Reliability Agents for Incident Detection and Response
RunLLM predicts issues before alert thresholds fire, investigates from first principles, and resolves incidents it's never seen before.
Trusted by
Other AI SREs are no-op without runbooks
Other AI SREs require alert thresholds for every data stream and runbooks for every failure mode. Miss one and agents are no-op. Only RunLLM detects anomalies before thresholds fire and investigates novel incidents without runbooks.
- Autonomous Onboarding Simply connect data sources and we map your architecture, system relationships, queries and data types.
- Predictive Incident Detection Custom anomaly detection models on each data stream. Surfaces validated issues with root cause before any alert fires.
- RCA Without Runbooks Multiple hypotheses, parallel sub-agents, RCA in minutes. 100% accuracy on known incidents. 70% on novel ones.
- Continuous Learning Gets more accurate with every investigation automatically. No model retraining. No knowledge base to maintain.
- Glass-Box Reasoning Hypotheses ranked by confidence with evidence chains your team can verify, steer, and correct in real time.
"This product performance during the PoC was very strong and our team is impressed. We're happy to be adopting it."
— VP Infrastructure, AI Company
No Alert Thresholds to Tune
RunLLM builds custom anomaly detection models per data stream. You don't have to anticipate every failure mode.
No Runbooks to Write
RunLLM investigates from first principles by understanding your environment. Other solutions require runbooks for every alert type.
Accurate on Incidents Nobody Anticipated
70% RCA accuracy on novel incidents. The only number that matters when something genuinely unexpected breaks.
Live in days, Not Months
Herald self-onboards across your existing observability stack. No professional services, no setup project, no rip-and-replace.