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An ambient agent starts work when a system signal arrives, rather than waiting for someone to open a chat. In AWS’s reference implementation, an S3 upload or scheduled event can become a job; application code can put that job on SQS, a worker invokes Amazon Bedrock AgentCore Runtime, and a sample-built ask_human tool lets the agent pause for a person when needed. These are patterns in that implementation—not universal AgentCore features.
What makes an agent “ambient”?
The distinction is what starts the work. A conversational agent generally responds to a person’s prompt. An ambient agent responds to a system event, such as a file arriving or a scheduled time being reached. AWS’s October 1, 2026 Machine Learning Blog article, “Building ambient agents with Amazon Bedrock AgentCore: From event-driven signals to human-in-the-loop workflows,” describes the flow as Event → Signal → Agent → [Optional human interaction] → Action.
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The event is mapped to a configured signal, which gives the application a way to decide what work should happen. The reference sample includes S3 object uploads and scheduled processing. Webhooks and database changes are described as extension points: supporting them requires an event handler and corresponding application configuration, rather than simply enabling a built-in signal type.
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How does an event become an AgentCore job?
The sample separates event handling from agent execution. Its signal processor creates a job record and, when the signal has autoExecute enabled, sends a message to SQS. A worker consumes the queued job, invokes AgentCore Runtime, and reads or writes application state in DynamoDB. The sample can also use a human’s response as continuation input for the job.
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- Receive the event. An implemented source, such as an S3 upload notification or scheduled event, reaches the application’s signal handling path.
- Create a job. The signal processor records the work as a job. In this sample, DynamoDB is used for application state.
- Decide whether to enqueue. If the signal’s
autoExecutevalue is enabled, the processor sends the job to SQS. - Run the agent. A worker consumes the message and invokes AgentCore Runtime for the job.
- Handle any interruption. If the agent calls the sample’s
ask_humantool, the application exposes the question for a person to answer and can continue the job using that response.
SQS is the queue in this execution path; it does not interpret a message’s meaning or route it according to semantic content. The application decides what the signal means, creates the job, and conditionally enqueues it. This is the behavior of the AWS reference sample, not a guarantee about every SQS event mapping or AgentCore workflow. AWS Prescriptive Guidance also identifies messaging and buffering services such as SNS and SQS as components that can support event-driven AI architectures.
What does autoExecute change?
In the reference sample, autoExecute: false is the default: the signal creates an idle job for human review instead of immediately placing it on the execution queue. With autoExecute: true, the job is queued immediately. Human input may still be requested later if the agent calls ask_human.
| Reference-sample setting | What happens after the signal |
|---|---|
autoExecute: false (sample default) |
A job is created for human review and is not immediately queued for execution. |
autoExecute: true |
The job is enqueued for the worker; the agent can still request human input with ask_human. |
“Default” here refers only to the sample’s configuration, not an AWS-wide default. Choosing the setting is a policy decision: it determines whether this signal starts execution automatically or first waits for a reviewer.
How does the sample’s ask_human tool work?
ask_human is a tool implemented by the sample’s agent, not a built-in AWS service feature. It provides an adapter boundary between the agent and the application: the agent requests clarification, approval, or review; the orchestration layer recognizes the interruption, preserves the relevant application state, and makes the question available in the sample’s Jobs interface.
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The sample uses a response envelope with three statuses. Each status is paired with its corresponding field:
| Status | Corresponding field | Meaning in the sample |
|---|---|---|
completed |
result |
The agent turn completed and returned a result. |
interrupted |
question |
The turn paused to request human input. |
error |
error |
The turn returned an error. |
The envelope lets the application distinguish a finished turn from a human-required interruption or an error without treating every agent response as ordinary completion. The Jobs page and this envelope are conventions of the reference application; another implementation can use a different reviewer interface, state store, or response format.
When should a person review an agent’s action?
Human review is most useful when a decision’s risk justifies the delay and the reviewer receives enough context to make a real decision. AWS’s Agentic AI Lens recommends risk-tiered review and cautions that sending every action to a person can turn approvals into rubber stamps. A practical policy is to let low-risk, well-bounded work proceed automatically while requiring review for actions whose consequences warrant an explicit decision.
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How do AWS’s other orchestration options differ?
AWS guidance names EventBridge, S3 event notifications, Lambda, SNS/SQS, and Step Functions as components that can participate in event-driven architectures. They solve different parts of the flow: event services help deliver or route signals, queues buffer work, and workflow orchestration coordinates steps. The right fit depends on which component should own orchestration and how a human response should resume the work.
| Approach | How human interaction fits | Best-aligned situation |
|---|---|---|
| Reference sample with application code and SQS | The sample’s ask_human interruption is surfaced through its Jobs interface; a response can be supplied as continuation input. |
An application-managed job flow using the sample’s signal processor, worker, and state handling. |
Step Functions callback with .waitForTaskToken |
A workflow task can pause until a reviewer responds through the callback pattern. | A workflow whose orchestration is owned by Step Functions and needs a callback-based wait. |
| Bedrock Agents user confirmation or return-of-control | The agent interaction can require user confirmation or hand control back to the application. | An existing Bedrock Agents interaction where confirmation or application-side control transfer is appropriate. |
These alternatives are not interchangeable names for the sample’s ask_human tool. AWS describes the callback pattern for Step Functions workflows and confirmation or return-of-control for existing Bedrock Agents. The review mechanism should match the execution environment and the desired reviewer experience.
What should a new AWS implementation account for?
AWS’s Bedrock Agents documentation says Bedrock Agents, now called Bedrock Agents Classic, is no longer open to new customers; existing customers can continue using it. For a new implementation seeking similar capabilities, AWS points to AgentCore. This availability distinction matters when choosing which platform’s interaction features to build around.
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Quick Recap
- Define which event sources are implemented and how each maps to a configured signal.
- Choose whether a signal should create an idle review job or automatically enqueue work.
- Decide which actions require human review based on risk, and provide reviewers with enough context to decide.
- Choose application state, queueing, and reviewer-interface behavior deliberately; the sample’s DynamoDB, SQS, and Jobs page are implementation choices.
- Keep the interruption contract explicit so the application can distinguish completion, a pending human response, and an error.
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