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Stacksona Runtime Governance
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Runtime governance for AI agents

Runtime governance for AI agents puts a control point between an agent plan and real-world execution. Instead of discovering damage in logs later, teams evaluate intent before side effects happen.

What teams need to get right

  • Evaluate each proposed tool call against policy, risk, and business context.
  • Route only high-impact actions to reviewers while allowing low-risk automation to continue.
  • Return a deterministic allow, deny, or require-approval decision back to the agent runtime.

How Stacksona helps

  • Policy engine that maps action type, data sensitivity, and environment to enforcement rules.
  • Approval routing with SLA timers, escalation paths, and full reviewer context.
  • Tamper-evident decision records for compliance and post-incident analysis.

Why this matters now

As agent deployments move from prototypes to customer and operational workflows, governance needs to be embedded in execution paths. Teams that rely only on after-the-fact monitoring often discover risk too late.

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