Blog
Insights on AI agent observability, governance, accountability, and the engineering practices that make autonomous systems trustworthy.
Compliance
When AI Agents Fail: Post-Incident Analysis for Autonomous Systems
Traditional post-mortems assume a human made a decision. Agent incidents require a new playbook — one that reconstructs reasoning traces, identifies systemic failure modes, and prevents recurrence.
Compliance
Provenance Engineering: Making Every AI Decision Reproducible
When a regulator asks why your agent approved a loan or denied a claim, can you reconstruct the exact context, reasoning, and model state that produced that decision? Provenance engineering makes the answer yes.
Compliance
Accountability as Code: Building Provable AI Audit Trails
When an AI agent makes a consequential decision, can you prove why? Accountability as Code turns every agent action into a cryptographically verifiable, tamper-evident record.