Story · Semantic Scholar
ReliHarness: A Self-Learning Reliability Harness for LLM Agent Tool Execution (Semantic Scholar)
paper · Story page
MCP task states are coarse and self-reported, so a worker thread that deadlocks keeps showing up as working. ReliHarness probes the thread itself with a heartbeat timeout and an operating-system liveness check, reporting 100% detection coverage and zero false positives on the crash and deadlock scenarios tested.
In plain words
- Researchers built ReliHarness to detect when software used by artificial intelligence (AI) stops working.
- A task can still appear to be running after the software carrying it out has frozen or stopped.
- ReliHarness checks for regular signals from that software and asks the computer whether it is still running.
- It detected every tested failure without wrongly flagging working software.
- For people overseeing long tasks, these checks can reveal failures that ordinary progress reports miss.
Appeared in
- Giving an agent the decoder's confidence never beat a plain deterministic gate
Sep 21, 2026 · in the sections
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