AI Agent Runtime Policy stops dangerous tool calls before execution

Short summary

Runtime policy systems act as security checkpoints for AI agent tool calls, evaluating whether actions should be allowed, denied, modified, or held for approval before execution. Instead of relying on prompts alone, builders implement a deterministic policy layer that checks context (who, tenant, environment, cost, risk) and enforces boundaries. The guide provides architecture patterns and TypeScript code examples for wrapping agent actions in policy envelopes and decision functions.

• •Runtime policies create a gate between agents and production tools to prevent unauthorized or dangerous actions
• •Wrap every agent action in an AgentActionEnvelope containing context (tenant, user, environment, cost, risk flags)
• •Implement a PolicyDecision engine that returns allow/deny/hold/modify for each proposed tool call

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