The outcome

Deploy a least-privilege inference integration whose model behavior and operational limits are known before production use.

Amazon Bedrock is a managed foundation-model, generative AI, retrieval, guardrail, and agent platform. Building governed generative AI applications across multiple foundation models with managed inference, evaluation, Knowledge Bases, Guardrails, and AWS integrations. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Define the use case, data classification, approved AWS Region, model access, quality tests, latency, throughput, retention, and budget.

  2. 02

    Create a least-privilege IAM role, enable only required model access, use temporary credentials, and keep application and control-plane permissions separate.

  3. 03

    Call the Converse API with a pinned model or inference profile, explicit system rules and limits, and an associated tested guardrail where appropriate.

  4. 04

    Evaluate factuality, safety, structured output, streaming, throttling, timeouts, and model-specific differences on a versioned benchmark.

  5. 05

    Record request metadata without sensitive prompts, monitor CloudWatch and cost, set quotas and alerts, and use staged configuration changes with rollback.

Working standard

What good use looks like.

  • Use temporary least-privilege credentials.
  • Evaluate every candidate model.
  • Set quotas and cost alerts before scale.

Bedrock spans regional services, many models, IAM roles, data sources, and billable resources. Use temporary least-privilege credentials, separate control and runtime access, classify data, verify model and Region support, apply and test guardrails where appropriate, evaluate every model change, preserve retrieval permissions and citations, set quotas and cost alerts, and avoid new designs centered on Agents Classic as it enters maintenance mode.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.