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This is one practitioner’s account and design approach—not a validated recipe or evidence that autonomous agents can reliably deliver software without oversight. Dechrai’s article describes how the idea developed and why he began treating agent work as a coordinated process rather than a single coding session.
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What “dark software factory” means in this account
“Dark” here describes a software workflow intended to keep moving with less moment-to-moment human direction. It does not mean a particular commercial product or a proven category of software. Dechrai uses “factory” for a system that organizes software work into stages, with defined responsibilities and handoffs.
The distinction matters: his story is about designing an operating model around coding agents, not simply asking an AI assistant to write code. The agent handles work within a process shaped by specifications, plans, iteration and guardrails; the human remains involved in defining and overseeing that process.
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Why the first coding-agent workflow fell short
Dechrai says he initially tried to make Claude Code handle tasks for longer than a few minutes. His approach was to write a mini-spec, break the work into tasks and have the agent complete one task at a time. In his experience, two problems emerged:
- The agent would pause and ask whether it should continue, interrupting the intended longer run.
- In longer sessions, it could lose track of the task list and its context.
These are reported observations from his own use, not results from a controlled comparison of coding agents. They helped motivate a shift from relying on a long conversation to building a harness—a surrounding workflow intended to give the agent structure and keep work progressing.
From implementation loop to a wider workflow
By March 2026, Dechrai says he had built several harnesses in different forms. Some lived in a web app; others were global npm modules used alongside a project; one used GitHub Actions and issues. He reports that these experiments differed in reliability and maintenance burden, but converged on a common pattern: “spec, plan, loop, guard.”
| Stage | Purpose in the approach |
|---|---|
| Spec | Define what needs to be built and clarify the intended outcome. |
| Plan | Break the specification into work the implementation process can tackle. |
| Loop | Run the implementation work through an iterative build process. |
| Guard | Apply constraints and checks around that work. |
The phrase names the system’s shape, not a guarantee about its output. The account does not establish a general reliability rate, prove that one harness form works better than another, or quantify how much supervision the process removes.
The next problem: automating the work before coding
The early versions concentrated on implementation: once the work had been specified and planned, the build loop could run autonomously. But Dechrai says he was still manually translating nontechnical requirements into specifications and plans. That left an important part of delivery outside the automation.
His next design question was how to automate those upstream stages as well: gathering and refining requirements, turning them into a useful specification, and planning the resulting work. Automating coding alone can move effort rather than remove it if a person still has to prepare every task in detail.
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Why he borrowed the agency-delivery model
To reason about those stages, Dechrai compares the process with an agency’s delivery workflow. In that analogy, work moves through a sequence of responsibilities rather than being handed directly from a client to an implementer:
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- Have a technical lead turn those requirements into specifications and tickets.
- Assign implementation work.
- Send completed work through QA.
- Package accepted work for staging, integration testing and client acceptance before production.
He calls this kind of familiar agency workflow “a human finite state machine”: work changes state as it passes through defined stages and handoffs. The analogy gives him a way to think about organizing agent work, but it does not show that agents perform like experienced human teams or that the same workflow is suitable for every project.
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Persistent roles, not just one long-running agent
The design develops from the idea of a main agent interacting with him as the client, while he also takes a separate role as the factory’s co-founder. Dechrai connects agency responsibilities to persistent “seats”: agents associated with particular responsibilities, capabilities and history.
This shifts the design question from “How do I keep one agent coding longer?” to “Which responsibilities should exist, what information should each have, and when should work pass from one to another?” The value of the model is organizational clarity. Its effectiveness still depends on how well requirements, responsibilities, checks and handoffs are defined.
What the experiments do—and do not—show
Dechrai’s experiments across a web app, global npm modules and GitHub Actions/issues point to a practical trade-off: a harness can provide structure, but it also creates something to maintain. He reports mixed reliability and maintenance costs rather than a single universally successful implementation.
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- Important limitation: the account does not provide independently measured results or controlled comparisons of the tools and harnesses.
- Practical implication: more autonomy upstream may require clearer role definitions and handoffs, not merely a longer-running implementation loop.
For readers considering a similar approach, the story is most useful as a way to frame the workflow problem. It is not evidence that a “dark software factory” can safely or reliably ship production software without human review.
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