Agentic-Workflow

One Prompt, Seven Features, Four and a Half Hours

Part 10 of Building My Agentic Workflow in Public, and Part A of a two-part story. Part 9 ended with a tease: one short prompt, four and a half hours, and a result that surprised me. This is that story. Part B lands after retro 5 runs on a real epic. Start with what I found when I came back In early September I typed one prompt into my pipeline and went to live my life. Four hours and thirty-six minutes later, one session had planned and built a seven-feature epic on my observability project. It asked me exactly zero questions while doing it. ...

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The Question Economy: How My Pipeline Went From 20 Interruptions to 5

Part 9 of Building My Agentic Workflow in Public. In Part 8 I promised the numbers from round two of testing my pipeline. Here they are. The biggest one turned out not to be tokens, it was how many times the machine stopped to ask me a question. When I first measured an autonomous epic in early August, the bill was ugly: 9.5 million tokens for five features, about 12,600 tokens for every line of shipped code, and around 27 eval rounds where only 16 percent of the findings were real bugs. But the number that actually ran my life was a different one. The pipeline stopped to ask me about 20 questions per epic, and each one cost me 5 to 8 minutes, because each one arrived written in the machine’s own jargon and I had to translate it before I could even decide. ...

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Trust, but Grep: The Plan Was Perfect and the Premise Was False

Part 8 of Building My Agentic Workflow in Public. Part 7 audited a painful bill and found my own specs were the culprit. This is what round two of testing revealed: the plan was not wrong because it thought badly. It was wrong because it never looked. After the retro in Part 7, I rebuilt my pipeline around a set of new rules and ran the whole thing again on the same two projects, SleemAI and Morpheus Observability. Round two went better, and I will share the full numbers in a coming post. But one finding from the audit was so simple, and so embarrassing, that it deserves its own short post. ...

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The Paperwork Outweighed the Product: What Testing My Own Pipeline Cost

Part 7 of Building My Agentic Workflow in Public. Part 6 said do not be obedient, verify, because your own context window is small. This is the first time I turned that rule on my own process. Over six parts I built a pipeline that can design, plan, and ship a batch of features mostly on its own. Then I finally pointed it at two real repos, back to back, on the same day. One of them took about ten hours and burned close to ten million tokens to ship roughly five features. I sat with that for a minute. Ten hours. Ten million tokens. Five features. ...

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Obedience Is Futile: The Guardrail You Build Against Your Own Limited Context Window

Part 6 of Building My Agentic Workflow in Public. Part 5 settled which engine runs my loop. This one is about a different bottleneck, and it is not in the machine. It is in my chair. Way back in Part 0 I said the bottleneck in all of this is human. You can spin up two hundred agents, but you cannot keep up with two hundred agents, because your attention does not scale the way agent count does. That was the first face of the limit. This post is about the second face, and it is quieter and more dangerous: my context window. ...

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Two Ways to Run a Loop: Portable Agents vs Dynamic Workflows

Part 5 of Building My Agentic Workflow in Public, and the open end of it. Part 4 got me an autonomous build loop. This one is about a fork I am standing at right now, with no final answer yet. The last four parts had endings. This one has one, but a provisional one. I am still in the middle of it, and the whole reason I write these in public is to think out loud while the question is still live. ...

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Letting It Run: Toward an Autonomous Build Loop

Part 4 of Building My Agentic Workflow in Public. Part 3 gave parallel agents a shared contract so their work stayed coherent. That coherence is what let me finally take my hands off the wheel. By this point I had the pieces: a process, session handoffs, sub-agents, a contract, and an evaluator that could not cheat. What I wanted next was simple to say and hard to do. Hand the system a batch of features, walk away, and come back to something that is 85% to 90% done. ...

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When Agents Diverge: The Code Contract

Part 3 of Building My Agentic Workflow in Public. In Part 2 I pushed work onto parallel sub-agents to save context. This post is about the mess that created, and how I cleaned it up. Parallel sub-agents let me build faster. They also introduced a problem I did not see coming. Each agent built its piece reasonably. The pieces did not match each other. The divergence problem Picture three sub-agents, each handed one feature of the same epic. Each one sees the epic. None of them sees the others. So they each make their own small decisions: how to log, how to handle errors, what to name things, where shared helpers live, how their feature talks to the next one. Every one of those decisions is defensible on its own. Together they are a mess. ...

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Surviving the Context Window

Part 2 of Building My Agentic Workflow in Public. Part 1 turned my prompting into a repeatable process. This one is about the wall I hit right after: a single session is never big enough for real work. In Part 1 I turned the way I work into a repeatable workflow: an agile-style vocabulary of epics, features, tasks, and defects, with a command to drive each step. It made the work predictable. It didn’t solve the bigger problem, which showed up the moment the work got real. An LLM session has a memory limit, and serious work blows right past it. ...

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From Prompts to a Process: Making Claude Code Repeatable

This is Part 1 of Building My Agentic Workflow in Public, a series on how I actually work with Claude Code day to day. It starts with the problem from AI Fatigue: the tools got faster, my brain didn’t. This post is the first thing I did about it. For a while, every session with Claude started the same way. I re-explained the project. I re-explained how I like things named, where files go, what “done” means. The model was capable. I was the bottleneck, retyping the same context over and over. Then the session ended, and all of it died with it. ...

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