Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 28·8 min read
A Policy-Driven Runtime Layer for Agentic LLM Serving
TL;DR
Large language model (LLM) agents—systems that use LLMs to make decisions and take actions autonomously—lack runtime controls to enforce safety, cost, and performance policies during execution. A policy-driven runtime layer was built to intercept and govern agent behavior at runtime without modifying the underlying LLM or agent logic.
✦ Why It Matters
Engineers can now enforce safety and cost guardrails on LLM agents without retraining or redesigning the underlying models.
Key Takeaways
- Deploy policies to control agent behavior post-launch without retraining models.
- Intercept agent decisions at runtime to enforce budget, rate, and safety constraints.
- Reduce operational costs and latency through centralized policy-based governance.
- Enable dynamic policy updates without modifying agent code or LLM weights.
Full Summary
Agentic LLM systems—where language models autonomously plan and execute multi-step tasks—face challenges in production: unpredictable costs from repeated API calls, safety violations, and latency issues that existing static controls cannot address. A policy-driven runtime layer was developed as a middleware that sits between the agent and external services, intercepting decisions and enforcing declarative policies (rules specifying constraints like budget limits, rate limits, or forbidden actions) without requiring changes to the LLM or agent code.
The approach uses policy evaluation at decision points to approve, reject, or redirect agent actions in real time. Testing across multiple agent types and tasks showed reductions in token consumption and API costs, faster execution through intelligent caching and batching, and improved safety by blocking policy-violating actions before they execute.
This enables operators to govern agent behavior dynamically post-deployment, addressing a critical gap in production LLM systems.