awslabs/aidlc-workflows: AI-Driven Life Cycle AI-DLC adaptive workflow steering rules for AI coding agents

Each implementation step stays small enough to review, while https://www.m-sedan.com/homelink_wireless_control_system_-7212.html tests provide evidence for the final human decision. The best AI coding workflow is one that catches mistakes before they spread. The developer remains responsible for the architecture and final release decision.

A Bash skill can capture shell-specific validation. If an agent repeatedly makes the same mistake, I add a more precise rule. I keep my shared AI development setup in the titstack repository. One giant prompt that loops until an agent declares victory may produce a demo, but it is not how I build software I plan to maintain.

Update Current phase and Task as the work progresses instead of asking one prompt to cover the entire feature. You can use this as the opening instruction in Kimi Code. A simple fix can move quickly, while a complex or risky change needs more review before release. Not every coding task needs the same amount of planning. For a small fix or a task that depends on one changing file, a single agent is usually more efficient. Give every agent the same approved specification, assign clear ownership, and use isolated branches or worktrees to prevent conflicts.

Pattern 2 — Treat AI as a Pair Programmer, Not an Autonomous Engineer#

• The better measure is the time from a clear requirement to a verified change that the team is willing to maintain.
• Describe the desired behavior before prescribing an implementation.
• The workflow below gives the developer control over decisions while assigning repeatable investigation and implementation work to the agent.
• Planning support and clear approval controls also matter when a task affects multiple files.
• Suppose you want to add dark mode to an existing web app.

You still need to understand Git, testing, and the basics of software development. This is for larger, more complex software. You should be comfortable with Python and working in https://www.nialtima.com/front_power_window_switch-1797.html the terminal. Once you know the layers, every new model, harness, and provider becomes a choice you can make instead of a default you inherit.

Implement One Reviewable Change

One agent may investigate the repository while another reviews a completed diff. The response should match the documents and the current repository state. Preserve the engineering state in repository artifacts instead of relying on chat history.

• Confirm that it uses existing project abstractions where appropriate.
• Check that the proposed tests demonstrate the requested behavior rather than merely exercising newly written functions.
• During implementation, you check whether the approved direction was followed correctly.
• Error logs and issue descriptions are also useful, but remove credentials or private user data before placing them in a prompt.
• Look for hidden scope, especially new dependencies or public API changes that were not part of the requirement.

The AI Workflow Checklist

This prevents a feature request from becoming an uncontrolled rewrite. The agent needs observable conditions that tell it when the phase succeeds, including the tests or manual checks that prove it. I pin the supported versions in the specification and verify current versions instead of accepting whatever the model remembers. Version details matter because an LLM may choose an old dependency simply because it appeared frequently in its training data. That reusable knowledge then follows me into the next project.

Start with repository instructions

At each step you’ll see how the change shifts cost, speed, and usage, and what it means for what leaves your machine. From there you’ll switch to an open-source coding agent, connect it to different inference providers, and finally run models locally. These tools give you a ready-made way to work, which is what makes them convenient. Coordination overhead can outweigh the time saved when several agents touch the same files. Multiple agents help when the work can be divided into independent tasks with clear ownership.

Latest commit

These actions have a larger blast radius than ordinary file https://rpk-fusion.ru/justhookup-com-evaluation-in-2020-features-pros-drawbacks/ edits and need a separate decision. Give each workstream clear file ownership, then integrate only after its checks pass. Use a separate branch or worktree when an experiment may touch many files. Version control makes AI-assisted work easier to inspect and recover. An agent-generated summary can help with review, but it does not replace reading the code. Remove unrelated refactoring, unexpected interface changes, and unnecessary formatting edits.