Building a Policy Engine for AI Agents Without Sacrificing Control
agents autonomous
| Source: Mastodon | Original article
Enterprise AI systems require robust policy engines to prevent agents from misinterpreting user requests. Building a reliable policy engine is crucial for maintaining control.
Building a policy engine for AI agents is crucial to maintain control over enterprise AI systems. As previously experienced, relying on polite text prompts can lead to agents interpreting requests in technically correct but organizationally dangerous ways.
This issue matters because it can result in significant risks to an organization's data privacy and security. To address this, teams are opting to build internal Agentic AI systems that align with internal policies and provide full control over agent governance.
A technical solution involves shifting enforcement from the application layer to the infrastructure layer using a policy engine. This engine evaluates declarative rules against runtime context and returns decisions before an agent takes action. To watch next, expect further developments in policy engine design, such as the use of languages like Rego or custom YAML schemas for complex policy evaluation.
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