Reliability & Self-Healing Systems Lab

Enterprise Autonomous Agent Self-Healing Pipeline & Regression Canary Simulator

Model and benchmark enterprise autonomous agent failure resilience. Quantify Mean Time to Detect (MTTD), Mean Time to Recover (MTTR), and token burn arbitrage between unmonitored rollouts, naive retry loops, and sovereign statistical shadow canaries with sub-320ms automated rollback.

Failure Resilience & Self-Healing Benchmark

Elesium Sovereign Canary & AST Rollback

Statistical shadow canary verification (5% partition), Kolmogorov-Smirnov drift detection in sub-9 seconds, and automated sub-320ms golden state rollback.

MTTD: 9s | MTTR: 320ms | Cascade Risk: 0.02% | Recovery: 99.98%

Naive Rule-Based Exception Retry Loops

Fixed retries on deterministic failures trigger infinite death loops, burning 4x-10x tokens, exhausting API quotas, and stalling downstream task queues.

MTTD: 120 mins | MTTR: 30 mins | Cascade Risk: 52.0% | Death Loops

Unmonitored Monolithic Agent Rollout

Zero runtime telemetry; undetected tool schema drift corrupts production databases silently, causing 24-hour average detection lag and manual fire drills.

MTTD: 1,440 mins (24h) | MTTR: 2 hrs | Cascade Risk: 88.5% | Severe Outage