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Runtime containment kernel for LLM agents. Enforces budget, step, retry, and circuit-breaker limits before the model call.

5 starsPython

Preventing runaway LLM agents (enforcement layer)

by amabito·Feb 17, 2026·1 point·2 comments
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AI Analysis

●●SolidNiche GemBig Brain
The Take

VERONICA puts an enforcement shim between your agent and the model so you can halt costly spirals before a request hits the provider — it natively exposes hard budget enforcement, circuit breakers, retry containment and degradation levels. The README + runnable runaway-loop demo make the failure mode concrete and the API (BudgetEnforcer, RuntimeContext, BudgetExceeded) is small and practical. I'd like to see richer observability/adapter docs for common agent frameworks, but as an enforcement-first primitive this is a clever, useful tool.

Target Audience

Backend developers, ML engineers, SREs and teams deploying autonomous LLM agents in production

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The demo implements post-generation admissibility checks and returns structured refusals (decision codes, rule triggered, divergence metrics and a stable prompt fingerprint) so you can audit enforcement decisions. It's a crisp, focused proof-of-concept for runtime enforcement — useful as a starting pattern — but it stops short of addressing bypass/adversarial vectors, deployment integration, or guarantees that make it enforceable at scale.

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