Your AI Agent Finished Coding. Is Your Delivery System Ready?

AI-generated code is produced quickly, but the process stops there. The true challenge lies in how the generated code integrates into the existing workflow. The delivery system must handle three critical handoffs: transforming a task into usable context, converting code into reliable evidence, and granting permission to ship.

Firstly, a ticket indicating "make the test pass" is insufficient. The AI agent requires additional context, such as the specific access control needed, the owning service, and the unaltered behaviors. This contextual information may be dispersed across code, documentation, and prior discussions. Instead of overwhelming the AI with all relevant sources, it's better to prepare a concise set of instructions tailored to each task.

These instructions should include the intended behavior, constraints, examples, and a method to run checks. Confidential information should be limited to what's necessary for the task.

Secondly, merely passing a check does not guarantee a fully verified change. For an access-control fix, a formatting check doesn't address whether unauthorized users remain blocked. Validation should focus on the risk posed by the change, using controlled tests that yield clear signals of failure. Validation speed is crucial as AI-generated changes become faster.

Techniques like caching, parallel execution, and shorter feedback loops can expedite this process without compromising quality. However, comprehensive validation should still be conducted at appropriate release gates.

Lastly, even after thorough testing, a well-tested patch still requires approval before it can be shipped. For our access-control example, a clear security review process and approval authority must be established before the pull request proceeds. Permissions should be enforced through existing systems rather than solely based on AI instructions.

The system should maintain all necessary information for future investigation, including the original task, context, tool activity, test outcomes, final diff, and approval records. This information should be accessible to external parties to ensure a full audit trail.

Before expanding AI's role in coding, it's essential to examine the handoff process. Measure the time spent on clarifying requirements, waiting for checks, fixing validation failures, and seeking reviews. Identify areas where additional information is needed or responsibility is unclear. By improving these handoffs, teams can move AI-generated code through the software delivery lifecycle more reliably.

The focus should be on ensuring that useful changes can successfully navigate the lifecycle, not just on how quickly an AI agent can produce code.