Abstract
Autonomous agents in complex workflows can be unreliable, potentially failing due to semantic errors, for example, incorrect tool usage, model hallucinations, or malformed outputs, which may not be addressed by some system-level fault tolerance methods. A resilient agent framework can introduce self-correction loops at the individual agent-execution level. The framework may operate by intercepting and validating an agent's actions; if a failure is detected, it can provide structured, context-aware feedback, enabling the agent to use in-context learning to self-correct and retry its immediate task. This approach improves the reliability of autonomous agents by helping to resolve semantic errors, reducing a need for full (workflow) retries and mitigating performance degradation from context bloat in long (running) processes.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Kochera, Michael, "Framework for Autonomous Agent Resilience via Self-Correcting Execution Loops", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10006