VIGIL: A Reflective Runtime for Self-Healing LLM Agents

LLM agent fleets fail in ways that per-session monitors cannot detect: behavioral drift, latency creep, and novel failure classes that emerge only across sessions. No infrastructure primitive exists for cross-session fleet health monitoring. Existing approaches either require pre-declared failure rules (AgentSpec), operate within single sessions (Wink), or trigger only on coarse SLA violations (ARM)---none accumulate behavioral evidence across sessions or detect novel failure classes. We present \textbf{V.I.G.I.L} (\textit{Verifiable Inspection and Guarded Iterative Learning}), a reflective runtime that occupies this maintenance layer. VIGIL is not a coding agent. It does not resolve issues, compete on task benchmarks, or produce repo-specific patches. VIGIL ingests structured behavioral logs, appraises each event via a deterministic appraisal engine, maintains a persistent \textit{Behavioral Signal Bank (EmoBank)} with exponential decay and coalescing policies, and derives a \textit{Roses/Buds/Thorns (RBT)} diagnosis mapping fleet behavior into strengths, opportunities, and systematic failures. Appraisal, decay, coalescing, threshold routing, and stage enforcement are all deterministic code. The LLM is used only for diff synthesis inside a governed runtime. Diagnostic reliability is architectural, not stochastic. We make two primary contributions. First, we identify and formalize the fleet-level agent maintenance problem, including a taxonomy of failure classes and the information-boundary constraints that determine what each class of system can detect. Second, we propose and evaluate VIGIL as a concrete instantiation of this primitive across three complementary settings. In a comparative fleet evaluation (20 agents $\times$ 7 episodes, 3 seeds), VIGIL is the only system to detect novel failure classes (episode 4); ARM misses drift for 5 episodes; AgentSpec and ARM achieve 0\% novel detection. False positive rate is 0\% across all four systems. Detection behavior is identical across all three seeds, confirming that VIGIL's diagnostic properties are architectural rather than stochastic. In a real-trajectory evaluation on 100 Nebius SWE-agent instances, Claude-as-judge correctness scores 4.20/5. Execution-based verification confirms 100/100 (100\%) of VIGIL-generated diffs are syntactically valid Python and execute successfully in a clean environment across all 100 distinct repositories. A cross-model validation study reveals GPT-4o self-inflation of $+1.05$ points ($t=34.0$, $p < 0.0001$). A longitudinal ablation confirms 142 accumulated signal rows produce measurably richer remediations than 23 rows from the same instance.