15 posts
Run a monthly drill with a deliberate violation. Verify which controls reject it, report failures, and confirm cleanup. The drill attempts real merges, including an administrator override; use a disposable test repository and inspect the…
Run the task queue on a schedule. Separate urgent failures, requests for your decision, and routine logs. Detect missed runs, and understand why a watch on the same machine cannot detect that machine's complete failure.
Add a separate reviewer, require automated checks before merging, and verify the deployed result before marking a task complete. Configure a runner, a GitHub workflow, and command restrictions, with explicit limits on what each can verify.
Give each task acceptance criteria, dependencies, and a status. Define how the agent selects the next eligible task and when it must stop. Includes a queue template and a five-item exercise.
Write down the actions agents may take, the actions requiring your approval, and the evidence they must provide before reporting completion. Includes a permission template and exercises that check whether an agent follows it.
Document which repository owns each part of the project. Assign each agent a specific scope and a separate checkout to prevent overlapping edits. Includes layout instructions and a repository-splitting exercise.
Turn a project rule into a script that detects violations. Run it before each push and test it with a deliberately invalid change. Learn what a local Git hook checks and how it can be bypassed.
Write a decision record documenting what you chose, which alternatives you rejected, and when to reconsider. Instruct agents to read relevant decisions before proposing changes, then test whether they retrieve them.
The full path to an agent platform that edits its own instructions is already published, scattered across seven articles that never mention each other. I run that architecture as one person: supervisor and worker tiers, verification guar…
Record what a feature must do, what is outside its scope, and how you will verify it. Create a specs directory and template, then give the agent an approved spec to implement.
SpecSelf looks like a set of features: coherence checks, persona rotation, review cadences, an audit trail. Every one of them was implied by ten frontmatter fields decided on day one. A life-OS is not a feature list. It is a schema decis…
I built a method as a public repo and the product that runs it as two private ones, and none of them treated the others as a source of truth. The domain enum lived in four places. A persona drifted between its lens file and its API contr…
Dumping the whole corpus into an AI agent makes it worse, not better. The fix is architectural: each task loads a curated slice, not everything you have. Here is the method, and the same move at three different layers: specs, sensor data…
Spec-driven development reads like a methodology for controlling AI agents. It isn't. It's a methodology for managing context across stateless sessions. The spec is the persistent memory.
Why spec-driven development and structured folder architecture are the missing infrastructure for AI-assisted engineering: methodology, common mistakes, and where to start.