Start with an executable specification
AI-assisted delivery makes implementation faster, but it does not make an ambiguous requirement precise. Define the user task, constraints, expected behavior and unacceptable failure modes before asking an agent to build. A useful specification includes concrete examples, permission boundaries and the evidence needed for acceptance. Treat it as a versioned contract that business owners and engineers review together.
Separate deterministic tests from model evaluations
Unit and integration tests still establish whether software obeys its contracts. Model evaluations answer a different question: whether probabilistic behavior is useful and safe on representative tasks. Build a task-level evaluation set with expected outcomes, grounded sources and adversarial cases. Check incorrect tool selection, insufficient evidence, access isolation and escalation alongside successful answers. Keep a regression set and compare each release against the same baseline.
Give delivery agents bounded authority
An agent that can edit code should not automatically be able to approve or deploy it. Scope tool permissions to the task, isolate working environments and require review for consequential changes. Preserve an audit trail of the specification, code changes, test output and reviewer decisions. Treat generated dependencies, configuration and migration scripts as untrusted until checked, just as you would any other external contribution.
Keep humans accountable for release decisions
Generated code and tests can share the same mistaken assumption. Independent review remains essential for architecture, security, privacy and business logic. Set explicit acceptance gates before release, including the evaluation threshold, unresolved risks and rollback criteria. A faster development loop is valuable only when it improves a real user outcome without hiding operational risk.
Observe the product after it ships
Passing an evaluation set does not guarantee reliable behavior on changing production inputs. Track task completion, failure and escalation rates, latency and cost per useful outcome. Tie traces to model, prompt, tool and specification versions so regressions can be diagnosed. Feed reviewed production failures back into the evaluation set. The result is a delivery loop that learns without abandoning engineering discipline.
These are general planning considerations, not a substitute for an assessment of your specific business, regulatory, and technical requirements.
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