Agent readiness for heads of AI
The head of AI owns the gap between a demo and a dependable system. Pilots succeed on capability; production survives on operations — evaluation, observability, and governance that keep an agent trustworthy after its tenth model update, not just its first demo.
Key concerns
The pilot-to-production gap
An agent that delights in a demo has proven capability, not reliability. The work between is operational: evaluation sets that gate changes, traces that make failures debuggable, and controls that make the risk story credible to security and compliance. Teams that skip this stall at pilot — not because the agent is weak but because nobody can prove it is safe.
Quality drift
Models update, prompts get edited, tools change — and agent behaviour moves with all three. Without continuous evaluation, quality drift is discovered by customers. The evaluation set is the asset that compounds: every production surprise becomes a case. Treat the eval set as a product asset with an owner, a backlog, and a growth target, not as a test fixture someone wrote once.
Credibility with governance functions
The head of AI is the translator between what agents can do and what the organisation will permit. Arriving with evidence — scores, traces, risk profiles — turns governance reviews from blockers into formalities. Arriving with anecdotes turns them into quarters of delay.
Readiness checklist
- Every production agent has an evaluation set that runs on every model, prompt, or tool change
- Step-level traces exist for every production task, with cost per task tracked
- Human-in-the-loop gates concentrate on irreversible actions, and rejection rates are reviewed
- Each agent's risk profile is current with its actual tool list
- A defined promotion path moves agents from pilot to production on evidence, not enthusiasm
- Post-incident, every surprise becomes an evaluation case