The Tool Desk
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Check whether these instructions apply to your account
AWS documentation now refers to the original Amazon Bedrock Agents service as Agents Classic. It is no longer open to new customers, although existing customers can continue using it. AWS directs readers seeking comparable capabilities to Amazon Bedrock AgentCore. That status determines whether the console labels and API workflow below are available to you, so verify the service and region in the current AWS documentation before designing a new deployment. See AWS’s action-group documentation and the Agents overview.
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For an existing Agents Classic deployment, the settings that most directly limit what an agent can do are its action groups, the state of each action group, the function-level confirmation requirement, and the IAM policies attached to the agent role and tool executors. The user-input setting determines whether the agent must ask for missing arguments instead of guessing. A Guardrail adds response and message safeguards, but it is not an authorization boundary.
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Think of an agent as a planner that can propose calls to a defined set of operations. Safety improves when each layer has a distinct job:
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| Layer | What it controls | What it does not do |
|---|---|---|
| Action groups and schemas | Which functions or API operations the model may propose, and which parameters are required | It does not grant AWS permissions by itself |
| Action-group state | Whether a configured group is allowed to invoke | It does not redesign the function schema |
| Execution mode | Whether Lambda executes the operation or your application receives the proposal | It does not automatically approve a risky call |
| Function confirmation | Whether the end user must approve a particular invocation | It does not limit unrelated functions that remain exposed |
| User input | Whether the agent asks for missing required values | It does not validate that a supplied value is authorized |
| IAM and Lambda policies | Which AWS resources the agent role and executor can actually access | They do not explain an ambiguous request to the model |
| Guardrail | Configured policy coverage for user messages and model responses | It does not replace least-privilege permissions or explicit consent |
Configure these controls together. A confirmation prompt cannot compensate for an action group that exposes dozens of unnecessary destructive operations, and a Guardrail cannot compensate for an over-permissioned Lambda function.
1. Minimize the action surface with action groups
Action groups define the actions an agent can perform. In the console or API, you can describe functions individually or supply an OpenAPI schema, then choose how each group is fulfilled. Start with the smallest useful set: for example, a read-only order-status function rather than a broad “orders” API that also cancels, refunds, and edits addresses.
Define only necessary functions and operations
- Expose one function for each meaningful operation instead of a catch-all function with loosely interpreted parameters.
- Describe required and optional parameters accurately. A field that is truly required should be marked required in the function details or schema.
- Use descriptions that state scope and constraints, such as which account, region, or resource identifier is valid.
- Disable or remove groups that are not part of the current workflow. The API’s
actionGroupStatefield can allow or disallow invocation.
These definitions shape what the model can select, but they are not an IAM boundary. The service role and the code that executes a function still need independent permission checks.
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Choose who executes the proposed action
A group can be fulfilled by a Lambda function, or you can return control to your application. With Lambda fulfillment, Bedrock invokes the configured function and the function performs the downstream work under its own resource-based policy and execution role. With return control, the InvokeAgent response contains the predicted action and parameters; your application inspects, authorizes, logs, and executes it—or declines it—before calling the downstream service.
| Fulfillment choice | Execution control | Typical use | Main risk to manage |
|---|---|---|---|
| Lambda | Bedrock hands the call to your configured function | Stable, well-tested operations with centralized executor logic | Function permissions and input validation must be tightly scoped |
| Return control to application | Your application receives the proposal and decides whether to execute | High-impact workflows needing custom authorization, auditing, or transaction checks | Application must reliably reject unsafe or malformed proposals |
A return-control design is useful when the application must display a review screen, apply business rules, or require a second approval system. It does not make the proposal trustworthy by itself; treat every returned parameter as untrusted model output.
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Follow AWS’s current procedure for adding a group and preparing changes in Add an action group to your agent in Amazon Bedrock and Use action groups to define actions for your agent.
2. Require confirmation for consequential functions
For function details, set requireConfirmation to ENABLED or DISABLED. The console exposes the same choice as a user-confirmation setting. Enable it for operations that create commitments or are difficult to reverse: deleting data, submitting an order, issuing a refund, changing access, or sending an external message. Read-only lookups generally do not need an interruption, but the decision belongs to your threat model and user experience.
- Open the agent’s action group and edit the specific function, not just the group description.
- Set the confirmation option to enabled for the high-impact function.
- Save and prepare the agent version.
- Exercise the path through
InvokeAgent. When confirmation is required, the proposed invocation and its inputs are returned so the end user can confirm or deny before execution. - Record the decision and the exact parameters in your application’s audit log.
AWS explains the security rationale directly: “Requesting confirmation before invoking the function may safeguard your application from taking actions due to malicious prompt injections.” Read the full workflow in Get user confirmation before invoking action group function.
Confirmation is an approval gate, not a substitute for validation. Show users the operation and material inputs in plain language, reject values that violate business rules, and avoid presenting a vague prompt such as “Continue?” when the call would delete or transfer something.
3. Keep user input enabled when required values may be missing
Enable the special AMAZON.UserInput action group when the agent may need to ask for information before it can construct a valid call. In the console, this is the User input setting; through the API, configure the corresponding action-group option. The agent can then reprompt for details such as an account identifier, date range, destination, or confirmation of an ambiguous resource.
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Disabling this setting removes that conversational recovery path. AWS warns: “If you disable user input, the agent doesn’t request the user for additional details. if it needs to invoke an API in an action group, but doesn’t have enough information to complete the API request.” In that situation, a model may insert defaults or make a best-guess call, potentially hallucinating the invocation. See Enable user input in Amazon Bedrock and Disable user input in Amazon Bedrock.
When disabling input can be acceptable
Only disable user input when your application guarantees that every required argument is supplied and validated before the agent reaches the action. For example, a tightly constrained internal workflow might inject a server-side account ID and fixed region. Even then, validate the final parameters in the executor; do not rely on the model to honor an implied default.
4. Bound effective access with IAM and Lambda policies
The service role determines what the agent can access in AWS. Create a trust relationship for the Bedrock service and attach identity-based permissions for the resources used by the configured features. The exact permissions depend on whether the agent uses models, action schemas, knowledge bases, Guardrails, customer-managed KMS keys, collaborators, or provisioned throughput. Grant only the permissions for features actually enabled, and scope resource ARNs, regions, and actions wherever AWS supports it.
Each action-group Lambda also needs a resource-based policy that permits the Bedrock service to invoke that function. Keep the function’s execution role separate and least-privileged: it should be able to perform only the downstream operations represented by that function, not every operation in the account. Validate identifiers and authorization again inside the function, because an agent can produce syntactically valid but unauthorized parameters.
A practical permission review asks:
- Can the agent role access only the model and AWS resources this agent uses?
- Does a knowledge-base, KMS, Guardrail, or collaborator permission exist only when that feature is configured?
- Can the Lambda resource policy be invoked by the intended Bedrock service and agent context, rather than any principal?
- Does the Lambda execution role prevent destructive operations that the action schema does not need?
- Are CloudTrail and application logs sufficient to connect a user request, confirmation decision, proposed parameters, and resulting AWS API call?
Use Create a service role for Amazon Bedrock Agents as the permission reference, then recheck policies whenever an action group or feature changes.
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5. Add a Guardrail for content safeguards, not authorization
When creating or configuring an agent, you can associate a Guardrail and a specific version. AWS describes Guardrails as safeguards for unwanted behavior in model responses and user messages. They can complement policies for harmful or disallowed content, but the Guardrail documentation does not describe them as a replacement for action definitions, IAM boundaries, input validation, or explicit confirmation.
Choose a Guardrail policy and version that match the application’s content requirements, then test both blocked and allowed conversations. A Guardrail may stop a response or message while still leaving an over-broad action group and over-permissioned Lambda untouched. Conversely, a narrowly scoped action may still need a Guardrail to prevent unsafe instructions or responses around that action. Configuration details are in Create and configure agent manually and Prerequisites for creating Amazon Bedrock Agents.
6. Save, prepare, and test the complete path
Console edits are not the same as a tested, prepared agent version. After changing action groups, confirmation, user input, or Guardrail association, save and prepare the configuration before invoking it. AWS includes this step in its setup procedures.
- List every action group and function that should be callable in this version.
- Check each group’s state and remove or disable unused operations.
- Verify required parameters and descriptions against the executor’s actual validation rules.
- Confirm that high-impact functions require confirmation and that the user-input setting matches your ability to provide complete arguments.
- Review the service role, Lambda resource policy, and Lambda execution role for least privilege.
- Confirm the intended Guardrail and version.
- Save and prepare the agent, then run representative
InvokeAgentconversations. - Test missing values, malicious or conflicting instructions, denied confirmations, malformed identifiers, and attempts to call disabled operations.
For return-control groups, verify that your application rejects proposals when authorization, parameter validation, or approval fails. For Lambda groups, verify that the function fails closed and does not silently substitute a default resource or account.
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Decision guide for common designs
| Situation | Recommended configuration | Reason |
|---|---|---|
| Read-only status lookup | One narrowly defined function, read-only IAM, user input enabled if identifiers can be absent; confirmation based on risk | Limits blast radius while allowing the agent to request a missing identifier |
| Delete, refund, purchase, or permission change | Dedicated function, least-privilege IAM, confirmation enabled; consider return control for application approval | Separates planning from explicit consent and business-rule checks |
| Workflow with all values supplied by a trusted backend | Server-side validation, tightly scoped action group, user input may be disabled only if completeness is guaranteed | Avoids unnecessary reprompts without permitting guesses |
| Several experimental tools | Keep unused action groups disabled; use a separate test agent or version | Prevents accidental invocation while schemas and permissions change |
| High-risk content domain | All of the above plus an appropriate Guardrail and version | Content safeguards address model messages in addition to authorization controls |
Troubleshooting unsafe or unexpected behavior
The agent calls an operation it should not know about
Inspect every active action group and its schema, including operations inherited from an older prepared version. Disable the group or function, prepare a new version, and confirm that the application is invoking that version.
The agent invents a missing identifier
Enable AMAZON.UserInput, mark the parameter required, and make the executor reject blank, synthetic, or unauthorized identifiers. If your architecture intentionally disables user input, supply and validate the value before invocation rather than relying on a model default.
A confirmation prompt appears, but the action still fails or is unsafe
Confirmation only records user consent to the proposed call. Check the function’s parameter validation, IAM permissions, resource ownership checks, and downstream transaction safeguards. For stronger control, return the proposal to your application and require an independent authorization decision.
A legitimate call is denied
Compare the action schema with the service role’s identity policy, the Lambda resource-based policy, and the Lambda execution role. Also check whether a Guardrail blocked the message or response. Grant only the missing permission required for the configured feature, then prepare and retest.
Bottom line
Limit an Amazon Bedrock agent’s tool access first by minimizing action groups, disabling unused groups, and selecting the smallest execution surface. Enforce the real boundary with least-privilege IAM and Lambda policies. Keep user input enabled when required arguments can be absent, and require per-function confirmation before consequential operations. Add a versioned Guardrail for content safety, then save, prepare, and test failure paths. If you are starting a new project, verify whether Agents Classic is available to your account and evaluate AWS’s AgentCore direction before following this workflow.
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