Runtime Guardrails for AI Agents - Steer, Don't Block

Most agent guardrails do one thing: block. The agent hits a rule, the workflow stops, and the user has to intervene. Agent Control adds a second option: steer — the agent receives corrective guidance, self-corrects, and completes the task without human intervention. Agent guardrails today are binary — allow or deny. When an agent violates a policy, the typical response is to block the action and surface an error. This works for hard constraints (PCI compliance, regulatory blocks), but it creates friction for rules where the agent could fix the problem itself: adjust a parameter, redact sensitive data, or reformat an output. Agent Control is an open-source runtime control plane that introduces steer controls alongside traditional blocks. Steer controls return corrective guidance via Guide() — the agent retries with the fix applied and completes the task. Rules live on a server, not in code — update them via API or dashboard without redeploying your agent. This post shows how Agent Contr