How to Build Agent Loops for Solo Founders

Spent way too much time building agents that never worked? Been there! After reading this LangChain guide and reflecting on my own messy journey, here’s the 6-step framework that actually works:- 1. Define with examples (not dreams) Stop saying “it’ll handle everything!” Start with 5-10 concrete examples. If you can’t teach it to a smart intern, your scope is probably broken. 2. Write the manual first Before touching any code, write out step-by-step instructions for how a human would do this task. Boring? Yes. Essential? Absolutely. 3. Build MVP with just prompts Focus on ONE core reasoning task. Get that prompt working with hand-fed data before you get fancy. Most agents fail here because we skip the fundamentals. 4. Connect the pipes Now connect real data sources. Gmail API, calendar, whatever. Start simple - resist the urge to build something that calls 47 different APIs. 5. Test like your job depends on it Run your original examples through the system. Set up automated testing. Use tools like LangSmith to see what’s actually happening under the hood. 6. Deploy and learn Ship it, watch how people actually use it (spoiler: differently than you expected), then iterate. Launch is the beginning, not the end. Real talk:- I’ve broken every one of these rules and paid for it. The “smart intern” test alone would’ve saved me months of chasing impossible dreams. What’s been your biggest agent-building experience? #AI #Agents #LLM #ProductDevelopment

Most conversations about AI focus on models. But the real innovation today is happening in how AI thinks, plans, acts, and improves — autonomously. This is where Agentic AI stands apart. Over the past year building agent systems, testing LangGraph, ReAct, ToT, Google A2A, MCP, and enterprise orchestration layers, one pattern has become clear: To build effective AI agents, you need more than prompts or tools — you need a cognitive operating system. Here is a simple, foundational framework called C-O-R-E-F, that captures how autonomous AI agents operate: C — Comprehend The agent understands the input, intent, and context. It reads prompts, data, documents, and knowledge bases to extract goals, constraints, and entities. O — Orchestrate It plans and reasons. The agent selects the best approach, breaks the goal into steps, and chooses the right strategy or chain-of-thought. R — Respond Execution happens. The agent calls tools, APIs, or systems, generates outputs, updates databases, schedules tasks, or creates content. E — Evaluate The agent checks its own work. It compares outputs, validates information, runs tests, or uses an LLM-as-a-judge to detect errors or inconsistencies. F — Fine-Tune The loop tightens. The agent refines its logic based on feedback or logs, learns from outcomes, and improves future performance. This cycle is not linear — it is iterative and continuous. Every advanced agent system eventually converges to this pattern, regardless of framework or model. If you're building agentic systems, start thinking in loops, feedback, and orchestration layers, not just responses. The future of AI belongs to those who design thinking systems, not just powerful models.

My 5 biggest takeaways from Kun, ex-Meta L8 engineer, on how he set up his agentic engineering system: 1. Spend most of your time planning and validating, not coding. Kun sees himself as the manager of an always-on engineering team. His job is to create plans, validate work, and improve the overall system. The coding phase is handled almost entirely by agents. 2. The quality of your plans determines how long agents can work on their own. Memes about loops aside, an one-line prompt might keep an agent working for a few minutes but a detailed plan can keep it going for hours. If you want agents to run longer, invest more upfront in the spec, goals, and validation criteria. 3. Use visual plans, not walls of markdown. Kun built Lavish, an open-source tool to make visual HTML plans, that anyone can use for free: https://lnkd.in/garrJXj9 Instead of reading a giant markdown plan, Lavish turns it into a visual HTML artifact where you can leave inline feedback. This makes it much easier to tell the agent exactly what to change before coding starts. 4. Run agents in parallel, but isolate the work. Kun uses Treehouse to manage reusable worktrees so agents don’t step on each other’s changes: https://lnkd.in/g_8c2hfk If the work is exploratory or likely to fill the context window, he delegates it to a subagent. This way, the main agent stays focused while the subagents dig, test ideas, and report back. 5. Let agents review the code before you do. Kun no longer manually reviews every AI-written PR. Instead, he uses No Mistakes to run a fresh agent review, rebase the change, run tests, update docs, create the PR, and assign a risk level: https://lnkd.in/gRRky6dK In Kun’s testing across 267 agent changes in 15 repos, No Mistakes caught and fixed 68% of mistakes that would have been missed. Kun walked through Lavish, Treehouse, No Mistakes, and his full agentic engineering workflow in our episode. 📌 Watch it here: https://lnkd.in/gfKEjG7s

Founders: the AI Agent era is your once-in-a-decade window. 2023 gave us generative AI.  2024 introduced AI Agents.  2025 is the year they go mainstream. In a recent workshop led by Amitay Boneh at Google for Startups, we explored this shift and why those who enter now are still pioneers. Here are my top takeaways: 1. Think beyond “If-this-then-that” rules. Example: Instead of “New lead → send generic email,” build an agent that researches the lead’s company, checks CRM history, and decides whether to email, call, or add a task in Slack. Try this: Pick one repetitive process in your business and map out how an AI Agent could add decision-making steps. 2. Pick the right stack for your stage. No-code (Zapier, Make) → Good for testing quick ideas without engineering help. Low-code (n8n, Flowise) → Best for SMB/startup scale; connects to APIs, CRMs, and databases without heavy coding. Code frameworks (LangChain) → Ideal if you’re embedding agent logic directly into your product. Try this: Audit your current automation tools and decide whether to add a low-code layer for flexibility. 3. Leverage n8n for flexibility and cost control. Pay per completed workflow (not per step) → saves money on complex processes. Self-host for lower cost + full control over sensitive data. Visual builder → powerful enough for developers, simple enough for ops teams. Try this: Install the free self-hosted version of n8n and rebuild one existing workflow to test. 4. Build the feedback loop into your agent. Example: A customer support agent who, at the end of each day, reviews all conversations it handled, flags any low-satisfaction ratings, compares its responses to “gold standard” examples, and updates its own answer templates for next time. Try this: Add an “end-of-day review” step to your AI workflows so they can self-correct and improve continuously. We’re at the start of the curve. Just like the early days of mobile apps, the founders who start experimenting now will write the playbook for everyone else. What stage are you at with your experimentation of AI agents?

A few weeks ago, one of our AI agents reviewed itself. It read its own logs, found where a human kept correcting it, and proposed fixes to its own instructions. Nobody asked it to. The routine just ran. That is the part people do not expect. Not that the agent worked. That it managed itself.   We run a handful of agents with names, job descriptions, and a seat on the org chart. The moment an agent has those, it stops being software. It is a coworker. And the real question stops being which model, and becomes who keeps it improving.   So we built a loop. The agent reviews itself and proposes its own fixes. Then we review it like a hire: did the work hold up, how often did a human step in, do people use it or route around it. Promote, re-scope, or cut.   One research agent went from unreliable to trusted once we turned its core task into a tool it could call. Another never improved. We cut it on day 13.   Gartner says more than 40% of agent projects will be cancelled by 2027. Not from weak models. From no value and no controls. The ones that die have no loop.   The model is rented. The loop that improves it is the moat.   What does your loop look like?   #AgenticAI #EnterpriseAI #FutureOfWork #AIagents

Give me 5 minutes and I’ll show you how to build your first AI agent that can save you 10 hours a week: 1. Pick one painful recurring workflow Choose something boring, repeated, context-heavy, and decision-oriented. Examples: email triage, meeting prep, customer feedback review, competitor tracking, weekly status updates, PR / ticket review, internal documentation lookup, etc. Your first agent should not be an AI chief of staff. It should be one workflow that wastes time every week. 2. Define the trigger An agent needs to know when to run. Let's say, every morning at 9, when a new email arrives, when a ticket is created, when a PR opens, when a Slack message matches a condition, etc. 3. Map the tools and context Your agent is only useful if it can touch the systems where work happens. Connect your Gmail, Calenda, Drive, Slack, GitHub, Jira, Notion, Confluence The system is: model + tools + context + workflow + guardrails. 4. Write the agent spec before building Before opening Codex, Claude Code, or any coding agent, define: • objective • inputs • tools • constraints • output format • human review point • success criteria • failure modes Agent building is purely behavior design. 5. Build a workflow, not a prompt Bad: “Summarize my emails.” Better: “Every hour, check unread emails, identify which ones need a reply, use Gmail and Drive context to draft responses, and leave them in drafts for me to review.” That has trigger, tools, context, task, output, and human review. 6. Give the coding agent skills Don’t just ask: “Build me an agent.” That’s how you get broken code, wrong patterns, missing evals, no observability, and weak deployment structure. Give the coding agent instructions for: • framework patterns • tests • eval data • deployment setup • observability • guardrails • best practices 7. Test locally first Before deploying anything, test it locally. Check: • Did it use the right tool? • Did it retrieve the right context? • Did it follow the right steps? • Did it stop for human review? • Did it hallucinate? • Did it overreach? The goal here is to make sure... “Can I trust this repeatedly?” 8. Add evals and guardrails before trust Agents are not deterministic. A good final answer can still come from a bad path. So evaluate: • output quality • tool calls • retrieved context • reasoning path • cost • latency • safety • failure cases • human handoff behavior Then add guardrails: • draft, don’t send • review, don’t merge • recommend, don’t execute • summarize, don’t contact customers 9. Deploy only when observable Before deployment, make sure you can see every model call tool, retrieved document, failed step, cost driver, escalation, user correction, etc.

The AI agent gold rush is here. But most builders are drowning in tool choices. Forget the 50-tool tech stacks you see on Twitter. Here's the minimal setup that powers production agents: 𝟭. 𝗗𝗲𝗳𝗶𝗻𝗲 & 𝗗𝗲𝘀𝗶𝗴𝗻 Skip the fancy stuff. Start with: • Miro/Whimsical for mapping agent workflows • Figma for UI/UX if you need interfaces Instead of jumping straight to coding, map your agent's decision tree first. 𝟮. 𝗦𝘁𝗮𝗿𝘁 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 Your framework choices matter: • LangGraph for complex multi-step workflows • Phidata for simpler, production-ready agents • Replit for quick prototyping (seriously underrated) I switched from raw OpenAI calls to LangGraph. The difference was night and day. 𝟯. 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿 This is where most agents fail. Pick based on your needs: • Supabase for general data + auth • Pinecone/Chroma for vector search • Neon for PostgreSQL that scales Pro tip: Start with Supabase. Add vector DB only when you actually need it. 𝟰. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Agents without memory are essentially glorified chatbots: • LangMem helps agents learn and adapt from their interactions over time • Zep for long-term user context • MemGPT for complex reasoning chains 𝟱. 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 The difference between hobby and production: • LangSmith for debugging agent flows • Langfuse for cost tracking • Arize for performance monitoring You don't need every tool on this list from day one. Start with: 1. Design tool (Miro) 2. Framework (LangGraph/Phidata) 3. Database (Supabase) 4. Basic memory (built-in) 5. Testing (LangSmith free tier) Total cost to starts under $50/month. Your agent doesn't need 20 different tools. It needs the RIGHT tools. Over to you: What's the first AI agent you want to build?

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build. Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention. The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention! Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on. The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience. When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful. [Truncated for length. Full text: https://lnkd.in/gKDQ6H9s]

𝗧𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝗜 𝘄𝗶𝘀𝗵 𝗜 𝗵𝗮𝗱 𝘄𝗵𝗲𝗻 𝗜 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀! ⬇️ Built together with Rakesh Gohel (aka Mr. AI Agent) — and now yours! We broke it down into 7 essential steps to go from zero to scalable, production-ready agents: 1. 𝗣𝗶𝗰𝗸 𝗮𝗻 𝗟𝗟𝗠 ➜ Choose a model that reasons well, supports step-by-step logic, and gives consistent outputs. Tip: Llama, Claude Opus, or Mistral are great for open-weight setups. 2. 𝗕𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗟𝗼𝗴𝗶𝗰 ➜ Should it reflect before answering? Plan or act directly? What if it gets stuck? Start simple with ReAct or Plan–then–Execute. Don’t overcomplicate. 3. 𝗪𝗿𝗶𝘁𝗲 𝗶𝘁𝘀 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 ➜ Define how it should respond, when to use tools, and what formats to reply in. Reusable prompt templates are your friend here — they scale better than hardcoded flows. 4. 𝗔𝗱𝗱 𝗠𝗲𝗺𝗼𝗿𝘆 ➜ LLMs forget. Your agent can’t. Use sliding windows, summaries, or memory frameworks like MemGPT or ZepAI to persist key facts and long-term context. 5. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝗧𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗔𝗣𝗜𝘀 ➜ Let the agent do things: query data, call systems, fetch information. Just be explicit about what tools exist and when to use them. 6. 𝗚𝗶𝘃𝗲 𝗶𝘁 𝗮 𝗝𝗼𝗯 Bad prompt: “Be helpful.” Good prompt: “Summarize customer feedback and suggest improvements.” Narrow scope wins. The tighter the job, the smarter the agent. 7. 𝗦𝗰𝗮𝗹𝗲 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗧𝗲𝗮𝗺𝘀 ➜One gathers data. One interprets. One formats results. You don’t need a super-agent. You need a smart team — built for specific tasks. 𝗢𝗻𝗲 𝗶𝗺𝗮𝗴𝗲. 𝗦𝗲𝘃𝗲𝗻 𝘀𝘁𝗲𝗽𝘀. 𝗜𝗻𝗳𝗶𝗻𝗶𝘁𝗲 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀! From solo agents to orchestration-ready systems — this is how you scale with intent. Image below. Save it. Use it. You can find more info in the comments! (Note: The entire roadmap is not exhaustive and can differ according to different use cases) ♻️ Share this to help your network level up. 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E

Loop engineering is replacing yourself as the person who prompts the agent. You design the system that does it instead. My latest free deep dive: https://lnkd.in/gV_b9AsR ✍ A loop can be thought of a recursive goal where you define a purpose and the AI iterates until complete. I believe this may be the future of how we work with coding agents. However, its still early and you absolutely have to be careful about token costs. For two years, the way you got something out of a coding agent was simple: write a good prompt, read what came back, write the next prompt. You held the tool the whole time, one turn after another. That part is changing. Peter Steinberger of OpenClaw put it like this: "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." Boris Cherny, head of Claude Code, said the same thing differently: "I don't prompt Claude anymore. I have loops running that prompt Claude." A loop is five things: → Automations - a heartbeat that runs discovery on a schedule so you're not the one going around checking → Worktrees - isolated branches so parallel agents don't collide on the same files → Skills - project knowledge written down once so the agent doesn't re-derive your conventions from scratch every run → Plugins/connectors - MCP connections to your real tools, so the loop opens PRs and updates tickets instead of just telling you what it would do if it could → Sub-agents - one agent writes, a different agent checks. The one who wrote the code is too nice grading its own homework. Plus one more thing that sounds too simple to matter: a memory file. Markdown, a Linear board, anything that lives outside the conversation. The agent forgets between runs. The repo doesn't. What surprised me is this isn't a bespoke scripting problem anymore. A year ago building a loop meant a pile of bash only you could maintain. Now the pieces ship inside the products. Claude Code and Codex both have all five. But three problems get sharper as loops get better, not easier: The loop changes the work, it does not delete you from it. And three problems actually get sharper as the loop gets better, not easier. Verification is still on you. A loop running unattended is also a loop making mistakes unattended. Your understanding still rots if you allow it. The faster the loop ships code you did not write, the bigger the gap between what exists and what you actually get. Thats comprehension debt and a smooth loop just makes it grow faster unless you read what the loop made. And the comfortable posture is the dangerous one. When the loop runs itself its very tempting to stop having an opinion and just take whatever it gives back. I called that cognitive surrender. Designing the loop is the cure when you do it with judgement. Build the loop. But build it like someone who intends to stay the engineer, not just the person who presses go. #ai #softwareengineering #programming