Varpulis: Real-time Behavioral Guardrails for AI Agents

Varpulis introduces a novel approach to monitoring AI agent behavior by providing real-time guardrails. Unlike existing observability tools that analyze traces post-hoc or static guardrails that validate individual inputs/outputs, Varpulis detects behavioral patterns as they unfold. This allows for immediate intervention and prevention of costly errors.

The system is powered by the Varpulis CEP engine, which uses NFA-based pattern matching with Kleene closure and Zero-suppressed Decision Diagrams (ZDD) for efficient combinatorial matching. It offers pre-packaged patterns for detecting retry storms, circular reasoning, budget runaways, error spirals, stuck agents, and token velocity spikes. These patterns are configurable, allowing users to customize the monitoring to their specific needs.

Varpulis integrates with popular AI agent frameworks like LangChain, MCP, and OpenAI Agents SDK, and can be deployed in-process via WASM (JS) or native extension (Python). This makes it easy to integrate into existing AI agent workflows without requiring significant infrastructure changes. The ability to detect temporal patterns, which are often missed by existing tools, is a key advantage of Varpulis. This can help organizations avoid costly errors and ensure that their AI agents are operating as intended.

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