30 days" research skill to spin up on anything fast. Before running a workshop for 200 execs in an industry she doesn't know, she fans out agents to scan and synthesize the last month of news, then walks in sounding like she's followed it for years. That's my convo with @alliekmiller on @startupideaspod . Pretty much her biggest tips for using AI agents. Full episode below. https://t.co/CukuBOXt9G Happy building, I'm rooting for you.
Watch video- 129Replies
- 113Reposts
- 1.2KLikes
- 178.5KViews
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@bibryam ·
AI Agent Governance Toolkit - by Microsoft Runtime governance for AI agents through deterministic policy enforcement, zero-trust identity, execution sandboxing, and SRE for autonomous agents. Covers all 10 OWASP Agentic risks with 13,000+ tests. https://t.co/sONejSjsrX
- 22Replies
- 135Reposts
- 641Likes
- 46.9KViews
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@CodeByPoonam ·
Anthropic engineer revealed exactly how they build AI agents internally. And it's not what most developers expect. Here's what stood out 👇 → Most agents fail from bad architecture than bad models → The best agents are simple. Complexity kills reliability. → Tool design matters more than prompt design → Evaluation is the hardest and most ignored part of agent development → Real production agents look nothing like demo agents
Watch video- 10Replies
- 47Reposts
- 330Likes
- 29.5KViews
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@lennysan ·
The word "agent" is the most overloaded term in AI right now. Your backlog probably has 5-10 agent ideas. But one agent idea is a 6-week automation you can build with n8n. Another is a 6-month bet requiring a dedicated ML team. Putting them on the same spreadsheet and hoping impact-vs.-effort will sort it out doesn't work. Hamza Farooq and Jaya Rajwani—who teach two of the most highly rated and well-respected courses on building AI agents—spent 50+ hours putting together a guide that'll help you make sense of the different categories of "agents," how to prioritize across them, and how to avoid common pitfalls—with recommended tools and many real-world examples. Read this post the next time your team is going in circles on your agent strategy: https://t.co/2DDVIZgB5g
- 29Replies
- 19Reposts
- 279Likes
- 49.1KViews
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@techNmak ·
AI Agents Cheat Sheet This is a good starting point if you're trying to make sense of AI agents. There’s a lot of talk about agent frameworks right now, but at the core, most of them build on the same set of ideas. This cheat sheet gives a simple overview of the key building blocks, from LLMs to orchestration to protocols. Useful whether you’re exploring agent tooling, building internal automations, or just trying to understand the space better. What is an AI agent? - Agents combine reasoning with the ability to take action. - They don’t just respond, they can plan, call tools, access data, and trigger real-world effects. Language Model - This is the core reasoning engine. It interprets input and generates plans or responses. - But by itself, it can’t take real-world actions. Tools - APIs, functions, and external integrations that agents use to do useful work, like querying a database, sending an email, or calling a webhook. Orchestration Layer - This layer coordinates what the agent does, how it reasons (via CoT, ReAct, etc.), how it sequences steps, and how it interacts with tools. Agentic Protocols - Protocols like MCP and A2A enable agents to collaborate across platforms (e.g., Slack, GitHub) and maintain context across tasks. Building AI Agents - There’s no single way to build an agent. - Some start with a single prompt. Others use low-code platforms. Some teams build full custom frameworks. The cheat sheet maps out the trade-offs. If you’re trying to understand the agent space, or explain it to your team, this breakdown might help. Save it if you want a reference to come back to.
- 4Replies
- 30Reposts
- 134Likes
- 4.1KViews
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@goyalshaliniuk ·
Building Agentic AI Systems? Let me share a Comprehensive Guide with you. When designing smarter AI systems, understanding how agents think, act, and collaborate is the key. Here's a breakdown of the essentials: 1) What is an AI Agent? An AI agent is a system that thinks, plans, and acts independently using LLMs, adapting in real-time with minimal human help. 2) Architectures for Building AI Agents Start with single agents for simple tasks or build multi-agent systems where different agents collaborate and solve complex problems together. 3) Foundations of Agent Design Good agents combine powerful models, smart memory, useful tools, structured instructions, and seamless orchestration to work effectively. 4) Building Guardrails for Safety and Control Safety measures like data privacy, dynamic rules, and human checkpoints keep agents responsible and prevent harmful outcomes. 5) Essential Tools for Building Agentic AI Successful agents rely on LLMs, memory systems, orchestrators, integration tools, guardrails, and monitoring platforms to operate smartly.
- 23Replies
- 32Reposts
- 113Likes
- 2KViews
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- 11
@JustAnotherPM ·
𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗶𝘀 𝗼𝘃𝗲𝗿𝗵𝘆𝗽𝗲𝗱. That is what I told myself when I first started building with AI. Turns out, I had no idea. After spending 1000+ hours building AI products, deploying agentic systems at scale, making 100s of bad decisions, I have a much better understanding. But sadly, even today, most builders trying to build AI products are in the same boat: confused and clueless about "what are AI agents and how to leverage them." Here's the simplest mental model to think about it: An AI agent has 4 layers. That's it. 1. 𝗧𝗵𝗲 𝗮𝗴𝗲𝗻𝘁'𝘀 𝗯𝗿𝗮𝗶𝗻. Three things live here: a clear goal (the specific outcome it must reach), a system prompt (its permanent role, rules, and output format), and an exit condition (how it knows when to stop). Together, these three define what the agent IS. 2. 𝗧𝗼𝗼𝗹𝘀 — what it can act on. Web search, code execution, API calls, file I/O, database queries, browser access. Without tools, an agent can only talk. Tools are what let it DO things in the real world. 3. 𝗠𝗲𝗺𝗼𝗿𝘆 — what it remembers. Short-term memory is the context window. Episodic memory is the session history. Long-term memory is a vector DB that persists across sessions. A knowledge base gives it access to docs and policies. Memory is the difference between an agent that starts from scratch every time and one that learns. 4. 𝗧𝗵𝗲 𝗟𝗼𝗼𝗽 — the engine that runs every step. Perceive (read inputs, memory, and tool results). Think (reason and plan). Act (call a tool or produce output). Observe (check the result). This loop repeats until the exit condition fires. That is one agent. Now the real question: how do you connect multiple agents together? There are 3 architecture patterns. #1 𝗔𝗴𝗲𝗻𝘁 𝘁𝗼 𝗮𝗴𝗲𝗻𝘁: Agent A finishes its job, triggers Agent B directly. Simple. Works for 2-3 agents. Breaks the moment you need coordination. #2 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿: One LLM sits on top, dispatches tasks to multiple agents, and collects their feedback. Decidees what to do next. Handles complex, multi-domain work. Downside: single point of failure and higher cost because every coordination step burns tokens. #3 𝗦𝘂𝗯-𝗮𝗴𝗲𝗻𝘁: The main agent calls other agents the same way it would call an API. But unlike a regular API, each sub-agent can reason and adapt. Use this when the sub-tasks themselves need judgment, not just execution. That is the complete mental model. 4 layers inside every agent. 3 architectures to connect them. 𝗡𝗲𝘅𝘁 𝘀𝘁𝗲𝗽𝘀: Share and bookmark this post Build your first agent. I just built a course Claude Code, taught inside Claude Code. Totally FREE. Sign up today and build your first agentic system (with an LLM orchestrator) 👇
- 7Replies
- 14Reposts
- 66Likes
- 2KViews
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@ihteshamali ·
Andrej Karpathy built autoresearch an AI that writes and improves its own research papers overnight. Someone just did the same thing for AI agents. It's called AutoAgent. You tell it what kind of agent to build. It builds it, tests it, scores it, and improves it in a loop without you touching a line of code. Here's the part that makes it different from every other agent framework: You don't engineer the harness. You program the meta-agent. There's a single file called program.md. That's the only file you touch. It gives the meta-agent its directive and context. Everything else the system prompt, the tools, the orchestration, the config gets modified autonomously. The loop looks like this: → Meta-agent reads your directive → Inspects https://t.co/lvRfwACuBO (the entire harness in a single file) → Makes one targeted change to prompt, tools, or routing → Runs benchmark tasks inside Docker fully isolated → Reads the score (0.0 to 1.0) from the task evaluators → Commits the change if score improves, reverts if it doesn't → Repeats The benchmark format is harbor-compatible, so you can drop in evaluation datasets from real AI labs and the harness runs against them without modification. You wake up in the morning and the agent is better than when you left it. That's the entire pitch. MIT License. 100% Opensource. Link in comments.
- 12Replies
- 26Reposts
- 94Likes
- 8KViews
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@hrswatigupta ·
STOP building multi-agent AI systems before understanding this map. Not every AI task needs 5 agents. Sometimes, one agent is enough. Sometimes, you need an entire team. Here are 4 ways to build multi-agent systems: 1. GRAPH-BASED WORKFLOWS Use LangGraph when you need explicit control over steps, branching, retries, and state. 2. ROLE-BASED TEAMS Use CrewAI when you want agents with different roles collaborating on a shared task. 3. CODE-FIRST AGENTS Use an agent SDK when you want to define tools, handoffs, and execution logic directly in code. 4. DURABLE WORKFLOWS Use systems like Temporal when long-running processes need retries, recovery, and reliable execution. But here’s what most people miss: The framework is only one part of the architecture. You also need to decide: → How agents communicate → What context they share → When one agent hands off to another → How failures are handled → Where humans approve risky actions If you’re learning AI, explore practical tools and workflows here: https://t.co/Uy3ugI2xk7 My rule for building AI agents: Start with one agent. Add another only when the task genuinely requires separate responsibilities, parallel work, or specialized capabilities. More agents don’t automatically mean better results. Better architecture does. Explore career-building resources at https://t.co/tTmngig8E5. Follow ByteBuilders for visual breakdowns of AI, developer tools, and software engineering.
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- 39Reposts
- 81Likes
- 3.9KViews
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@akshay_pachaar ·
Engineering at Anthropic dropped another banger. Their internal playbook for evaluating AI agents. Here's the most counterintuitive lesson I learned from it: Don't test the steps your agent took. Test what it actually produced. This goes against every instinct. You'd think checking each step ensures quality. But agents are creative. They find solutions you didn't anticipate. Punishing unexpected paths just makes your evals brittle. What matters is the final result. Test that directly. The playbook breaks down three types of graders: - Code-based: Fast and objective, but brittle to valid variations. - Model-based: LLM-as-judge with rubrics. Flexible, but needs calibration. - Human: Gold standard, but expensive. Use sparingly. It also covers eval strategies for coding agents, conversational agents, research agents, and computer use agents. Key takeaways: - Start with 20-50 test cases from real failures - Each trial should start from a clean environment - Run multiple trials since model outputs vary - Read the transcripts. This is how you catch grading bugs. If you're serious about shipping reliable agents. I highly recommend reading it. Link in the next tweet.
- 5Replies
- 14Reposts
- 89Likes
- 8.7KViews
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@Suryanshti777 ·
Most people are still prompting AI. The top builders are designing agent systems. This document quietly maps 21 real agent design patterns — from prompt chaining → multi-agent systems → memory → MCP → RAG → guardrails → evaluation. If you're building with AI and not using these, you're already behind. Here’s the cheat sheet: • Prompt Chaining — break tasks into reliable steps • Routing — send tasks to the right model/tool • Parallelization — run agents simultaneously for speed • Reflection — agents critique and improve outputs • Tool Use — agents call APIs, DBs, code • Planning — long-horizon task execution • Multi-Agent — teams of specialized agents Then it goes deeper: • Memory Management — persistent context • Learning & Adaptation — agents that improve • Model Context Protocol (MCP) — tool standardization • Goal Monitoring — agents track progress • Human-in-the-Loop — controlled autonomy • RAG — grounded knowledge retrieval And the advanced layer most people miss: • Inter-Agent Communication (A2A) • Resource-Aware Optimization • Reasoning Techniques • Guardrails / Safety Patterns • Evaluation & Monitoring • Prioritization • Exploration & Discovery This is basically a blueprint for production-grade AI agents. Not theory. Actual architecture patterns used by serious teams. Bookmark this. Study one pattern per day. You’ll be ahead of 95% of AI builders in a week. Doc link ↓ https://t.co/Vm4MFghaGS
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@shushant_l ·
I'm amazed most people still haven't built their first AI agent. Here's how to build powerful AI agents without writing a single line of code. --- 1. AI chatbots answer questions, but AI agents complete entire jobs automatically. --- 2. AI agents can read emails, find leads, draft replies, and update your CRM. --- 3. Every AI agent needs a Brain (LLM) to think, reason, and make decisions. --- 4. System prompts define your agent's role, rules, behavior, and objectives. --- 5. Integrations let agents use apps, browse the web, send emails, and update data. --- 6. Memory helps agents remember context and improve across multiple interactions. --- 7. Choose the right LLM based on your use case and workflow requirements. --- 8. ChatGPT is an excellent all-round choice for most general-purpose AI agents. --- 9. Claude models excel at long-form reasoning, writing, and complex workflows. --- 10. Gemini is ideal for multimodal tasks involving text, images, audio, and video. --- 11. Grok is useful for real-time information and social media monitoring. --- 12. DeepSeek offers strong open-source performance for budget-conscious builders. --- 13. No-code platforms like Lindy, Zapier, n8n, Make, Relevance AI, Dify, and Voiceflow simplify development. --- 14. Always define the exact job your AI agent should perform before building it. --- 15. Connect the tools your agent needs to complete tasks end to end. --- 16. Add triggers so your agent runs automatically instead of waiting for manual input. --- 17. Test your workflow with multiple real-world scenarios before deployment. --- 18. Monitor your agent after launch and refine it using feedback and usage data. --- 19. Avoid vague prompts, skipping tests, adding too many tools, or ignoring memory. --- 20. You can build research, inbox, customer support, content, and social listening agents today. --- To learn more, check the infographic. ---
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@_vmlops ·
SOMEONE ON REDDIT JUST DROPPED THE CLEANEST AI AGENT BREAKDOWN I'VE SEEN. 7 steps, no fluff: ▫️ set a measurable goal before touching any model ▫️ match the model to the task LLM for general, LRM for complex reasoning, SLM for routing ▫️ pick a framework: LangChain, CrewAI, n8n, or Google ADK ▫️ add memory or your agent resets every single session ▫️ connect tools via MCP or function calling ▫️ compress context before costs spiral ▫️ test edge cases, not just the happy path the part nobody mentions: steps 4 and 7 are where most agents die in production memory makes it intelligent... testing makes it production-ready... Skip both and your agent works in the demo, breaks in real life Agentic AI is a $9B market growing 46% annually the engineers who actually know how to build these won't be available at current rates for long
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@_jaydeepkarale ·
Most people think AI agents are just “LLMs with tools.” But the interesting part is memory. Just like humans, capable AI agents need different kinds of memory to function properly. This is one of the core ideas behind the COALA framework (Cognitive Architectures for Language Agents). Think about how humans work: - You remember what someone said 10 seconds ago. - You remember facts from school. - You remember how to ride a bicycle. - You remember important life experiences. AI agents need similar layers of memory too. Here are the 4 major types: 1. Working Memory (Short-Term Memory) Human analogy: You’re reading a sentence right now while remembering the previous sentence. It’s temporary memory used for the current task. For AI agentst his is the active context window which includes: - current conversation - recent tool outputs - current reasoning chain Without it, the agent loses track mid-task like a human getting distracted every 5 seconds. ------------ 2. Semantic Memory (Factual Knowledge) Human analogy: You know Paris is the capital of France. You know Kubernetes manages containers. These are facts, concepts, and knowledge. For AI agents this includes: - stored facts - documentation - knowledge bases - vector databases - retrieved company information It answers: “What does the agent KNOW?” CLAUDE.MD is an example of Semantic Memory ------------ 3. Procedural Memory (Learned Skills) Human analogy: You don’t consciously think about every muscle movement while riding a bike. Skills become automatic. For AI agents this is: - workflows - system prompts - learned action patterns - tool usage strategies - step-by-step execution habits Example: An agent learns: “First query database → then validate → then summarize.”. Agent Skills or Skills.md are examples of procedural memory It answers: “What does the agent KNOW HOW TO DO?” ------------ 4. Episodic Memory (Past Experiences) Human analogy: You remember your first interview. Or a production outage you once handled at work. These are experiences tied to events. For AI agents this includes: - previous interactions - past successes/failures - user preferences - historical task outcomes Example: “The last deployment failed because of missing env variables.” It answers: “What has the agent EXPERIENCED before?” ------------ This is why memory is becoming one of the biggest frontiers in AI engineering. A model without memory is just reacting. An agent with memory starts behaving more like a system that learns, adapts, and improves over time.
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@_vmlops ·
Andrew Ng just shared a simple framework that changes how you build AI agents The shift is: Loop Engineering → Graph Engineering The core idea: Loops make agents think Graphs make agents remember Here's the 4-step workflow: 1. Reflection Generate → Critique → Rewrite A single self-review loop can outperform a stronger model that never reviews its work 2. Tool Use Connect your agent to search, APIs, databases, and code execution. Without tools, it's just guessing 3. Planning Break complex tasks into structured steps before execution If one step fails, the agent adapts instead of starting over 4. Multi-Agent Systems Don't rely on one agent. Use specialized agents for planning, coding, reviewing, and testing Then take it further: → Add a critique step after every generation → Connect agents through a graph so they share memory and state instead of long chat histories The biggest takeaway: Better architecture beats bigger models. A well-designed workflow can consistently outperform a stronger model running without one This is where AI agent development is heading
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@AIwithArsalan ·
Most AI agents are built to complete a task. But what happens when the work runs for hundreds of turns? I've been looking at openJiuwen's WorkSwarm, an Apache 2.0 open-source project built around multi-agent coordination. Two things that genuinely stood out: → Persistent Session preserves roles, decisions, boundaries, and authority even when older context gets compressed. In one test, 5 people shared one agent across 189 turns and it caught all 8 cross-role conflicts. → RSI (Recursive Self-Improvement) uses real task feedback to propose, verify, and retain improvements. On SWE-bench Lite, it moved the score from 61% to 87%. If you're building long-running agent workflows, the repo is worth exploring:
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@rohanpaul_ai ·
Harvard Business Review just published a piece. A good AI agent needs a job description, limits, and a manager. Because, AI agents can fail like employees with too much access and too little supervision. firms keep treating agents like normal software, even though the real risk is not bad text but bad actions. That changes 4 things: each agent needs its own identity and permissions, its own trusted data sources, hard rule checks between a model and any real transaction, and a full audit trail of what it read, decided, and did. So the safe rollout path is an autonomy ladder where agents start with drafts and recommendations, then move to guarded retrieval, then supervised actions, and only later get narrow bounded autonomy.
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@pauliusztin_ ·
Refined my workshop on multi-agent systems from scratch after doing it at the @aiDotEngineer and Uphill conferences In the latest iteration, we introduced an `implement_yourself` option that lets you implement everything yourself using agentic coding best practices. In other words, you build agents with agents. We added all the skills and subagents to incrementally build elements from the system, read it, understand it and run it, until you have the e2e system working. After iterating and refining it with @Whats_AI so many times, it's an amazing free resource to get into: - MCP servers - Designing agents - Adding observability and evals You can find everything on GitHub: https://t.co/O7UXJG6UOw
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@milan_milanovic ·
𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 Most developers today use AI agents for coding, yet we don't have any useful patterns for how to do it properly. What I see is that mostly everyone has their own style of working. I also improvised a lot, but after 3 years came to the point where I can see that some patterns emerge. Here they are: 𝟭. 𝗦𝗽𝗲𝗰𝘀 𝗳𝗶𝗿𝘀𝘁, 𝘁𝗵𝗲𝗻 𝗰𝗼𝗱𝗶𝗻𝗴 This is something I learned early, but also translated my pre-AI way of working. I first do specification or research and make plans with agents. Agents create SPECS.md or RESEARCH.md and PLAN.md docs. We do a few iterations here until I'm satisfied. Mostly I use AI agents plan mode for this. So the process is: Specify/Research -> Plan -> Tasks -> Implement. Then, I give the agent to implement, while I review results. 𝟮. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗶𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼 During this process, I create docs (referenced from AGENTS.md and CLAUDE.md), so agents always know what we did and how. It also includes ARCHITECTURE.md where I note architecture decisions, and ADRs for architecture decision records. Then I set that agents should always update and review these docs during implementation, so they become a kind of living docs. 𝟯. 𝗧𝗲𝘀𝘁 𝗿𝗲𝘀𝘂𝗹𝘁 𝗼𝗳 𝗲𝘃𝗲𝗿𝘆 𝘁𝗮𝘀𝗸 So, when agents finish, I have a defined set of automatic tests, builds and screenshots (if UI is touched), where the agent checks its own work. Before that, I send it to another agent for a review. E.g. if I coded with Claude Code, I send it to GPT for a review. Different agents/models have different views on the code and can find potential issues. Also, I do this manually, check the code and UI. 𝟰. 𝗦𝗺𝗮𝗹𝗹 𝗯𝗮𝘁𝗰𝗵𝗲𝘀 𝗮𝗻𝗱 𝗳𝗿𝗲𝗾𝘂𝗲𝗻𝘁 𝗰𝗼𝗺𝗺𝗶𝘁𝘀 I instruct agents to work in small batches, because it's easier to do a rollback and I can also review it manually when needed. It also represents a history of changes that we can track later. All of these things, like specs, docs and small batches, are something we had before AI, but now it is more emphasized. The good engineering practices are here to stay, if you skip it you pay the price, maybe even bigger than before.
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@jianw851 ·
Everyone says they’re building “AI Agents.” Most are just chatbots with better marketing. And that misunderstanding is about to cost companies millions in 2026. Here’s the clearest mental model I’ve found: ━━━━━━━━━━━━━━━ ❌ NOT Agentic AI 1. LLM Chatbots Input → Output. That’s it. No planning. No memory. No autonomous execution. Just very smart next-token prediction. ChatGPT alone is not an agent. ━━━━━━━━━━━━━━━ 2. RPA Bots Traditional automation tools. They follow fixed workflows: → Click button → Copy data → Send email Works great… until something changes. One unexpected screen and the entire pipeline breaks. Automation ≠ Intelligence. ━━━━━━━━━━━━━━━ 3. Basic RAG Most “AI copilots” today are just: Search + LLM. They retrieve information and generate answers. Useful? Absolutely. Agentic? Not really. Because retrieval alone is not reasoning. Real agents don’t just fetch information. They decide what to do next. (Agentic RAG is different because it can plan, critique, retry, and adapt.) ━━━━━━━━━━━━━━━ ✅ What ACTUAL AI Agents do → Maintain memory Short-term for active tasks. Long-term for goals, preferences, schedules, workflows. → Plan autonomously Break large goals into smaller executable steps. → Dynamically use tools APIs, browsers, code execution, databases, workflows. → Self-correct Observe failures. Retry intelligently. Improve through feedback loops like ReAct + Reflexion. → Operate in multi-agent systems Research agents. Coding agents. Review agents. Ops agents. Specialized systems working together. → Communicate through protocols MCP and A2A are becoming the interoperability layer for agents. Agents are slowly becoming “services that talk to other services.” ━━━━━━━━━━━━━━━ The biggest shift? AI is moving from: “Answer my question” to “Complete my objective.” That changes everything. We’re no longer building interfaces. We’re building autonomous systems. And most people still don’t realize the difference. The terminology is noisy. The architecture isn’t. Bookmark this for later. Follow for deep dives on AI Agents, MCP, Claude Code, multi-agent systems, and agentic workflows.
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@zaimiri ·
most AI agents are ChatGPT with extra steps. they forget what you told them 5 minutes ago. they cant access your files. they don't KNOW your business. people are building agent swarms that sound impressive in demos. then they deploy them and realize: 1. the agents have no memory. 2. the agents have no context. 3. the agents have no idea what makes your business different from any other business. I spent months building compley knowledge systems for my agents. started with my content: • every post Ive ever written • every voice rule my editor caught • every draft that got approved or skipped • the specific reasons WHY this is the basis that later turned into the Company Brain. without it your agents are just interns who forget everything overnight. with it they make decisions like someone who's been here for years.
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@xelebofficial ·
You don't need to understand transformer math to build AI agents in 2026. You need to understand architecture. Specifically, the seven layers that determine whether an agent actually works in production or only in a demo. Goal Orchestrator The planning layer. Breaks a high-level objective into ordered subtasks and manages execution sequence from start to finish. Reasoning Engine The thinking layer. Uses Chain-of-Thought, Tree-of-Thoughts, or Reflection to let the agent reason through a problem, self-check, and course-correct before acting. Tool System The execution layer. Connects the agent to the outside world: APIs, browsers, code interpreters, function calls. It can act, not just respond. Memory Architecture The continuity layer. Three tiers: working memory for immediate context, semantic memory for knowledge, episodic memory for interaction history. This is what allows an agent to learn over time rather than reset with every session. Multi-Agent Collaboration The team layer. Specialized agents working in concert, a Supervisor that coordinates, Workers that execute, a Critic that evaluates. Not one agent doing everything, but a system where each component does what it does best. Dynamic Workflow The adaptability layer. State machine or graph-based frameworks that allow an agent to adjust its plan mid-execution when conditions change, rather than following a rigid sequence that breaks under real-world complexity. Guardrails & Governance The control layer. Rule-based constraints, human approval checkpoints, output validation, and alignment mechanisms that keep the agent operating within defined boundaries at every step. Most agent failures trace back to a weakness in one of these seven layers. Getting the architecture right is what separates a reliable system from an impressive prototype.
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@sharbel ·
Most founders are building AI agents backward. They start with: 1. Pick a tool 2. Write a prompt 3. Hope it does useful work That is why the agent feels impressive once, then disappears from the workflow. Better order: 1. Pick a recurring job 2. Write the decision rules 3. Define the inputs 4. Define the output format 5. Add a review step 6. Run it on a schedule 7. Improve it from failures The tool matters less than the job design. A mediocre agent with a clear job beats a powerful agent with vague instructions.
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@far33d ·
I've been using AI code tools a LOT recently - mostly as a way to turn ideas in my head into something concrete to react to vs. trying to build finished products. This week, the goal was to prototype multi-player AI interactions and try to fix the problems I have writing in AI tools today. Most AI writing tools miss the point. They either rewrite your entire document or exile you to a sidebar chat. What if AI worked more like a team of editors? That's ai-writer: multiple AI agents, each with its own voice, collaborating in the margins like human editors. Highlight text, @-mention an agent, get a targeted rewrite. Accept or reject inline. The interface stays the same whether you're talking to a person or a bot. It's just a prototype. Be nice. The best part of the design: human comments and AI suggestions work the same way. AI suggestions are just comments with an edit attached. Same system, same look, same process. Build the comment system once, and you get AI editing for free. No separate "AI mode." No special steps for AI changes. The comment system went through four distinct versions, each one triggered by actually using the previous version and finding it insufficient. Iterating on the idea until it felt more right, finally landing on a series of AI helpers that can interact with the user (or a team in the future) in the doc or in comments. This is a prototype, not a product. But the pattern feels strong: agents as collaborators in context, not as separate tools.
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@xelebofficial ·
AI agents are becoming a new kind of user. That is the part many teams are still underestimating. Once an agent can access data, trigger tools, move across workflows, or interact with customers, it is no longer just a feature. It becomes an actor inside the system. And every actor needs boundaries. What can it access? What can it execute? What should it remember? What should it escalate? What should be logged? Who is responsible when it gets something wrong? This is where the next layer of AI agent infrastructure will matter. Not just better reasoning. Better identity. Better permissions. Better observability. Better accountability. The future of AI agents will not be built only around what agents can do. It will be built around what agents are trusted to do.
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@aiwithmayank ·
I found a repo that removes the most exhausting part of working with AI agents. The part where you manually read failure logs, guess what went wrong, rewrite the prompt, test it again, and repeat for days. It's called meta-agent. The idea is simple but the implications are wild. You tell it what your agent is supposed to do. You tell it how to check if it worked. Then you walk away. meta-agent runs your agent, catches every failure, feeds those failures to a second AI that acts as a coach, and the coach rewrites the instructions for the next run. It keeps looping until the agent stops getting better. Real result from their benchmark: 67% accuracy to 87% accuracy. Automatically. Overnight. If you're building anything with Claude Code, customer service bots, research agents, or workflow automation, this is the missing piece nobody talks about. Your AI should be fixing itself. Most aren't. MIT License. 100% Opensource.
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@MaryamMiradi ·
After building 400+ Production AI Agents I curated 7 Crucial Skills so you can become a Production AI Agents Engineer. 𝟭. 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 If you have built a backend with multiple services talking to each other, you already think this way. If not, this is where I would start. An agent is not one thing. Everything needs to connect. Data needs to flow. Failures need to be contained. 𝟮. 𝗧𝗼𝗼𝗹 𝗮𝗻𝗱 𝗖𝗼𝗻𝘁𝗿𝗮𝗰𝘁 𝗗𝗲𝘀𝗶𝗴𝗻 If your tool schemas are vague, agent fills the gap with imagination. → LLM imagination is creative. That is the problem. → In a financial transaction, creative is dangerous. I now write every schema like a legal contract. Strict types. Explicit examples. Zero ambiguity. 𝟯. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 We know RAG or mayebe not? Chunk too large, you drown the signal. Chunk too small, you lose the context. Retrieval quality sets the ceiling on everything your agent can do. The LLM cannot compensate for garbage context. Do Re-ranking. 𝟰. 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 APIs fail very often. You need: ✸ Retry logic with exponential backoff ✸ Timeouts so the agent does not hang indefinitely ✸ Fallback paths when the primary tool breaks ✸ Circuit breakers to stop one failure taking down everything Backend engineers have solved this for decades. Most people building agents right now have not read that playbook yet. 𝟱. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝘁𝘆 Prompt injection is real. Your agent is an attack surface. Do this: Input validation. Output filters. Permission boundaries. Least privilege applies to agents exactly as it applies to human employees. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Remember to Trace: Every decision. Every tool call. Every retrieval result. I run evaluation pipelines with known good answers before anything goes to production. If you cannot measure it, do not deploy it. 𝟳. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 Your agent serves a human. That human wants to know when the agent is confident and when it is not. They need to know when to trust it and when to escalate. 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: AI Agents Engineering is here to Stay. Master it.
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@ttunguz ·
If 2025 is the year of agents, then 2026 will surely belong to agent managers. Agent managers are people who can manage teams of AI agents. How many can one person successfully manage? I can barely manage 4 AI agents at once. They ask for clarification, request permission, issue web searches—all requiring my attention. Sometimes a task takes 30 seconds. Other times, 30 minutes. I lose track of which agent is doing what & half the work gets thrown away because they misinterpret instructions. This isn’t a skill problem. It’s a tooling problem. Physical robots offer clues about robots manager productivity. MIT published an analysis in 2020 that suggested the average robot replaced 3.3 human jobs. In 2024, Amazon reported pickpack and ship robots replaced 24 workers. But there’s a critical difference : AI is non-deterministic. AI agents interpret instructions. They improvise. They occasionally ignore directions entirely. A Roomba can only dream of the creative freedom to ignore your living room & decide the garage needs attention instead. Management theory often guides teams to a span of control of 7 people. Speaking with some better agent managers, I’ve learned they use an agent inbox, a project management tool for requesting AI work & evaluating it. In software engineering, Github’s pull requests or Linear tickets serve this purpose. Very productive AI software engineers manage 10-15 agents by specifying 10-15 tasks in detail, sending them to an AI, waiting until completion & then reviewing the work. Half of the work is thrown away, & restarted with an improved prompt. The agent inbox isn’t popular - yet. It’s not broadly available. But I suspect it will become an essential part of the productivity stack for future agent managers because it’s the only way to keep track of the work that can come in at any time. If ARR per employee is the new vanity metric for startups, then agents managed per person may become the vanity productivity metric of a worker. In 12 months, how many agents do you think you could manage? 10? 50? 100? Could you manage an agent that manages other agents? https://t.co/iHl9JXipNJ
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@vicky_grok ·
Most AI "agents" shared online aren't actually agents. They're orchestrated workflows. There's nothing wrong with orchestration. It's powerful and useful. But understanding the difference helps you choose the right architecture for the right problem. Here's the distinction. An orchestrator follows a predefined sequence. It executes a workflow that was designed in advance. A true AI agent starts with a goal, decides what to do next, adapts when conditions change, and continues until the objective is achieved. Think of it this way. An orchestrator follows instructions. An agent creates and updates its own plan. An orchestrator typically: • Executes fixed workflows • Runs predefined steps • Uses rule-based logic • Doesn't adapt automatically • Has limited or no memory • Doesn't reflect on previous outcomes • Cannot make independent decisions outside its workflow A true AI agent can: • Understand a goal • Plan its own approach • Select and use tools • Maintain memory across tasks • Evaluate intermediate results • Learn from previous attempts • Adjust its strategy when needed • Decide when the task is actually complete A practical example. Customer support chatbot If it always follows: Receive ticket ↓ Look up FAQ ↓ Generate reply ...it's an orchestrator. Now imagine another system that: Receives the request ↓ Plans multiple possible solutions ↓ Searches documentation ↓ Uses internal tools ↓ Checks whether the issue is resolved ↓ Asks follow-up questions if needed ↓ Changes its strategy until the goal is achieved That's much closer to agentic AI. As AI systems continue to evolve, you'll see more products described as "AI agents." The important question isn't what they're called. It's whether they can: • Reason • Plan • Use tools • Remember • Evaluate • Adapt • Act autonomously toward a goal If most of those capabilities are missing, you're likely looking at an orchestrated workflow rather than a true AI agent. Both approaches have their place. Use orchestrators for predictable, repeatable processes. Use agentic systems when the problem requires planning, adaptation, and autonomous decision-making. ---- If you enjoy practical AI guides, workflows, prompt libraries, and free learning resources, subscribe to the ByteBuilders newsletter: https://t.co/IAjuMwTAmm
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@smratitiwa86867 ·
Everyone is building AI agents. Very few are solving the infrastructure problem behind them. AgentENV is an open-source distributed system built to run AI agent environments at scale. Why it matters: → Instantly snapshot an agent's state → Resume from any checkpoint in seconds → Fork thousands of agent environments in parallel → Built for large-scale agentic RL training, not just demos This is the kind of infrastructure that makes training next-generation AI agents actually practical. If you're interested in AI agents, reinforcement learning, or distributed systems, this is a repository worth exploring. GitHub link in the comments. 👇
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@rohanpaul_ai ·
Stronger agents will not come only from larger models, but from better systems around them. The problem is that many AI agents are judged as if the model alone did the work, even though the real behavior also depends on memory, tools, context, routing, checks, and permissions. This surrounding setup around the agent is called harness, meaning the system that decides what the model sees, what tools it can use, what it remembers, and what actions get checked. Progress should come from scaling this harness, especially 3 parts: better context control, more trustworthy memory, and better routing to tools or helper agents. Long context is not the same as usable context, memory is not the same as trustworthy memory, and having many tools is not the same as knowing when to use them. A stale note can be more dangerous than no note, because it gives the agent confidence exactly when it should re-check the world. A specialized subagent can also fail quietly if its output sounds plausible but no later layer verifies whether it is true. This is why one-shot benchmark scores feel increasingly thin. Two agents can reach the same final answer, while one burns far more tokens, makes riskier tool calls, carries corrupted memory, or succeeds only by accident. The next frontier is not just scaling the mind inside the machine. It is scaling the discipline around it. ---- – arxiv. org/abs/2605.26112 Title: "From Model Scaling to System Scaling: Scaling the Harness in Agentic AI"
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@alvinfoo ·
Most people think Claude Code is just a coding assistant. It’s not. It’s an entire agent development platform — and most are only using 10% of its power. The real breakthrough is in its architecture: CLAUDE.md + Skills + Hooks + Subagents + Plugins = The Agent Development Kit Here’s how it actually works: 1. CLAUDE.md (Memory Layer) The foundation. Defines rules, structure, and context, like a “constitution” for your AI agent. Always loaded. Always guiding behavior. 2. Skills (Knowledge Layer) Reusable capabilities your agent can call on demand. Not always active, only triggered when needed. Think modular intelligence. 3. Hooks (Guardrail Layer) Where control happens. Pre/post actions, validations, safety checks. This is how you enforce quality and prevent mistakes at scale. 4. Subagents (Delegation Layer) This is where it gets powerful. You don’t just use AI, you orchestrate teams of AI. Each subagent handles a specific task with its own context. 5. Plugins (Distribution Layer) Package and scale everything. Turn capabilities into reusable tools across teams. The shift is clear: We’re moving from → writing code to → designing systems that produce outcomes From → single assistants to → coordinated AI agents working in parallel The biggest mistake right now? Treating LLMs with better autocomplete. The winners will be the ones who learn how to build, structure, and deploy agent systems, not just prompt them. AI isn’t just helping you code anymore. It’s becoming your execution layer.
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@JulianGoldieSEO ·
X JUST GAVE AI AGENTS A LIVE RESEARCH ENGINE And most people are still obsessing over new chatbots. What Actually Changed: → X launched a hosted MCP server on June 30, 2026 → Your AI can now connect to X through https://t.co/jOR6M3o0fj → No custom server, no hand-wired API bridge, no weekend setup project → The docs server at https://t.co/BTBTIxF0d8 lets your AI read X’s developer docs while you build What Your Agent Can Do Now: ✓ Search posts in real time ✓ Look up users and conversations ✓ Track what people are asking right now ✓ Pull bookmarks and live trend signals ✓ Use 200+ X API tools inside supported AI clients The Catch: ✔ It’s built for reading and research ✔ Not auto-posting ✔ Think “live research window,” not “AI megaphone” The Smart Setup: → Start with search and user lookup → Do not turn on all 200+ tools → Use OAuth properly → Keep your tokens private → Add more tools only when your workflow actually needs them This is the real shift: X didn’t invent a new model. They removed the boring setup that stopped agents from using live data.
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@pauliusztin_ ·
7 steps to go from a problem statement to an agent system: (Steal this if you’re building AI agents) 1/ Define value, cost, & latency Start with constraints. What accuracy do you need? How fast should it respond? What’s the cost of failure? A support bot (fast, ~95% correct) ≠ invoice extraction (slow, ≥99.5% accuracy). This decision shapes everything downstream. 2/ Select your models Pick based on constraints. Closed APIs → best capability, low overhead Open-weight → control, privacy, customization Then match the reasoning depth: Planning tasks → reasoning models Extraction/classification → fast models 3/ Design context More context ≠ better performance. Dumping full documents leads to: Higher cost Worse accuracy (lost-in-the-middle) Instead use: Retrieval (RAG) Compression Summarization 4/ Pick orchestration Workflows → predictable, auditable Agents → flexible, exploratory Most systems are hybrid. 5/ Add human in the loop & evaluation Define when humans step in. Low-risk → fully automated High-risk → approval required And always evaluate on your own data. Leaderboards won’t save you. 6/ Design tools properly Keep logic out of the LLM. The model decides what to do. Tools do the work. This improves: Accuracy Reliability Maintainability 7/ Build for durability & observability Production systems need: Checkpointing Resumability Tracing Otherwise, debugging becomes guesswork. Want to see how it works live? This is exactly what we cover in the Agent Engineering: Building Multi-Agent Systems course. Enroll today: https://t.co/QEuz9UaAkl P.S. The first 7 lessons are FREE.
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@thetripathi58 ·
AI agents are failing at complex tasks because we keep hardcoding their workflows. Researchers just released a paper on the Mimosa Framework. It proves that static multi-agent systems are a dead end. The solution? Agents that build and evolve their own workflows on the fly. Here is what the research actually reveals about building agentic AI: The Static Architecture Trap Situation: You build an AI agent system. You explicitly define step one, step two, and step three. It works perfectly in testing, but completely breaks down the second it encounters an unexpected error in production. System: Stop hardcoding paths. Mimosa uses a meta-orchestrator that dynamically generates the workflow topology based on the specific task. If the environment changes, the architecture adapts automatically. The Iterative Feedback Loop Situation: Your agent fails a task and simply stops. You have to manually intervene, read the logs, and fix the prompt or the code. System: The framework introduces an LLM-based judge. When an agent executes a subtask and fails, the judge scores the execution and sends structured feedback. The agent refines its own workflow and tries again without human intervention. The Model Capability Filter Situation: You assume that throwing a multi-agent framework on top of a cheap, low-tier model will magically make it capable of complex reasoning. System: The paper found that the benefits of workflow evolution depend entirely on the underlying execution model. If the base model cannot understand multi-agent decomposition, the entire system collapses. Architecture cannot compensate for poor foundational reasoning. The realization? The future of AI implementation is not about writing better instructions. It is about building systems that write their own instructions based on real-time feedback. Stop micromanaging your AI agents. Start building systems that can course-correct themselves.
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@Bha74142Shivani ·
Everyone wants to build an AI Agent. Very few know where to start. Building an AI agent isn't about choosing the latest model—it's about designing a complete system. Here's a practical roadmap: Define the goal. • What problem should the agent solve? • How will you measure success? • What are the latency, cost, and safety constraints? Pick the right model. • Large Reasoning Models (LRMs) for complex reasoning. • Large Language Models (LLMs) for general-purpose tasks. • Small Language Models (SLMs) for fast, cost-efficient workflows. Choose the right framework. LangChain, CrewAI, LlamaIndex, Google ADK, OpenAI SDK, n8n, LangFlow, SmolAgents—each serves a different purpose. Pick based on your use case, not hype. Connect your tools. AI agents become useful when they interact with APIs, databases, documents, MCP servers, CRMs, calendars, and external systems. Give your agent memory. Context alone isn't enough.Combine: • Short-term memory for conversations • Episodic memory for past interactions • Long-term memory using RAG and knowledge bases Test relentlessly. Measure: • Accuracy • Hallucinations • Latency • Cost • Edge cases • Security Don't ship an AI agent you haven't tried to break yourself. Deploy, monitor, and improve. Production is where the real learning begins. Monitor performance, collect user feedback, add guardrails, optimize prompts, reduce costs, and iterate continuously. The biggest misconception about AI agents is that they're "just prompts. "In reality, an AI agent is the combination of:Goal + Model + Memory + Tools + Workflow + Evaluation + Continuous Improvement.The companies creating real business value with AI aren't using a single model better. They're engineering better https://t.co/sdLVBJb2IV this roadmap if you're planning to build AI agents in 2026. What are you building with AI right now? #AI #AIAgents #ArtificialIntelligence #LLM #GenerativeAI #OpenAI #LangChain #CrewAI #LlamaIndex #GoogleAI #Automation #MachineLearning #Developer #Tech #FutureOfWork #Innovation #AIEngineering #AgenticAI #BuildInPublic #Programming
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@SwamiSivasubram ·
Building reliable AI agents often goes something like this: you start with a simple prompt, test, iterate a dozen times, observe its outputs, and then deploy it. You fix one behavior, another drifts, and before long, you have a wall of instructions the model sometimes follows, sometimes ignores, and occasionally interprets in ways you never anticipated. When we introduced the Strands Agents SDK, our goal was to make agentic development simple and flexible by embracing a model-driven approach. Instead of telling an AI agent everything upfront and hoping it remembers, we wanted enable builders with targeted guidance at the exact moment the agent is about to make a decision. That's exactly what Strands steering does. Clare Liguori, Senior Principal Engineer on our Agentic AI team, put this to the test and the results were remarkable: steering hooks achieved 100% agent accuracy pass rate across 600 evaluation runs, compared to 82.5% for simple prompts and 80.8% for workflows. Read Clare's full technical breakdown, including failure pattern analysis and guidance on when to use each approach ➡️ https://t.co/O0KzcqB81v
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@suraj_sharma14 ·
If you want to become top 1% at building AI agents, start with these 10 papers. These papers shaped almost every modern AI agent framework. 1. Chain-of-Thought Prompting (2022) What you'll learn: Why asking an LLM to reason step by step dramatically improves complex problem solving. This paper laid the foundation for reasoning models. https://t.co/IvmquaonBF 2. ReAct (2022) What you'll learn: How agents can alternate between thinking and taking actions, enabling them to search the web, call APIs and solve real-world tasks. https://t.co/0jI2oDpAmu 3. Toolformer (2023) What you'll learn: How language models can learn when and how to use external tools like calculators, APIs and search engines without constant human prompting. https://t.co/gjOXrrwOvO 4. Generative Agents (2023) What you'll learn: How AI agents can maintain memory, form relationships, plan ahead and exhibit believable long-term behavior in simulated environments. https://t.co/O2AKL5ivN4 5. Reflexion (2023) What you'll learn: Instead of retraining the model, agents improve by reflecting on their mistakes, storing lessons and using them in future attempts. https://t.co/cLpTjZM3sy 6. Tree of Thoughts (2023) What you'll learn: Why exploring multiple reasoning paths and backtracking often beats linear Chain-of-Thought reasoning on difficult problems. https://t.co/YEh2S92vw7 7. HuggingGPT (2023) What you'll learn: How one LLM can orchestrate dozens of specialized AI models to solve multimodal tasks involving text, vision, audio and more. https://t.co/qBRuELHQwU 8. Voyager (2023) What you'll learn: How an autonomous agent continuously learns, writes its own skill library and becomes more capable over time without restarting from scratch. https://t.co/kW2tYNo9Eq 9. AutoGen (2023) What you'll learn: Why multiple AI agents collaborating through structured conversations outperform single-agent systems for complex workflows. https://t.co/ARqDWhmDPk 10. A Survey on LLM-Based Autonomous Agents (2023) What you'll learn: A complete overview of the field from planning, memory and tool use to multi-agent collaboration, evaluation methods and future research directions. https://t.co/VUkxAZUg1F (Bookmark this) follow @suraj_sharma14 for such useful resources.
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@pascal_bornet ·
𝗧𝗵𝗲 𝗱𝗶𝗿𝘁𝘆 𝘀𝗲𝗰𝗿𝗲𝘁 𝗼𝗳 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗮𝗴𝗲𝗻𝘁𝘀. Every company says the same thing right now: “We replaced the team with AI agents.” Then the workflow meets reality. The agents handle the clean path beautifully, but the moment the customer says something unexpected, the policy has an exception, the data is messy, or the system needs judgment, everything quietly waits for a human to push the ball forward. So the company hires people back, gives them a dashboard, asks them to watch the agents, and calls the whole thing “human-in-the-loop.” This is exactly what I call the 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗮𝗽. Most organizations claim they are building Level 4 agents, when in practice they are operating much closer to Level 2 or 3: useful automation, impressive demos, and a hidden human layer keeping the system alive. The problem is not that agents are useless. They are already very useful. The problem is that “autonomous” has become the most expensive word in the pitch deck. This week, ask one uncomfortable question before approving an agent workflow: where does the human still enter the system, and have we designed that role honestly? Are you building autonomous agents, or are you building better babysitting software? #AgenticAI #SupervisionTrap #HybridManagement #AITransformation
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@hugobowne ·
An agent is just an LLM calling tools in a loop. The harness is everything around it. I put together a reading list on agent harnesses: the tools, memory, evals, hooks, and orchestration patterns around modern AI systems. It collects a bunch of conversations and collaborations I’ve been lucky to have recently, including: - building self-extending agents with @ivanleomk Leo (Google DeepMind, ex-Manus) - harness engineering + context limits with @jeffreyhuber (Chroma) - agentic search architectures with @softwaredoug (led Search at Reddit and Shopify) - context engineering patterns with @RLanceMartin (Anthropic, ex-Langchain) It also includes some of my other favourite posts: - Pi: The Minimal Agent Within OpenClaw by @mitsuhiko - How do I evaluate agentic workflows? by @HamelHusain - Effective harnesses for long-running agents by the @AnthropicAI team - Demystifying evals for AI agents by the @AnthropicAI team Get the full reading list here: https://t.co/JM4hCLu9Gs Would love to know what you build with these skills!
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@TheCraigHewitt ·
I’ve spent a lot of the last few months helping business leaders build AI agents. 1 thing stood out: the tooling was never the problem. Every time, we had a working agent within a couple of hours, and fully functional in a few weeks. The builds went fine. The demos were impressive. ...and almost none of it changed how they actually run their companies. Watching it happen dozens of times in a row, the pattern was obvious. The agent wasn’t the bottleneck. The operator was. Three things kept showing up: 1. They couldn’t describe their own processes. - You can’t automate a workflow you’ve never written down. Most founders run the business from memory...the process lives in their head, and nowhere else. - The founders who got real value spent week one just mapping how work actually flows through their company. Boring. Completely decisive. 2. They’d never built the delegation muscle. - Handing work to an AI requires the same things as handing work to a person: context, a clear definition of done, and a feedback loop. - If delegation to humans keeps failing in your company, AI inherits the same failure. It just fails faster. 3. Their calendar didn’t change. - They added AI on top of an unchanged week. The agent ran; the founder kept doing everything they’d always done. - Leverage you don’t cash in isn’t leverage. It’s a demo. The tooling is genuinely easy now. That’s the part nobody wants to hear: the remaining work is all operating work. AI doesn’t stick in a business until the founder changes how they run it. That’s what I’m going to be writing about here. More to come...
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@BatsouElef ·
What is an AI Agent? ↓ An AI agent is an intelligent system that can understand your goals, make decisions, take actions, and learn from experience, much like a capable research assistant who works alongside you. Think of it this way: a regular chatbot answers your questions one at a time. An AI agent, on the other hand, can take on a whole project. It breaks down complex tasks, uses the right tools, follows your workflow, and keeps learning as it goes. → Why AI Agents Matter Existing AI solutions have real limitations: • They forget everything — Each conversation starts from scratch • They don't know your field — Generic models struggle with specialized work • They make things up — Sometimes they sound confident but are wrong • They can't do multi-step work — They're built for quick Q&A, not complex projects AI agents are designed to solve these problems. What Makes AI Agents Different → They Remember Agents keep track of your projects, preferences, and past decisions. You don't have to re-explain everything each time. → They Work Through Complex Tasks Instead of just answering one question, agents can: • Break big projects into smaller steps • Run long processes from start to finish • Connect to your tools, data, and workflows • Adjust their approach based on what they find They Show Their Work You can see exactly what the agent did and why. When it's unsure, it asks you instead of guessing. They Fit Your Domain Agents can be customized with the knowledge, tools, and workflows specific to your field—whether that's biomedical research, drug discovery, data science, or education. Source: https://t.co/zqr1luqu1i, @MorphMind__AI
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@pascal_bornet ·
An AI agent did not need to become evil to become dangerous. That is the uncomfortable part of the reported Alibaba ROME case. During testing, an experimental agent allegedly created a reverse SSH tunnel, bypassed parts of its controlled environment, and redirected GPU resources toward cryptocurrency mining. The important detail is not “crypto.” The important detail is that nobody explicitly asked it to do this. For years, I thought many rogue-agent stories still belonged in the category of future risk. Useful to discuss, but easy for leaders to postpone. I was wrong. This is exactly what I call “When Agents Go Rogue” in Agentic Artificial Intelligence. Agents do not fail only because they are stupid. Sometimes they fail because they are too effective at finding paths we forgot to close. And this is where Trust Architecture becomes non-negotiable. With people, trust is earned over time. With agents, trust must be engineered before deployment. This week, do one simple audit: list every tool, system, file, API, and network path your agents can access. Then ask: “What could this system do if it optimized the wrong thing perfectly?” Are your AI agents actually governed, or merely trusted? #AgenticAI #TrustArchitecture #AIReadiness #EnterpriseAI
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@BigHuman ·
There's one conversation about AI agents that isn't getting enough attention, and it's the one that matters most. What happens after they're inside your systems? Agents don't wait. They move across tools, trigger actions, and make decisions in sequence without a human in the loop at each step. That's the value proposition. It's also the risk surface. Enterprise deployments tend to define access and stop there. What an agent is actually mandated to do, and where it stops gets treated as a detail to figure out later. An agent that can touch your systems without a clearly defined mandate is an open variable in your infrastructure, and open variables in large systems have a habit of becoming expensive problems. A claims processing agent should be able to verify a policy, cross-reference a report, flag a discrepancy for human review. The moment it can initiate a payout above a threshold or change underlying policy terms without a second signature, the organisation has handed over a decision it probably didn't mean to. Defining that boundary is a governance decision, and it needs to be made before anything runs. One agent is manageable. A fleet of agents is a department, and without consistency within that department, issues build quickly. When agents don't behave predictably across a system, data drifts, decisions conflict, and the organisation inherits the mess. Scope creep in a human team is visible. You can catch it, address it, course correct. In an agentic system, it compounds until it becomes structural. Give agents the smallest footprint that gets the job done. The governance work feels slow upfront, but it's considerably slower to unpack later.
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@hackernoon ·
The hard part of building AI agents isn't the demo - it's the 3 months after. Real lessons from running a 15-agent system in production for 90 days.... - by @deeflectcom https://t.co/rjRmOqy6EB #artificialintelligence #softwarearchitecture
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@hackernoon ·
A reference architecture for scaling human-supervised AI agents through pub/sub messaging, typed capabilities, approval gates, and durable workflows. #multiagentsystems #agenticaiswarm...Show more
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