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Long-horizon agent frameworks and evaluation
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Three new arXiv papers address reliability and scalability challenges in long-horizon autonomous agent systems: Agent Mesh documents production failures in orchestrator retry logic, SKILL.state proposes a stateful runtime to replace append-only conversation histories, and PILOT introduces live self-improvement that redirects active runs rather than waiting for post-execution analysis.
- Agent Mesh analyzed 147 production incidents across 81 runs, finding all three service-mesh assumptions (retry, timeout, circuit breaking) violated in agentic systems
- SKILL.state replaces growing conversation history with mutable execution state to prevent latency degradation and context-poisoning failures over long horizons
- PILOT enables live self-improvement during execution to redirect active runs and validate lessons immediately, rather than only post-execution processing
- Group size effects and collective misalignment in LLM multi-agent systems published in PNAS on same date