Closing the AI agent trust gap with graduated autonomy

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How much to trust an AI agent is now a daily operational question. Agents read customer data, open tickets, process refunds, and delete accounts, yet most teams pick up a binary: full access or read-only. Full access is risky because agents fail unpredictably. Read-only leaves most of the agent’s value unused. The distance between what an agent could do and what an operator trusts it to do is the agent’s trust gap. In this post, we describe graduated autonomy, an architectural pattern that closes the gap. Agents earn expanded permissions through sustained reliability and lose them when performance degrades. Amazon Bedrock AgentCore, a platform to build, connect, and optimize agents at scale with any framework or model, provides the runtime, gateway, policy, and evaluation capabilities. Amazon DynamoDB stores trust state. AWS CodePipeline gates delivery on evaluation results. We cover each layer’s responsibility and the key design decision behind it. The agent trust gap Identity and access management answers “who can do what?” once, at provisioning. That model assumes that the principal behaves consistently. A large language model agent breaks it: the same agent can be accurate Monday and hallucinated Tuesday after a prompt change or model update. Closing the gap requires three capabilities raw API logs rarely provide: Visibility. API logs tell engineers what happened but tell a compliance officer nothing about whether an action was safe. Decision provenance. Tracing an action back to the signal that triggered it, the alternatives considered, and the confidence …

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