Engineering leadership
Engineering questions for AI-assisted delivery
Use these guides to connect agent work to requirements, strategy, and review evidence. Each answer names the checks that can be automated and the decisions that still require human judgment.
Questions engineering leaders ask
10 questions
- 01 How do I verify AI-generated PRs against product requirements? Verify an AI-generated pull request by linking it to ratified product intent, checking its exact acceptance criteria, and recording evidence for each conclusion.
- 02 How do I stop AI coding agents from using stale requirements? Stop coding agents from using stale requirements by giving them one live source of product intent and gating work whose premise is obsolete.
- 03 How do I give AI coding agents the right product context? Give coding agents bounded, live product context that connects the task to its user, problem, requirement, and strategic outcome.
- 04 How do I trace an AI-generated PR back to product strategy? Trace an AI-generated pull request through its ticket, requirement, project, goal, and strategy with explicit product relationships.
- 05 How do I review more AI-generated PRs without slowing delivery? Review more AI-generated pull requests by automating evidence collection and reserving human judgment for ambiguous or authoritative decisions.
- 06 How do I know whether an AI agent built the right thing? Determine whether an AI agent built the right thing by verifying product fit, accepted behavior, and implementation evidence separately.
- 07 How do I prevent multiple AI agents from building the same feature? Prevent duplicate agent work with explicit task claims, shared product scope, and code-level duplicate implementation checks.
- 08 How do I catch contradictions between tickets and strategy? Catch contradictions by preserving the ticket-to-strategy chain and evaluating each parent-child intent relationship with cited evidence.
- 09 How do I make product requirements machine-readable for AI agents? Make requirements machine-readable with typed fields, explicit relationships, lifecycle state, testable clauses, and stable identifiers.
- 10 How do I audit why an AI-generated code change was made? Audit an AI-generated code change with durable provenance from the pull request to the requirement, product decisions, evidence, and human authority.
See how Helen checks the work.
Read each detector’s evidence, method, implementation policy, and limits.