Observe
Model, route, token usage, retries, tool calls, latency, outputs, changed artifacts, and evaluator results.
AI & engineering
Give autonomous work a budget, boundary, and burden of proof.
AI systems should not earn trust from fluent output or green CI alone. They need routed cost controls, stable evaluations, protected evidence, explicit permissions, and human approval where consequences matter.
01
Agent cost control
Estimate routed work, enforce configured spend caps, and expose waste before a run becomes expensive.
See Runcap →02Evaluation & reliability
Build repeatable datasets, rubric-based checks, regressions, and an evidence trail around model behavior.
See the capability →03Permissions & data
Constrain tools, tenants, retrieval, secrets, write actions, and high-impact operations.
See the capability →04Merge evidence
Protect workflows and verifiers from the same AI-generated change they are meant to judge.
Inspect the case →Control
Budgets, allowed scope, permissions, stop conditions, approval gates, and fallback behavior.
Prove
Replayable tests, immutable evidence, failure examples, versioned prompts, and documented limits.
Start with a failing run
Bring one agent or AI feature whose cost or correctness is difficult to defend.
The first diagnosis separates observability gaps, control gaps, and model-quality gaps.