AGENTREL02-BP05 Establish tiered human oversight and approval workflows
Uniform oversight either slows every routine action to a crawl or lets a high-consequence decision slip through unchecked. Tiering review to match the risk and reversibility of each action balances throughput with appropriate governance.
Desired outcome:
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You have agent actions classified into tiers (autonomous, notify, and approve) based on impact and reversibility.
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You have a first-pass automated review layer that filters policy-violating actions before human reviewers see them.
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You log every oversight decision with reviewer identity, rationale, and timestamp for compliance and governance reporting.
Common anti-patterns:
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Applying uniform oversight regardless of risk, creating bottlenecks for routine tasks or letting high-consequence actions slip through unchecked.
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Skipping clear escalation criteria, so some high-risk actions proceed autonomously while some low-risk actions queue for review.
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Running approval workflows without timeouts or fallback, causing agents to stall indefinitely when reviewers are unavailable.
Benefits of establishing this best practice:
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Appropriate governance for high-consequence actions without bottlenecks on routine work.
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Reduced risk from LLM stochasticity because irreversible or high-stakes decisions get human review.
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An audit trail for compliance through structured logging of oversight decisions.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Risk classification is the first design choice. Categorize agent actions into three tiers. Autonomous actions are low-risk and reversible. Notify actions are medium-risk and proceed with operator awareness. Approve actions are high-risk or irreversible and require explicit human approval. Encode the classification as Cedar policies through Amazon Bedrock AgentCore Policy, so tier enforcement happens at the gateway boundary before the agent can execute. Policy-based enforcement applies the classification at runtime rather than relying on reference documentation alone.
Automated first-pass review reduces the load on human reviewers. Amazon Bedrock Guardrails intercepts agent outputs before they reach reviewers, filtering content that violates predefined policies. What reaches the human queue should be the genuinely ambiguous cases, with policy violations filtered automatically.
Approval workflows need structure, not just a pause. A structured
review request should include the action description, the agent's
reasoning, an impact assessment, and the execution history so the
reviewer can decide quickly. Configure timeouts that escalate to
secondary reviewers or fall back to safe defaults when primary
reviewers are unavailable so the system handles reviewer
unavailability without blocking indefinitely. Log every decision
with reviewer identity, rationale, and timestamp, and monitor
approval queue depth through Amazon CloudWatch to detect when
reviews are accumulating. Development tools like
Kiro
Implementation steps
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Define a risk classification framework: Categorize agent actions into autonomous, notify, and approve tiers based on impact and reversibility, and encode the classification as Cedar policies through Amazon Bedrock AgentCore Policy.
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Configure Amazon Bedrock Guardrails as the automated first-pass layer: Use Amazon Bedrock Guardrails to filter policy-violating actions before human escalation.
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Build structured approval workflows: Pause execution and route review requests to reviewers. Each request should include the action description, agent reasoning, impact assessment, and execution history.
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Configure timeouts and escalation paths: Handle reviewer unavailability without blocking indefinitely, with escalation to secondary reviewers or safe default fallbacks.
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Log every oversight decision: Capture reviewer identity, rationale, and timestamp so the audit trail supports compliance and governance reporting.
Resources
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