AI Agent governance

In the rapidly evolving field of AI agents, traditional safeguards, designed for single large language model (LLM) requests, are proving inadequate for managing the complexities of agents that operate through multi-step execution chains. These agents autonomously chain model calls, invoke tools, and trigger external effects, which can lead to unauthorized access, runaway costs, and untraceable outputs. Effective governance for AI agents requires a comprehensive approach that includes execution control to manage decision-making, tool and action permissions to restrict unauthorized tool use, cost and resource governance to prevent overspending, and policy enforcement layers to ensure compliance at every action point. A centralized infrastructure layer, such as Portkey's AI Gateway, offers a robust solution by embedding governance controls directly into the request path, ensuring consistent application across agents without altering their code, and providing a unified view of agent activity.

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