Streamline Workflows with Amazon Bedrock Agents Return of Control

Introduction

Amazon Bedrock Agents can now “return control” to your application instead of invoking a Lambda function for every action. With RoC, the agent elicits parameters, hands them back to you in the InvokeAgent response, and then resumes orchestration after you post results from your app. It’s ideal when you already have integration code, need awkward network access in AWS Lambda, or must run long workflows.

What does “Return of Control” mean?

When an action group is configured for RoC, the agent doesn’t call Lambda. Instead, it returns:

• invocationInputs (the function/API name and fully elicited parameters), and
• invocationId (a correlation ID you must echo back).

Your backend performs the call or kicks off a workflow. To continue, you call InvokeAgent again and place the output in sessionState.returnControlInvocationResults along with the same invocationId. The agent uses that context to plan the next step and compose a natural response.

Configuration‑wise, you enable this by setting customControl: RETURN_CONTROL in the action group executor when creating or updating the action group.

When to Use RoC?

AWS highlights RoC for cases where it’s simpler to call existing systems from your app (rather than wrapping them in a new AWS Lambda) and for tasks that exceed AWS Lambda’s single‑invocation limit. Skipping AWS Lambda also helps when the API you need sits behind VPC‑specific networking, and your application already handles

AWS Lambda’s maximum execution time is 900 seconds (15 minutes). If your action might take longer, say, waiting on approvals, then RoC lets you offload the work to Step Functions or containers and then pass the result back to the agent.

Use Case: P2P Invoice Exception Handling (Three Way Match)

In procure-to-pay workflows, every supplier invoice must be validated against the purchase order (PO) and the goods receipt note (GRN), a process called the three-way match. Exceptions arise when mismatches occur in quantity, price, tax codes, or duplicate submissions. For example, a PO may specify 1,000 units at $5 each, but the GRN logs 980 received, while the invoice bills 1,000 at $5.10.

Such discrepancies trigger exception handling that requires investigation and resolution. Accounts Payable (AP) clerks are the first line of triage, but resolution often involves buyers, warehouse teams, suppliers, and finance controllers. Tolerances may allow minor variances, but larger discrepancies must go through policy checks and multi-step approvals.

The challenges include high transaction volumes, manual interventions, and resolution times that can stretch over days. This delays payments, increases compliance risks, and may result in lost early-payment discounts or overpayments if not properly controlled.

Automating this process through conversational AI and return of control logic can drastically reduce cycle times, enforce policy compliance, improve auditability, and scale exception handling across thousands of monthly invoices.

How does RoC help?

1. User asks: “Why did invoice INV‑12345 fail?”
2. Agent plans & elicits: It gathers needed fields (invoice ID, PO, GRN, thresholds) and selects ResolveInvoiceException: an action group configured with RoC, so the parameters are returned to your app

1. Your app takes over: It calls the ERP/finance APIs, runs a three-way match, applies variance tolerances, and decides the path: auto‑resolve (if within policy) or start an AWS Step Functions workflow for approvals.
2. Resume the conversation: When results are ready (immediately or after the workflow completes), your app calls InvokeAgent with sessionState.returnControlInvocationResults and the original invocationId. The agent updates the user: “It’s a 2% price variance; I triggered Level‑1 buyer approval and will confirm when it’s cleared.”

Why does this fit RoC?

1. No forced AWS Lambda wrappers. Reuse your existing integration layer and credentials.
2. Long-running steps. Approvals and supplier follow-ups easily exceed 15 minutes; AWS Step Functions can pause for human-in-the-loop decisions and continue when callbacks arrive.

Implementation Blueprint

1. Define the action group with concise parameters (e.g., invoiceId, poNumber, grnNumber, variancePolicyId). Choose function details or OpenAPI depending on your governance.
2. Enable RoC by setting customControl: RETURN_CONTROL in the action group executor (CreateAgentActionGroup or update equivalent).
3. Handle the first InvokeAgent response: Parse returnControl.invocationInputs and invocationId, then call your ERP or start a state machine.
4. Return results with sessionState.returnControlInvocationResults (function or API result), echoing the same invocationId. The agent continues planning with this new context.
5. Design for long-running work: For multi-hour approvals, rely on Step Functions’ human‑approval pattern (email, callback token, or integration), then post the outcome back to the agent when complete.
6. Operationalize: Validate inputs, apply least‑privilege AWS IAM, add rate limits, and monitor action latency and success rates. (See the AWS blog’s RoC performance, security, and observability considerations.)

Conclusion

At the same time, your back‑office logic does the heavy lifting, three-way match, approvals, and supplier notifications via the most appropriate compute and workflow tools. The net result is faster cycle times, fewer manual hops, and an auditable, conversational trail your stakeholders can understand.

Drop a query if you have any questions regarding Amazon Bedrock Agents and we will get back to you quickly.

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