Runtime governance for AI-agent fleets that continuously monitors agent health, confidence, and behavior through check-ins, and returns verdicts to enable self-correction before failures occur.
The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.
Enables LLM-driven agents to autonomously detect, diagnose, repair, verify, and prevent software and hardware failures on local and remote systems. Includes built-in safety checks and automatic rollbacks.
A unified MCP server providing observability, safety control, and behavior evolution for high-agency AI agents through tracing, replaying, and auditing. It features real-time firewall guardrails and ML-driven anomaly detection to monitor, block, or fork agent actions based on risk.