If you treat an AI agent like an expert who reads minds, you deserve the hallucination it delivers.
Require agents to walk through implementation plans before code to prevent seniors from fixing avoidable mistakes that should never have started.
Example: A team that allows an agent to dive into code without a prior review finds itself untangling a logic knot that a simple plan would have exposed.
Models apply global knowledge to local contexts; without specific architectural constraints, the output is technically correct but operationally useless.
Example: An agent writes a high-performance search function that violates company data privacy compliance because it was never told which data was sensitive.
Moving from vague directives to explicit context catches errors in the planning phase where they cost nothing to fix.
Example: A developer catches a circular dependency in an agent's implementation plan before a single line of code is written, saving three days of integration testing.
It is always cheaper to fix a plan than to refactor a hallucinated monolith.
From the Executive Brief
Provisioning models without improving specifications is simply a faster way to generate technical debt rather than real engineering value.
Example: A leader tracks how many seats are provisioned in a new model rollout instead of measuring if the code produced actually passes review on the first attempt.
Engineers must treat billion-parameter models as high-leverage juniors who require a clear, rigorous brief rather than a magic wand.
Example: A senior engineer spends thirty minutes writing a rigorous specification and receives a perfect pull request, avoiding hours of trial-and-error chatting.
Vague directives and trial-and-error
Hallucinated monoliths and technical debt
Explicit context and plan-first workflow
High-leverage output and first-pass success
You will continue to buy faster ways to generate technical debt while your seniors burn out on rework if you do not gate this rollout by success rates.