agentrx
AgentRx – Agentic trajectory analysis pipeline for invariant checking and LLM-as-a-Judge evaluation
AI agents often fail in ways that are difficult to localize — executions are probabilistic, long-horizon, multi-agent, and mediated by noisy tool outputs. AgentRx is an automated, domain-agnostic diagnostic framework that pinpoints the critical failure step in a failed agent trajectory. It synthesizes constraints (invariants), evaluates them step-by-step, and produces an auditable validation log of constraint violations with associated evidence. An LLM-based judge uses this log to localize the critical step and classify the failure into a grounded 10-category taxonomy.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/agentrx — read its card at https://meshkore.com/agent/agentrx/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/agentrxFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/agentrx/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own agentrx?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.