VIGIL: A Reflective Runtime for Self-Healing Agents

TL;DR

VIGIL introduces a reflective runtime system for self-healing agents that monitors, diagnoses, and repairs agent behavior autonomously, enhancing reliability and robustness in agentic LLM frameworks.

Contribution

The paper presents VIGIL, a novel runtime that enables self-diagnosis and autonomous repair of agentic LLMs through structured logs, emotional appraisal, and guarded prompt/code updates.

Findings

VIGIL successfully identified and repaired latency issues in a case study.

The system produced explicit error reports when internal diagnostics failed.

VIGIL demonstrated effective self-repair capabilities in a deployed environment.

Abstract

Agentic LLM frameworks promise autonomous behavior via task decomposition, tool use, and iterative planning, but most deployed systems remain brittle. They lack runtime introspection, cannot diagnose their own failure modes, and do not improve over time without human intervention. In practice, many agent stacks degrade into decorated chains of LLM calls with no structural mechanisms for reliability. We present VIGIL (Verifiable Inspection and Guarded Iterative Learning), a reflective runtime that supervises a sibling agent and performs autonomous maintenance rather than task execution. VIGIL ingests behavioral logs, appraises each event into a structured emotional representation, maintains a persistent EmoBank with decay and contextual policies, and derives an RBT diagnosis that sorts recent behavior into strengths, opportunities, and failures. From this analysis, VIGIL generates both guarded prompt updates that preserve core identity semantics and read only code proposals produced by a strategy engine that operates on log evidence and code hotspots. VIGIL functions as a state gated pipeline. Illegal transitions produce explicit errors rather than allowing the LLM to improvise. In a reminder latency case study, VIGIL identified elevated lag, proposed prompt and code repairs, and when its own diagnostic tool failed due to a schema conflict, it surfaced the internal error, produced a fallback diagnosis, and emitted a repair plan. This demonstrates meta level self repair in a deployed agent runtime.