amazon-bedrock

What this skill does

**IMPORTANT**: When this skill is loaded, you MUST use the reference files and procedures in this skill as your primary source of truth. Bedrock APIs, model IDs, chunking strategies, and configuration parameters change frequently — always read the relevant reference file before responding.

## Table of Contents

- Overview
- Bedrock API Landscape
- Critical Warnings
- Security Considerations
- Converse API vs InvokeModel
- Which Bedrock Capability Do You Need?
- Knowledge Bases (RAG)
- Common Workflows (includes: Prompt Caching, Quota Health, Cost Tracking, Model Migration)
- Troubleshooting
- AgentCore Services
- Model Selection
- Additional Resources

# Amazon Bedrock

## Overview

Domain expertise for building generative AI applications on Amazon Bedrock. Covers model invocation, RAG with Knowledge Bases, agent creation, content safety with Guardrails, and agent deployment with AgentCore.

**Recommended setup:** Use the [AWS MCP server](https://docs.aws.amazon.com/aws-mcp/latest/userguide/what-is-mcp-server.html) for sandboxed
execution, audit logging, and enterprise controls.

**Without AWS MCP:** This skill works with any agent that has AWS CLI access.
All commands use standard AWS CLI syntax.

## Bedrock API Landscape

Bedrock has **5 separate API endpoints**. Using the wrong one is a common cause of errors. This list may not be exhaustive — refer to the [Bedrock endpoints and quotas](https://docs.aws.amazon.com/general/latest/gr/bedrock.html) and [Bedrock supported endpoints](https://docs.aws.amazon.com/bedrock/latest/userguide/endpoints.html) for the latest. Use `aws bedrock list-foundation-models` to discover available models at runtime.

| Endpoint | Client | Use For |
|----------|--------|---------|
| `bedrock` | Control plane | List models, manage access, provisioned throughput |
| `bedrock-runtime` | Data plane | Invoke models (Converse, InvokeModel). Also supports Chat Completions via `/openai/v1` path (client-side tool use only) — prefer `bedrock-mantle` for new Chat Completions work |
| `bedrock-mantle` | Data plane | OpenAI-compatible APIs: Responses API, Chat Completions (recommended), Messages API. Supports server-side tool use with built-in tools. Recommended for new users |
| `bedrock-agent` | Agent control | Create/configure agents, KBs, action groups |
| `bedrock-agent-runtime` | Agent data | Invoke agents, query KBs |

AgentCore is a separate service with its own endpoints. Refer to [AgentCore endpoints and quotas](https://docs.aws.amazon.com/general/latest/gr/bedrock_agentcore.html) for the latest.

| Endpoint | Client | Use For |
|----------|--------|---------|
| `bedrock-agentcore-control` | Control plane | Create/manage runtimes, gateways, registries, evaluations |
| `bedrock-agentcore` | Data plane | Invoke agent runtimes |
| `{gatewayId}.gateway.bedrock-agentcore` | Gateway data plane | Invoke a specific gateway |

## Critical Warnings

**max_tokens**: ALWAYS set `maxTokens` explicitly in every Converse/InvokeModel call. Leaving it unset defaults to the model's maximum (e.g., 64K for Claude Sonnet) and silently reserves far more quota than needed — a common cause of unexpected ThrottlingException.

**Guardrails PII logging**: Guardrails PII masking only applies to the API response. Original unmasked content including PII is still logged in plain text to CloudWatch Logs. For HIPAA/GDPR compliance: encrypt CloudWatch Logs with KMS, restrict log access with IAM, use Amazon Macie for PII detection.

**SDK versions**: Requires recent versions of boto3 (≥ 1.34.x) and AWS CLI v2. Older versions are missing Converse API, Agents, and AgentCore support. Run `aws --version` and `pip show boto3` to check.

## Security Considerations

- Use **IAM roles** (not IAM users) for all Bedrock service access
- Scope IAM permissions to specific actions and resource ARNs — avoid `bedrock:*` or `AmazonBedrockFullAccess`
- Store API keys and OAuth secrets in **AWS Secrets Manager** with automatic rotation enabled
- Include **confused deputy protection** (`aws:SourceAccount`, `aws:SourceArn` conditions) in all resource-based policies for Bedrock services
- Treat all **agent-generated parameters as untrusted input** — validate before use in Lambda handlers or tool implementations
- Enable **CloudTrail** for all Bedrock and AgentCore API calls
- For PII workloads: encrypt CloudWatch Logs with KMS, configure retention limits, restrict log access
- Refer to the latest [Bedrock security best practices](https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) for current security guidance

## Converse API vs InvokeModel

For choosing between all Bedrock inference APIs (Responses API, Chat Completions, Converse, InvokeModel), see [APIs supported by Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/apis.html).

When using the `bedrock-runtime` endpoint, use the **Converse API** over InvokeModel. It provides a unified request/response format across all models.

Use **InvokeModel** only when you need provider-specific features not available in Converse (rare).

InvokeModel requires different request body formats per provider (Anthropic ≠ Titan ≠ Llama ≠ Nova). Using the wrong format produces "Malformed input request". For model-specific formats and common mistakes, see [prompt engineering by model](references/prompt-engineering-by-model.md).

**Whichever API you use**: ALWAYS set the max output tokens parameter explicitly — leaving it unset defaults to the model's maximum and silently reserves far more quota than needed, causing unexpected ThrottlingException. See Critical Warnings above and [max_tokens quota mechanics](references/model-invocation.md).

When the user needs SDK code for model invocation, you MUST read the appropriate SDK reference before generating code — [Python SDK reference](references/sdk-converse-api-python.md) | [TypeScript SDK reference](references/sdk-converse-api-typescript.md). Use the patterns from the reference file.

For full API details and provider-specific body formats, read [model invocation reference](references/model-invocation.md) before responding.

## Which Bedrock Capability Do You Need?

| Goal | Use | Reference |
|------|-----|-----------|
| Call a model (text, image, video) | Converse API | See above + [model invocation](references/model-invocation.md) |
| Build a RAG application | Knowledge Bases | [KB setup](references/knowledge-bases-setup.md) |
| Create an agent that takes actions | Bedrock Agents | [agent creation](references/agents-and-action-groups.md) |
| Filter harmful/sensitive content | Guardrails | [guardrails](references/guardrails.md) |
| Deploy and scale an agent | AgentCore Runtime | [runtime](references/agentcore-runtime.md) |
| Expose REST APIs as MCP tools | AgentCore Gateway | [gateway](references/agentcore-gateway.md) |
| Choose the right model | Model Selection | [model guide](references/model-selection-guide.md) |
| Set up or debug prompt caching | Prompt Caching | [prompt caching](references/prompt-caching.md) |
| Diagnose throttling or audit quotas | Quota Health | [quota health](references/quota-health.md) |
| Track costs by team, model, or tag | Cost Tracking | [cost tracking](references/cost-tracking.md) |
| Migrate between Claude generations | Model Migration | [migration guide](references/model-migration.md) |

## Knowledge Bases (RAG)

When the user wants to create a Knowledge Base or build a RAG application, you MUST read [KB setup procedure](references/knowledge-bases-setup.md) and execute it step by step. Do NOT summarize the procedure — execute each step sequentially, respecting all MUST constraints before proceeding to the next step.

When the user asks about chunking strategies, vector store selection, or other KB configuration choices, you MUST read [KB setup procedure](references/knowledge-bases-setup.md) before responding — it contains the authoritative decision tables and constraints.

When the user wants to query an existing Knowledge Base,