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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.03409 |
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| _version_ | 1866910227833552896 |
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| author | Perera, Srinath Hapuarachchi, Kaviru Leymann, Frank Khalaf, Rania |
| author_facet | Perera, Srinath Hapuarachchi, Kaviru Leymann, Frank Khalaf, Rania |
| contents | We present Robust Agent Compensation (RAC), a log-based recovery paradigm (providing a safety net) implemented through an architectural extension that can be applied to most Agent frameworks to support reliable executions (avoiding unintended side effects). Users can choose to enable RAC without changing their current agent code (e.g., LangGraph agents). The proposed approach can be implemented in most existing agent frameworks via their existing extension points. We present an implementation based on LangChain, demonstrate its viability through the $τ$-bench and REALM-Bench, and show that when solving complex problems, RAC is 1.5-8X or more better in both latency and token economy compared to state-of-the-art LLM-based recovery approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03409 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust Agent Compensation (RAC): Teaching AI Agents to Compensate Perera, Srinath Hapuarachchi, Kaviru Leymann, Frank Khalaf, Rania Artificial Intelligence D.2; I.2.1 We present Robust Agent Compensation (RAC), a log-based recovery paradigm (providing a safety net) implemented through an architectural extension that can be applied to most Agent frameworks to support reliable executions (avoiding unintended side effects). Users can choose to enable RAC without changing their current agent code (e.g., LangGraph agents). The proposed approach can be implemented in most existing agent frameworks via their existing extension points. We present an implementation based on LangChain, demonstrate its viability through the $τ$-bench and REALM-Bench, and show that when solving complex problems, RAC is 1.5-8X or more better in both latency and token economy compared to state-of-the-art LLM-based recovery approaches. |
| title | Robust Agent Compensation (RAC): Teaching AI Agents to Compensate |
| topic | Artificial Intelligence D.2; I.2.1 |
| url | https://arxiv.org/abs/2605.03409 |