A Model Context Protocol server that empowers AI agents with metacognitive monitoring to detect reasoning loops and provide intelligent recovery using case-based reasoning and statistical analysis.
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.
Detects and breaks repetitive fix loops in AI coding assistants by tracking attempts and providing escalating intervention strategies. It utilizes error fingerprinting and similarity analysis to redirect the AI toward new approaches when it gets stuck on the same error.
AgentGuard47 is runtime safety infrastructure for AI agents. It adds budget caps, loop detection, retry limits, timeouts, local traces, and incident reports so agents can stop bad runs while they are happening, not just explain them afterward.
An MCP server for detecting retry loops and analyzing iteration patterns in agentic coding workflows, providing structured debugging intelligence to improve repair attempts.
Temporal state machine for AI agents that detects and breaks repetitive error loops across languages by fingerprinting errors and injecting preventive constraints, running as an MCP server with SQLite backend.