We design governance around decisions, actions, and accountability, so every agent has a clear scope, tool permission set, escalation path, and audit trail. That usually includes approval thresholds, role based access, prompt and policy versioning, evaluation gates, logging of tool calls, exception review, and business ownership for ongoing changes once the system is live. Good governance should not feel like a compliance tax; it should make agent behavior easier to trust, easier to monitor, and easier to improve without turning every release into a fire drill.

We test agents against real workflow scenarios, not just synthetic happy paths, because production failures usually come from ambiguity, missing context, tool misuse, and edge cases that look small until they compound. Evaluation covers task completion, handoff quality, grounded reasoning, tool selection, latency, cost behavior, and policy compliance, along with adversarial testing for bad inputs, incomplete data, and conflicting instructions. Once the system moves into rollout, we keep measuring live performance so the team can catch drift, tighten prompts, update business rules, and improve decisions with evidence instead of intuition.