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Approval workflows for AI agents

AI agent approval workflows define exactly when humans are required, who must review, and how an action resolves if nobody responds in time.

What teams need to get right

Set thresholds by operation, system, data class, and confidence score.
Use tiered approvers for financial, customer, and production-changing actions.
Design explicit timeout and fallback behavior so agents stay predictable under load.

How Stacksona helps

Visual policy-to-workflow mapping for approval paths and escalation.
Real-time decision inbox with structured evidence, not raw logs.
Deterministic outcomes that feed directly back into agent orchestration.

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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