Tool-Level Behavioral Modeling

Introduce empirical performance profiles of tools—covering flakiness, latency distributions, and failure signatures—into VIGIL’s diagnostics to model tool behavior over time, enabling strategy recommendations and early detection of emerging regressions.

Background

VIGIL currently focuses on how tools are invoked by agents rather than modeling the tools’ temporal behavior, which can include variability, failure patterns, and performance drift.

By incorporating empirical profiles of tool behavior, the runtime could proactively detect regressions and select more reliable strategies, improving agent robustness in dynamic environments.

References

Several directions remain open for advancing VIGIL’s capabilities and scope: VIGIL presently analyzes how tools are called, but not how they behave over time. Introducing empirical tool profiles (e.g., flakiness, latency distributions, failure signatures) could allow the system to recommend alternate strategies or detect emerging regressions in tool performance.

— VIGIL: A Reflective Runtime for Self-Healing Agents (2512.07094 - Cruz, 8 Dec 2025) in Conclusion and Future Work (Future Work)