amazon-bedrock

**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. ## Guardrail — where this skill's own files live (MCP vs local install) This skill can be loaded two ways, and they resolve the skill's own bundled files from different places. Determine how the skill was loaded before reading a reference or running a script: - **Loaded through the AWS MCP `retrieve_skill` tool:** The skill is not installed on the local filesystem. You MUST fetch each reference or script via `retrieve_skill` with the `file` parameter (e.g. `file="references/managed-knowledge-bases-setup.md"` or `file="scripts/fetch_bedrock_agent.py"`), and run the script from the returned content. Do NOT `file_read` these paths locally — they do not exist on disk. - **Installed locally** (e.g. `.kiro/skills/amazon-bedrock/` or `~/.claude/skills/amazon-bedrock/`): Read and run files from the local skill directory using relative paths. This distinction applies only to the skill's own packaged files. User data and session artifacts are always read from and written to the user's working directory. Never fetch or write customer data through `retrieve_skill`. ## Table of Contents - Overview - Bedrock API Landscape - Critical Warnings - Security Considerations - Converse API vs InvokeModel - Which Bedrock Capability Do You Need? - Knowledge Bases (retrieval, agentic retrieval and RAG; includes: migration to Managed KB) - 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. **Bedrock Agents classic is in maintenance mode**: classic Bedrock Agents (`bedrock-agent`) is in maintenance mode and closed to new customers ([announcement](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-classic-maintenance-mode.html)). For new agent workloads use AgentCore (the Harness managed loop); for existing agents, recommend migrating to an AgentCore Harness — see the [migration guide](references/migrate-bedrock-agents-to-agentcore-harness.md). ## 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 [Bedrock security best practices](https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) for 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 (RAG on Bedrock) | Knowledge Bases — **Managed KB (MKB) by default**; Customer-managed only if the user explicitly asks | [managed KB setup](references/managed-knowledge-bases-setup.md) (see KB decision guide) | | 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) | | Run a config-based managed agent loop on AgentCore (no code, no container) | AgentCore Harness | [harness](references/agentcore-harness.md) | | Migrate an existing Bedrock Agent (classic) to an AgentCore Harness | Bedrock Agents to AgentCore harness Migration | [migration guide](references/migrate-bedrock-agents-to-agentcore-harness.md) | | Migrate an existing Customer-managed Knowledge Base to a Managed KB | KB migration | [migrate KB to Managed](references/migrate-knowledge-base-to-managed.md) | | Deploy and scale an agent loop you wrote yourself | 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 (retrieval, agentic retrieval and RAG) For RAG on Bedrock, **default to the Managed Knowledge Base (MKB)** — AWS recommends it and it fully manages storage, chunking, parsing, and retrieval, so there is no infrastructure to provision. Prefer it for essentially all new RAG work. Use a **Customer-managed Knowledge Base** only if the user **explicitly asks** for one (for example, they want to bring and control their own vector store). Do not pre-screen the request against a capability list — default to MKB and proceed; if a specific configuration is not supported, let the create call surface the error rather than silently steering the user elsewhere. Read the [KB decision guide](references/knowledge-bases-decision-guide.md) to route creation and querying to the correct reference for the chosen type. Connector availability, quotas, and regional coverage change frequently — consult the [Bedrock Knowledge Base documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html) rather than assuming a fixed list. To move an **existing** Customer-managed KB to a Managed KB, follow the [migrate KB to Managed](references/migrate-knowledge-base-to-managed.md) procedure rather than creating a new KB from scratch. Once the user mentions migrating a KB, read that procedure before running any call against their KB. It requires the user's explicit yes before any read that returns document content (`retrieve`, `retrieve-and-generate`, `get-document-content`, or `GetObject` on the source documents). Ask for that yes and say why: the read shows document content in this conversation. ## Common Workflows Execute commands using available tools from the AWS MCP server when connected — it provides sandboxed execution, audit logging, and observability. When the MCP server is not available, fall back to the AWS CLI or shell as needed. Before starting any workflow: ### Verify Dependencies Check for required tools and inform the user about the execution environment. **Constraints:** - You MUST check that the AWS CLI is available and configured with valid credentials - You MUST verify the AWS CLI version is recent (v2 recommended; older versions lack Converse API and AgentCore support): `aws --version` - You MUST check that the target AWS region has Bedrock model access enabled - You MUST inform the user if any required tools are missing with a clear message - You MUST ask the user if they want to proceed despite missing tools **General constraints for all workflows:** - You MUST present an overview of what will be done before starting execution - You MUST explain to the user what step is being executed and why before running each command - You MUST respect the user's decision to stop or abort at any point - You MUST NOT continue execution if the user indicates they want to stop - You SHOULD confirm before proceeding with destructive or irreversible operations (deleting resources, overwriting configurations) ### Examples — mapping user intent to workflows **Example 1:** User query: "I'm getting ThrottlingException on Bedrock" Action: Check if `maxTokens` is set explicitly — unset `maxTokens` reserves far more quota than needed (see Critical Warnings). If already set, check quota: `aws service-quotas get-service-quota --service-code bedrock --quota-code <code> --region <region>` **Example 2:** User query: "Set up RAG for my PDF documents" Action: Follow the Create a Knowledge Base workflow — default to the **Managed KB** (Bedrock manages chunking, parsing, and the vector store; no infrastructure to provision). See [managed KB setup](references/managed-knowledge-bases-setup.md) and the [KB decision guide](references/knowledge-bases-decision-guide.md). Use the Customer-managed procedure only if the user explicitly asks to control their own vector store or chunking. **Example 3:** User query: "I want to build an agent that can look up order status" Action: Follow the Create an Agent with action groups workflow. See [agent creation procedure](references/agents-and-action-groups.md). **Example 4:** User query: "How do I call Claude on Bedrock?" Action: Use the Converse API (not InvokeModel). Set `maxTokens` explicitly. Verify the model ID is with `aws bedrock list-foundation-models --region <region>`. Use cross-region model ID with `us.` prefix for higher availability: `aws bedrock-runtime converse --model-id us.anthropic.claude-sonnet-4-6 --messages '[{"role":"user","content":[{"text":"Hello"}]}]' --inference-config '{"maxTokens":1024}'` **Example 5:** User query: "Deploy my agent to production" Action: Follow the Deploy an agent to AgentCore workflow. Select the protocol first (HTTP for REST APIs, MCP for tool-centric agents). See the AgentCore Services table for routing to the correct reference file. **Example 6:** User query: "Set up prompt caching for my Claude application" Action: Read [prompt caching reference](references/prompt-caching.md) for setup workflow, TTL configuration, and minimum token thresholds. Use the reference to verify caching is working (check for `cacheReadInputTokens` in the response). **Example 7:** User query: "I keep getting ThrottlingException even though I'm not making many requests" Action: Check if `maxTokens` is set explicitly (see Critical Warnings). Read [quota health reference](references/quota-health.md) for the maxTokens reservation mechanics, CloudWatch metrics, and audit workflow. **Example 8:** User query: "How do I track Bedrock costs by team?" Action: Read [cost tracking reference](references/cost-tracking.md) for inference profile tagging, CUR 2.0 approaches, and Cost Explorer queries by model/region/tag. **Example 9:** User query: "I'm upgrading from Claude 4.5 to 4.6, what breaks?" Action: Read [model migration reference](references/model-migration.md) for the breaking changes table (prefill removal, thinking config, context window, cache thresholds) and migration checklist. ### Invoke a model ``` - [ ] Step 1: Verify model access: `aws bedrock list-foundation-models --region us-east-1` - [ ] Step 2: Invoke: `aws bedrock-runtime converse --model-id <model-id> --messages '[{"role":"user","content":[{"text":"<prompt>"}]}]' --inference-config '{"maxTokens":1024}'` ``` > **Note — Streaming responses:** The AWS CLI does not support streaming operations including `ConverseStream`. Use the SDK (`converse_stream()` in boto3, `ConverseStreamCommand` in JS SDK). > > | Mode | When to use | > |------|-------------| > | **Converse** | Batch/backend pipelines — single complete response, no stream handling required | > | **ConverseStream** | Chat UIs/interactive apps — tokens delivered as they generate | ### Create a Knowledge Base Default to the **Managed KB**: you MUST read [managed KB setup](references/managed-knowledge-bases-setup.md) and execute it in order (create → managed-connector data source → ingest → verify with `Retrieve`). Use [customer-managed KB setup](references/knowledge-bases-setup.md) (its 7-step procedure) **only if the user explicitly asks** for a Customer-managed KB. Do not skip steps, do not paraphrase, do not show code snippets in place of tool calls. See the KB decision guide. ### Query a Knowledge Base Determine the KB type — the query API differs: - **Managed KB (MKB):** use `Retrieve` (single-shot chunks) or `AgenticRetrieveStream` (agentic; streaming ⇒ SDK-only, not the AWS CLI); read [managed KB retrieval](references/managed-knowledge-bases-retrieval.md). - **Customer-managed KB:** read [customer-managed KB retrieval](references/knowledge-bases-retrieval.md) for its retrieve-and-generate / retrieve / manual modes. `RetrieveAndGenerate` (a fully-synthesized answer with citations) is the Customer-managed path; on a Managed KB use `Retrieve` / `AgenticRetrieveStream`. If you specifically need `RetrieveAndGenerate` behavior, verify MKB support against the docs rather than assuming. ### Create an Agent with action groups You MUST read [agent creation procedure](references/agents-and-action-groups.md) before responding. Execute the procedure step by step. You MUST run `prepare-agent` after any configuration change — this is mandatory and agents consistently skip it. ### Apply Guardrails You MUST read [guardrails reference](references/guardrails.md) before responding. Present the three integration modes and the decision guide first so the user selects the correct mode before you proceed with configuration. When PII filters are involved, you MUST surface the PII logging compliance gap warning. Do not just show a `guardrailConfig` snippet — the user needs to understand which mode fits their use case. ### Deploy an agent to AgentCore If the user wants a managed agent loop without writing orchestration code, route to **Harness** (config-based). Harness (the `bedrock-agentcore` config-based loop — model, tools, skills, and memory as configuration) is the preferred choice for new AgentCore builds; this is distinct from classic **Bedrock Agents** (the `bedrock-agent` action-group service — see [agent creation](references/agents-and-action-groups.md)). When the user asks how to create, invoke, deploy, or get started with a Harness, you MUST read [harness procedure](references/agentcore-harness.md) and follow its Deployment Workflow step by step before responding. Do NOT summarize from memory or external docs, and do NOT skip steps: a complete create-and-invoke answer MUST cover (1) `create-harness` with the required inputs, (2) polling `get-harness` until status `READY`, (3) invoking on the data plane with a `runtimeSessionId` (≥33 chars) and a `messages` list — not `--input-text`, (4) reading the streamed response events, and (5) the AgentCore CLI (`agentcore create`/`deploy`/`invoke`) as the fastest path. The reference is authoritative over any external documentation. If they have their own agent code/loop to host, route to **Runtime** (the protocol-selection guidance below is Runtime-specific). Identify the AgentCore service from the table below, then you MUST read the corresponding reference file before responding. Follow any procedures in the reference step by step. Do not summarize — execute. ### Set up or debug prompt caching