Autonomous agents fail quietly without tool allowlists, escalation paths, and evaluation loops. Here is how we ship them safely.
AI agents look impressive in demos and fragile in production. Without tool allowlists, confidence thresholds, and human escalation, they take irreversible actions or invent answers with high confidence.
Guardrail patterns
We design agents with explicit action boundaries: read-only tools by default, write tools behind approval, and hard stops for high-risk operations like refunds, access grants, or data deletion.
Evaluation loops
Evaluation is continuous. Golden task suites, shadow mode, and regression checks catch prompt and model drift before customers do.
Operating model
The operating model matters as much as the model. Ownership, runbooks, and on-call paths turn an agent from a science project into a production system.