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Start by defining the job, success criteria, permissions, grounding, safeguards, and operating metrics before selecting an Amazon Bedrock agent design. One important service change affects that decision: AWS says Amazon Bedrock Agents Classic is no longer open to new customers as of July 30, 2026. Existing customers can continue using it, while AWS recommends Amazon Bedrock AgentCore for new development and migration. The durable engineering principles remain the same, but implementation details and feature availability should be checked for your target Region.
1. Define the purpose, model, and instructions first
An agent is not successful because it produces a convincing demonstration. It is successful when it completes the intended task accurately, consistently, and within acceptable cost and latency limits.
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Write a measurable task definition
Describe the user request, the permitted outcome, and what counts as failure. For example, “find the status of an order and explain the next available action” is testable; “act as a helpful support assistant” is not. Include representative routine requests, ambiguous requests, incomplete information, and requests the agent must decline.
Choose the orchestration approach after the task is clear
In the documented Agents configuration, a foundation model orchestrates the interaction and natural-language instructions guide its behavior. AgentCore provides a managed harness for models, tools, and instructions, while code-defined agents are available when custom orchestration is necessary. The migration documentation compares these options with Agents Classic; do not assume every feature is identical across them.
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Measure completion and correctness separately
A response can be factually correct yet fail to complete the requested workflow, or complete a workflow while giving an incorrect explanation. Score both:
- Goal attainment: Did the agent achieve the requested outcome?
- Answer correctness: Were its statements and calculations accurate?
- Refusal quality: Did it decline requests outside its authority or policy?
Version the scenario set and compare results after changing the model, instructions, prompt templates, or orchestration code. There is no universally best model established by the AWS documentation; select one against your application’s measured requirements.
2. Treat tools and permissions as part of the agent’s behavior
Tools determine what an agent can actually do. In Agents Classic terminology, action groups expose operations, their parameters, and the API handling behind each operation. In AgentCore or a custom implementation, the equivalent tool contract still needs the same discipline.
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Design narrow, explicit tool contracts
- Name each operation for one clear action.
- Define required and optional parameters, types, allowed values, and validation rules.
- Return structured success and error results rather than ambiguous prose.
- Make authorization, idempotency, and confirmation requirements explicit.
Apply least-privilege access
Give the agent role only the permissions its operations require. Separate read operations from state-changing operations where practical, and require an explicit confirmation step for consequential actions such as refunds, account changes, or deletion. A tool that is technically available can still be inappropriate for a particular request.
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Measure whether the agent selects the right tool, supplies valid arguments, handles tool errors, and refrains from calling a tool when the request can be answered without it or when policy forbids the action. Include malformed parameters, timeouts, authorization failures, duplicate requests, and stale data in the scenarios.
| Tool test | What to record |
|---|---|
| Correct selection | Whether the intended operation was chosen |
| Argument accuracy | Required fields, values, and formats |
| Error recovery | Whether the agent explains or safely retries a failure |
| Prohibited call | Whether it correctly avoids an unauthorized or unnecessary operation |
3. Use knowledge bases for grounding, not as a correctness guarantee
A knowledge base gives an agent information it can retrieve when answering questions. Retrieval access does not prove that the final generated answer is correct: the model may select an irrelevant passage, misunderstand a relevant one, or state more than the source supports.
Build a known-answer retrieval set
Create questions whose answers are present in the approved source material, along with questions where the answer is absent or deliberately ambiguous. Record whether the retrieved material is relevant, whether the answer is supported by it, and whether the agent appropriately says that the information is unavailable.
Check the entire grounding chain
- Is the source current, authoritative, and within the intended scope?
- Did retrieval return the passage needed to answer the question?
- Did the response stay within that passage’s meaning?
- Did the agent distinguish sourced facts from assumptions?
- Did it avoid inventing an answer when no supporting material was found?
Measure retrieval correctness and response correctness as separate values. A high retrieval rate cannot compensate for unsupported generation.
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AWS describes Amazon Bedrock Guardrails as evaluating both user inputs and model responses. Documented controls include content filters, denied topics, sensitive-information filters, word filters, and image-content filters. Guardrails can be used with Agents and Knowledge Bases.
Test both interventions and legitimate requests
For every policy, include examples that should be blocked and benign examples that should pass. Record whether the intervention occurred, whether the explanation is usable, and whether acceptable requests were blocked unnecessarily. Test direct prompts, indirect attempts through retrieved content, and sensitive information in both the user message and the generated response.
Keep the boundary clear
Filtering is not a general correctness guarantee. Guardrails may prevent specified content while leaving factual errors, poor tool choices, or incomplete task execution untouched. They are one control in a broader design that includes permissions, validation, human review, and evaluation.
5. Establish evaluation and operational observability
AgentCore Evaluations can assess end-to-end goal attainment, tool-call accuracy, and custom criteria. AgentCore observability documents latency, duration, token use, error rates, and session activity, with CloudWatch as the telemetry destination.
Best Value
Create a small, versioned baseline
Start with a scenario set that covers normal work, edge cases, tool failures, retrieval questions, and disallowed requests. Save the inputs, expected outcomes, relevant source material, tool traces, and scores. Re-run the same set after each meaningful change so improvements and regressions are visible.
Track the measures that drive your decision
| Measure | Why it matters |
|---|---|
| Goal attainment and answer correctness | Shows whether the agent delivers the intended result accurately |
| Tool-call accuracy | Shows whether operations are selected and parameterized correctly |
| Retrieval correctness | Shows whether relevant supporting material is found and used |
| Guardrail behavior | Shows whether prohibited content is stopped without excessive blocking |
| Latency and duration | Shows whether the experience meets response-time requirements |
| Token usage and service cost | Shows the resource impact of prompts, model calls, retrieval, and tools |
| Error rates and session activity | Shows reliability and where users encounter operational failures |
Use traces to diagnose, not just rank
A score tells you that a scenario failed; a trace can show whether the cause was an incorrect plan, a bad tool argument, missing retrieval context, a guardrail intervention, a timeout, or a model response that ignored instructions. Review traces alongside the aggregate measures and compare before-and-after versions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How the pieces fit in a minimal design
For the traditional Bedrock Agents configuration, AWS describes a minimum prepared agent with an agent resource role, a foundation model, and instructions. AWS also recommends configuring an action group or knowledge base. Without either, the agent responds using the foundation model, instructions, and base prompt templates alone. Guardrails and provisioned throughput are optional configurations in that setup.
For a new project, verify AgentCore support and Region availability before committing to an implementation. Decide whether the managed harness supplies enough orchestration or whether a code-defined agent is justified by requirements such as custom routing, state management, or specialized control flow. Preserve the same evaluation set when migrating from Agents Classic so that a change in service path does not hide a regression.
A practical pre-build checklist
- Write the user tasks, success conditions, unacceptable outcomes, and refusal cases.
- Select a candidate model and orchestration path only after those conditions are explicit.
- Define narrow tools, validate their parameters, and map every operation to least-privilege permissions.
- Prepare authoritative knowledge sources and questions with known answers, unsupported answers, and ambiguous answers.
- Configure Guardrails policies, then test blocked and benign requests.
- Create a versioned scenario set covering task completion, correctness, tools, retrieval, policy, failures, and edge cases.
- Baseline latency, duration, token usage, errors, session behavior, and cost implications.
- Inspect traces for failures and repeat the baseline after model, prompt, tool, knowledge, guardrail, or orchestration changes.
- Recheck Agents Classic and AgentCore documentation and Region availability immediately before production deployment.
The Bottom Line
Build the measurement plan before you build the agent. Define the outcome, constrain the tools, verify grounding, test safeguards, and establish operational baselines; then choose between AgentCore approaches or an existing Agents Classic migration path with evidence from representative scenarios rather than a polished demo.
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