AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP

2026-07-14 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

AgentCheck is an open-source web workbench designed to reproduce, intervene on, and mitigate failures in tool-using LLM agents operating over a Model Context Protocol (MCP) server. It addresses the critical gap where agents are typically evaluated assuming perfect tool functionality. AgentCheck records an agent's interaction with real tools, then re-runs it with one of 12 fault types injected into a tool response, enabling a controlled comparison. This system allows developers to apply mitigations, re-run against the identical fault, and confirm if the fix worked. Evaluation across five agents and 120 scenarios revealed silent data-quality faults as a dominant weakness, with the weakest agent scoring only 77/120. For instance, a retry mitigation improved success on timeout errors from 30% to 100% for one agent.

Key takeaway

For MLOps Engineers deploying tool-using LLM agents, you must proactively test agent resilience to real-world tool failures. Use AgentCheck to reproduce specific fault types like stale data or timeouts, apply your proposed mitigations, and deterministically verify their effectiveness. This ensures your agents handle degraded tool performance gracefully, preventing silent failures and fabricated responses in production environments.

Key insights

LLM agents require controlled fault injection and fix verification to ensure robustness against real-world tool failures.

Principles

• Controlled comparison isolates fault impact.
• Fault injection reveals silent failures.
• Deterministic verification confirms fixes.

Method

AgentCheck acts as an MCP proxy, recording clean runs, then replaying with one of 12 fault types injected into a tool response. It compares trajectories and scores outcomes deterministically, confirming mitigation effectiveness.

In practice

• Test agents against 120 pre-defined scenarios.
• Profile agent vulnerability to specific faults.
• Verify mitigation strategies before deployment.

Topics

• LLM Agents
• Tool-Using Agents
• Fault Injection
• Model Context Protocol
• Agent Evaluation
• Mitigation Strategies
• Data Quality Faults

Code references

• aritra741/AgentCheck

Best for: AI Architect, AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.