The Tool Desk
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What is Amazon Bedrock Agents Classic?
Agents Classic is a managed orchestration layer for building AI agents with Amazon Bedrock. It uses a foundation model to interpret a request, break the work into steps, and coordinate a conversation with the user, calls to APIs, and retrieval from connected data sources.
An agent can ask a user for missing information before proceeding, call an API to perform or look up an action, and use a Knowledge Base to retrieve relevant information. A typical configuration gives the agent a foundation model and instructions, then connects one or more action groups, Knowledge Bases, or both.
How do action groups and Knowledge Bases work?
They serve different purposes: an action group lets an agent carry out or request actions through APIs, while a Knowledge Base lets it retrieve information from connected data.
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| Component | What it does | Typical role in an agent |
|---|---|---|
| Action group | Describes executable actions, parameters the agent may need to collect, APIs it may call, and how fulfillment and results are handled. | Interact with an application or company system—for example, by looking up information or initiating an API-backed action. |
| Knowledge Base | Retrieves relevant material from a connected data store for retrieval-augmented generation. | Ground a response in retrieved content, which can include source quotations or a generated answer based on that material. |
An agent can use either component or combine them. Retrieval does not perform the API actions defined by an action group, and an action group is not the same thing as a source of reference material.
What models does Amazon Bedrock Agents Classic support?
AWS documentation says Agents Classic supports all Amazon Bedrock foundation models, but support does not mean every model is optimized for the agent architecture. For optimized models, AWS has tuned the prompts and parsers used in orchestration. An unoptimized model may require prompt or parser overrides.
AWS says it does not plan to optimize additional models for Agents Classic. Its model catalog is frozen, and actual model availability still depends on the model and AWS Region. For new agent work, consider model and provider flexibility in AgentCore rather than assuming the Classic catalog will expand.
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Can I still create a Bedrock Agent?
AWS closed Agents Classic to new customers on July 30, 2026. Existing users can continue only if AWS determines their accounts qualify based on Agents activity in the preceding 12 months. AWS says there is no exception process for a new account; qualifying users retain access to existing APIs.
AWS has not announced a planned end-of-life date for Agents Classic. That does not change the new-customer restriction or the frozen model catalog. AWS recommends evaluating AgentCore for new development and future migration.
Should I use AgentCore or migrate from Agents Classic?
AWS describes two AgentCore approaches: a managed harness intended to be the closest analogue to the managed Agents Classic experience, and code-defined agents running on AgentCore infrastructure for more customized orchestration. Which fits depends on the existing design and how much control the team needs over orchestration and operations.
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AWS says straightforward workloads built around a model, action groups, and Knowledge Bases may be migrated in hours; more complex deployments require more work. Treat that as AWS guidance, not a schedule for a particular migration. Inventory the components and dependencies before estimating effort:
- Which foundation models, prompts, and parser settings the agent uses.
- What each action group calls, what parameters it collects, and how fulfillment is implemented.
- Which Knowledge Bases and data sources are connected.
- Whether custom orchestration, memory, logging, identity, or other operational controls need equivalents in the target design.
How much does Amazon Bedrock Agents Classic cost?
AWS says, “There is no charge for Bedrock Agents Classic itself.” That means there is no separate orchestration charge; it does not mean an agent deployment is free. Charges can come from model inference and associated resources, including Knowledge Bases and Lambda invocations. Provisioned capacity or other resources in a particular design can also affect the bill.
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- The selected model and Region.
- Request volume and token usage.
- Knowledge Base and retrieval configuration.
- API and Lambda usage.
- Any provisioned capacity or other attached resources.
AWS says AgentCore’s managed harness may yield comparable or lower inference costs, but actual costs vary by workload. That is not a guarantee that migrating will reduce total spend.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does Amazon Bedrock use my data to train models?
The documented encryption control does not answer every question about prompts, retention, model-provider sharing, or logging. Do not treat encryption at rest as proof that prompts are never retained, reviewed, or used in any processing. Those details can depend on the chosen model and features, the applicable model terms and AWS service policies, invocation logging, and connected resources.
Before deployment, review the current terms and settings for the specific model and configuration, including logging and any connected services. The available encryption documentation establishes how agent resource information is protected at rest, not a universal data-use or retention guarantee.
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How is agent information encrypted, and how does memory affect data handling?
AWS says control-plane and session information for agent resources is encrypted at rest by default with AWS-owned keys. Customers cannot view, manage, or audit those keys. A customer can instead choose a symmetric customer-managed AWS KMS key; AWS KMS charges apply to that option.
Agent memory is optional. If enabled, it can preserve conversational context across sessions: memory is associated with a memory ID, can be loaded on later invocations using that same ID, and has a configurable retention duration. Decide whether the application needs cross-session context and set its retention intentionally as part of the data lifecycle design.
Quick Recap
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