/amazon-bedrock

Component mapping

Map each source component to its harness target. Mirror behavior, not Bedrock structure. Adapt the .tmpl templates in the skill's assets/ directory: substitute every {{TOKEN}}, and delete every marker block — # <<< RENDER … # <<< /RENDER (token docs) and # <<< OPTIONAL: … # <<< /OPTIONAL (features that don't apply). After rendering, no {{, }}, or <<< may remain — that grep is the verification gate, and it must come back clean.

The .tmpl files are not for any template engine; they are guidance for you (the LLM) on how to fill them in.

Mapping action groups to Gateway targets

A Bedrock action-group Lambda speaks the Bedrock event envelope; AgentCore Gateway invokes Lambda targets with a different shape, so the original won't work behind Gateway unchanged.

Default: proxy-by-ARN. Create a new shim Lambda (lambda_shim.py.tmpl — its docstring documents both envelopes) that translates the Gateway event into the Bedrock event, invokes the original by ARN, and unwraps the response. This leaves the original untouched — editing it in place can change its response shape and break the source agent. The original's ARN is rendered into the shim source as a literal (no env vars — see deploy.md).

OpenAPI action groups: pass the route TEMPLATE verbatim. The original Lambda dispatches by matching apiPath against the literal route template (/customer/{customer_id}), with path-param values delivered separately in parameters. Substituting a value into the path (/customer/tkashina) matches no template, so the original falls through to its unhandled-op branch. Render the shim's _OP_ROUTES table (mapping each operationId to its {method, apiPath-template}) from the source OpenAPI schema with placeholders intact, and keep values in parameters.

Each action group becomes one Gateway targetType: "lambda" code target, hand-added to agentcore.json (the CLI can't create it non-interactively), with toolDefinitions holding one entry per function/operation. agentcore deploy then builds the shim Lambda. Exact block + gotchas in deploy.md; follow it.

Tool schemas — mirror exactly

Reproduce the source action group's schema faithfully into the tool-schema file (tool_schema.json.tmpl): same tool names, parameter names, types, and descriptions. Do not rewrite or "improve" them — fidelity to the source agent's behavior is the goal, and a renamed tool or tightened type changes how the model selects it.

Mapping the knowledge base (connector or KB shim, by type)

Decide by knowledgeBaseConfiguration.type FIRST — the native Gateway connector accepts ONLY a MANAGED Bedrock KB.

• Any non-MANAGED type (VECTOR, KENDRA, SQL) → always the KB shim. The connector cannot accept these at all, so type alone decides and retrieval config is irrelevant. Use the KB shim (kb_shim.py.tmpl): a hand-added targetType: "lambda" code target (see deploy.md) that calls bedrock-agent-runtime:Retrieve and returns MCP-shaped passages.
• MANAGED → then (and only then) check the retrieval config on the KB association (knowledgeBaseConfigurations[].retrievalConfiguration: reranker, metadata filter, hybrid/search-type override, top-k):
  • default retrieval config → native Gateway connector: add gateway-target --type connector --connector bedrock-knowledge-bases --knowledge-base-id <id>.
  • non-default retrieval config → the KB shim (the connector uses a fixed retrieval contract and would silently drop the custom config), reproducing that config from the manifest's KB association.

Both paths reproduce the source agent's retrieval — the choice is wiring, not fidelity, so every reachable KB is still clean. (An unreachable KB is a hard-stop — see eligibility.md. Type picks the wiring; it is never itself the blocker.)

Return-of-Control action groups — cannot migrate

A customControl: RETURN_CONTROL action group has no Lambda; the original application handled execution outside Bedrock. There is no automatic harness equivalent. Do not silently drop it. Classify it cannot in the migration assessment and confirm with the user before continuing — the migrated agent will lack that capability unless the user supplies a backend for it.

Built-in action groups

• AMAZON.UserInput: no tool. Add a clarification instruction to the system prompt; the harness asks clarifying questions naturally.
• AMAZON.CodeInterpreter: use the built-in tool, agentcore add tool --harness <harness-name> --type agentcore_code_interpreter --name <tool-name> (--harness and --name required — see deploy.md).

Mapping memory to managed memory (default)

Use the harness's managed memory, which is on by default — the harness auto-provisions an AgentCore Memory instance (semantic + summarization strategies, with the service's default expiry) and loads/saves session history automatically. Check the harness memory devguide or DescribeHarness for the current defaults rather than assuming a fixed retention. Do not pass --no-harness-memory. This covers the common source case (SESSION_SUMMARY) via the built-in SUMMARIZATION strategy; customize strategies via UpdateHarness only if the source clearly needs more.

Leave truncation at its default (sliding_window). Do not set truncation to summarization — it runs mid-conversation and errors on short sessions (Cannot summarize: insufficient messages). The default is already safe.

Reference: https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/harness-memory.html

Mapping model parameters to the harness model config

Set model params via agentcore add harness flags: --model-id, --model-max-tokens, --temperature, --top-p, --api-format. Some models reject setting temperature and top-p together — check the target model's inference-parameter constraints (e.g. aws bedrock get-foundation-model, or a probe call) and set only one if so.

Mapping idle session TTL (preserve the source's session-lifetime bound)

The source's idleSessionTTLInSeconds bounds how long an idle session is retained — a security control (it caps the credential/context leakage and replay window). Read its actual value from the discovery manifest (the source agent's own setting, whatever it is) and preserve it: if agentcore add harness exposes an equivalent session-TTL flag, set it to the source value. If the CLI has no equivalent, classify it degraded in Phase 4 with the concrete delta (e.g. "source set 300s; migrated defaults to Ns") so the builder decides — never silently extend the window.

Guardrail — cannot migrate; surface to the user

Check whether the installed CLI can attach a guardrail to a bedrock harness before classifying: look in agentcore add harness --help for a guardrail field, and note that --additional-params (the pass-through) is lite_llm-provider only, so a bedrock harness rejects it. If no guardrail path exists in the CLI surface you're running, classify the guardrail cannot in the assessment and tell the user the migrated harness will not enforce it, recording the source guardrailIdentifier + version. Do not fake it with a system-prompt mention (that enforces nothing). Enforcing it means applying ApplyGuardrail outside the harness — out of scope. If the CLI surface you're running exposes a guardrail field, use it and reclassify as clean.

Mapping the prompt to the system prompt

Fold the agent instruction into the harness --system-prompt. If AMAZON.UserInput was present, include the clarification instruction.

Prompt overrides in DEFAULT mode carry nothing custom — skip them. But a non-DEFAULT override (especially ORCHESTRATION or PRE_PROCESSING) may hold real business logic — routing rules like "use tool X for billing questions, tool Y for refunds," or input-classification the app relied on. Don't discard that as boilerplate. Read each non-DEFAULT override, separate the Bedrock-Agents scaffolding (the orchestration loop mechanics the harness now owns) from the business intent, and fold the intent into the system prompt — a modern model handles tool-routing guidance cleanly in the prompt. When unsure whether an override is boilerplate or load-bearing, surface it to the user rather than dropping it.

Mapping the model (mirror the source)

Default --model-id to the source agent's foundationModel. If the CLI's harness default differs, set it explicitly to match the source (parity).