agentcore-harness-builder git:20260813.a836c74A

Immutable. This exact content is served forever at /api/v1/blob/0e40be374ce77b61.

---
name: agentcore-harness-builder
description: Build production-ready AWS Bedrock AgentCore Harness agents end to end — declarative model + system prompt, managed or BYO Memory, built-in Browser and Code Interpreter, Web Search (Gateway connector), Gateway/MCP tools + rate limiting, inline functions, Skills (incl. the AWS-curated catalog), versioning + endpoints (prod rollout/rollback), advanced config (truncation, limits, lifecycle, network, inbound auth, BYO S3/EFS filesystems, BYO container), Observability (log delivery + tracing + unified log group), Evaluations, Optimizations, Identity (outbound auth, Token Vault, credential providers, BYO Secrets Manager secrets), Policy guardrails (Cedar + temporal/stateful policies), Payments, the Agent Registry, Runtime Instances (EC2-backed 14-day sessions), Step Functions integration, and export-to-Strands-code. Use whenever the user wants to create, configure, deploy, version, wire, harden, invoke, or troubleshoot an AgentCore Harness — or asks about AgentCore best practices, harness.json, CreateHarness/UpdateHarness/InvokeHarness, harness endpoints/qualifiers, attaching Memory, wiring browser/code-interpreter, adding skills, observability/log delivery, or A/B-testing prompts. Trigger even when the user describes a managed, declarative Bedrock agent with tools/memory/skills without saying "harness".
license: Complete terms in LICENSE.txt
---

# AWS Bedrock AgentCore Harness Builder

## Overview

A **Harness** is AWS Bedrock AgentCore's declarative, fully-managed way to run an agent. You hand AWS a JSON
configuration — model, system prompt, tools, memory, skills, limits — and AWS runs the agent loop (Strands under the
hood) inside a per-session Firecracker microVM with its own filesystem and shell. No container to build by default
(a custom image via `environmentArtifact` is an advanced option), no agent loop to write. You change behavior by
changing config, not redeploying code, and you can override model/prompt per invocation.

This skill builds a **complete, best-practice Harness use case** that exercises every AgentCore capability the user
needs, wired correctly the first time. Harness is **generally available** (GA 2026-06-17, all AWS Commercial Regions
where AgentCore is available, plus GovCloud US-West since 2026-08; only Payments remains preview), but it is still
fast-moving and the real API shapes often differ from the published docs — this skill encodes the hard-won facts so
you don't rediscover them through validation errors.

### The two-plane mental model (internalize this first)

AgentCore has two distinct API surfaces. Confusing them is the #1 source of wasted time.

| Plane | What it is | How you call it |
|---|---|---|
| **Control plane** | Create/configure/inspect resources (harness, memory, runtimes) | `boto3.client("bedrock-agentcore-control")` — `create_harness`, `update_harness`, `create_memory`, … |
| **Data plane** | Invoke a running harness | `boto3.client("bedrock-agentcore")` — `invoke_harness` |

The agent-side SDK (`pip install bedrock-agentcore`) is a *third* thing: it's the library that runs **inside** a custom
Runtime container (`BedrockAgentCoreApp`, `BrowserClient`, `MemorySessionManager`). **A Harness does not need it** — the
managed harness loader image already wires the tools. You only touch the agent-side SDK if you drop down to Runtime
mode. See `references/decision-guide.md`.

---

## Before you build: confirm Harness is the right tool

Don't assume. If the user needs custom orchestration, sub-second latency, or to embed the agent inside an existing HTTP
service, **Runtime** (code-based) is the better fit. (Classic **Bedrock Agents** is in maintenance mode and closed to
new customers as of 2026-07-30 — don't recommend it.) Read `references/decision-guide.md` and confirm with the user
when it's ambiguous. If Harness is
clearly right (filesystem/shell needed, multi-model switching, declarative iteration, built-in browser/code-interpreter,
stateful memory), proceed.

If Runtime is the right fit instead (you need control of the loop, AG-UI / A2A protocols, embedding in an existing app),
this skill still helps: `references/runtime.md` covers the code-first path end to end (`BedrockAgentCoreApp`,
`@app.entrypoint`, the `/invocations`/`/ping` HTTP contract incl. the `time_of_last_update` gotcha, AG-UI / A2A, and
`CreateAgentRuntime` shapes). Memory, Identity, Observability wire the same way as for a Harness — pass the Runtime's
`roleArn` to `wire_memory.py` and `setup_observability.py`.

---

## The build workflow

Work through these phases in order. Each phase points to a reference file — **read the reference before writing the
config or running the script for that phase.** Don't try to hold every field shape in your head; the references exist
because the exact shapes are non-obvious and the cost of guessing wrong is a failed `update_harness` or a broken
session start.

### Phase 0 — Preflight (always do this first)

The harness control-plane operations simply **do not exist** in older SDKs. Before anything else, run:

```bash
python scripts/preflight.py --region us-east-1
```

This verifies `boto3 >= 1.43.51` and AWS CLI v2 `>= 2.34.57` (older versions return **zero** harness operations),
confirms credentials and region (Harness is GA in ~16 regions — e.g. `us-east-1`, `us-west-2`, `eu-central-1`,
`ap-southeast-2`; check the AgentCore regions page for the full list), and prints the live
`CreateHarness`/`UpdateHarness` input shapes via schema introspection so you build against *this*
account's actual API, not stale docs. If it reports a version gap, fix that before continuing — nothing downstream will
work otherwise. Details: `references/gotchas.md` §versions and §schema-introspection.

### Phase 1 — Design the use case

Decide which capabilities this agent needs. Walk the user through the **feature checklist** below and record choices.
Anchor the design on a known-good shape: `assets/harness.json.template` mirrors a real, working production harness
(the UITestAgent) and is the safest starting point. Copy it and strip what the use case doesn't need rather than
building from an empty file.

### Phase 2 — Author the configuration

Fill in the config section by section. Read the matching reference as you go:

| Section | Reference | Key best practice |
|---|---|---|
| Model + system prompt + inference config | `references/model-and-prompt.md` | Pick an inference-profile model id (`global.*`/`us.*`, default `global.anthropic.claude-sonnet-5`); `apiFormat: "converse_stream"`; keep the prompt declarative and rule-based |
| Tools: Browser, Code Interpreter, Gateway/MCP, inline functions | `references/tools.md` | Built-ins need **no** `config` (gateway/MCP/inline do); `allowedTools` has **no** `browser_*` glob — use `["*"]` or match by name (`"browser"`). For browser SSO behind interactive login (human-in-the-loop), see `references/browser-auth.md` |
| Skills | `references/skills.md` | Every `SKILL.md` **must** start with YAML frontmatter (`name` + `description`) or session start fails; git source has **no branch field** |
| Advanced config: truncation, invocation limits, lifecycle, network, inbound auth | `references/advanced-config.md` | Set explicit limits (maxIterations/maxTokens/timeout) and lifecycle (idle/max lifetime); choose network + inbound auth deliberately |

### Phase 3 — Create or update the harness

```bash
python scripts/create_harness.py --config harness.json --role-arn <EXECUTION_ROLE_ARN>
# or, to modify an existing harness:
python scripts/update_harness.py --harness-id <ID> --config harness.json
```

`update_harness` has subtle payload rules (the `optionalValue` wrapper applies only to `memory` /
`environmentArtifact` / `authorizerConfiguration`, `tags` is a separate `TagResource` call, `clientToken` must be
≥33 chars). The script handles these by introspecting the live shape.
If you ever hand-write an `update_harness` call, read `references/harness-config.md` §update-payload-rules first.
The harness execution role needs a trust policy and base permissions — see `assets/iam_execution_role.json`.

### Phase 4 — Memory: managed (default) or BYO

The simple path is **managed memory** — set `memory.managedMemoryConfiguration` with a `strategies` list
(`SEMANTIC` / `SUMMARIZATION` / `USER_PREFERENCE` / `EPISODIC`) and AWS creates and owns the Memory resource,
IAM included. Only go **BYO** (`agentCoreMemoryConfiguration`) when you need to share one Memory across agents or
control strategy/namespace details. BYO is **not** just "create it and point the harness at it" — the harness's
execution role also needs data-plane permissions on the new Memory ARN, or every invocation fails at session start
with `AccessDeniedException`.

```bash
python scripts/wire_memory.py --harness-id <ID> --role-arn <EXECUTION_ROLE_ARN> \
    --memory-name <name> --actor-id ci-pipeline
```

This does all three BYO steps: `CreateMemory` (with the strategy set), `UpdateHarness(memory=…)`, and an idempotent
`iam:PutRolePolicy` grant scoped to the Memory ARN and namespaces. Read `references/memory.md` before customizing
strategies — episodic requires `reflectionConfiguration`, the field is `strategyId` (not `memoryStrategyId`), and
namespace `{placeholder}` templates must be converted to `glob*` patterns in the IAM condition.

### Phase 5 — Observability: log delivery + tracing

```bash
python scripts/setup_observability.py --harness-id <ID> --region us-east-1 \
    --log-group /aws/bedrock-agentcore/harness/<NAME>
```

Sets up CloudWatch `APPLICATION_LOGS` delivery and X-Ray `TRACES` delivery (delivery sources + destinations +
deliveries, idempotent). Note the asymmetry: `TRACES` go to the **X-Ray** destination type (no `outputFormat` param),
`APPLICATION_LOGS` go to a CloudWatch log group, and the destination log group needs the `AWSLogDeliveryWrite20150319`
resource policy extended for `delivery.logs.amazonaws.com`. The runtime already emits rich OTel logs to a default
group `/aws/bedrock-agentcore/runtimes/<name>-DEFAULT` — that's where dashboard data actually lives.
**Also set `OTEL_TRACES_SAMPLER=always_on`** in the harness's `environmentVariables` (trace sampling is OFF by
default; without it, Phase 7 evaluations silently score nothing). See `references/observability.md`.

### Phase 6 — Invoke and verify

```bash
python scripts/invoke_harness.py --harness-arn <HARNESS_ARN> --prompt "Hello, what can you do?"
```

Use `invoke_harness` on the **data-plane** client (`bedrock-agentcore`), not `invoke_agent_runtime`. Pass a
`runtimeSessionId` and process the streaming response. A successful streamed reply that uses the wired tools is your
proof the configuration is correct end to end. This is the single most important verification — a harness that
`CreateHarness` accepted can still fail at session start (missing SKILL.md frontmatter, missing Memory IAM grant,
tools stored-but-not-wired). Always invoke before declaring success.

### Phase 6b — Version and roll out (production)

Every `UpdateHarness` creates an immutable **version**. For production, pin a named **endpoint** to a known-good
version and test the latest on `DEFAULT`:

```bash
# invoke a specific endpoint/version
python scripts/invoke_harness.py --harness-arn <ARN> --qualifier prod --prompt "..."
```

`CreateHarnessEndpoint(name="prod", version=N)` → callers pass `qualifier="prod"`; promote with
`UpdateHarnessEndpoint`, roll back by repointing. See `references/versioning.md`.

### Phase 7 — Assess: Evaluations and Optimizations

Once the harness runs, make it measurably good. **Prerequisite:** the harness must have
`OTEL_TRACES_SAMPLER=always_on` set (Phase 5) — evaluators read OTel spans, and sampling is off by default, so
without it evaluations sit forever at zero scores with zero errors.

- **Evaluations** (`references/evaluations.md`) — create a batch evaluation over agent traces using built-in or custom
  evaluators, or an evaluation configuration that scores live traffic. Results surface in AgentCore Observability.
- **Optimizations** (`references/optimizations.md`) — generate recommendation candidates (improved system prompts / tool
  descriptions), then validate them with an A/B test (control vs variant) and deploy the winning configuration bundle.

### Phase 8 — Govern: Policy guardrails + publish to the Registry (optional)

If the agent needs **guardrails** beyond IAM (constraining what actions/tools/data it may use), set up a Policy Engine
and policies — see `references/policy.md`. If the org uses the **Agent Registry** to discover and manage agents, MCP
servers, tools, and skills, register the finished harness and its skills there — see `references/registry.md`. For
agents that authenticate to external services (outbound) or transact, see `references/identity.md` and
`references/payments.md`.

---

## Feature checklist

Use this to make the build genuinely comprehensive. For each capability, decide *include / skip* with the user, then
wire it per the referenced phase. A best-practice harness rarely uses *all* of these, but you should consciously
consider each rather than silently omitting it.

- [ ] **Model + system prompt** — provider, inference-profile model id, `converse_stream` apiFormat, inference config (Phase 2)
- [ ] **Browser tool** — `agentcore_browser` (no config needed); allowlist by name `"browser"` or `"*"` (Phase 2)
- [ ] **Code Interpreter tool** — `agentcore_code_interpreter` (no config needed) (Phase 2)
- [ ] **Gateway / remote MCP tools** — external APIs as MCP tools, incl. the managed **web-search** connector and per-user/group **rate limits** (Phase 2; consume via `references/tools.md`, **build** via `references/gateway.md`)
- [ ] **Inline functions** — human-in-the-loop / callbacks that return control to your orchestrator (Phase 2)
- [ ] **Skills** — domain knowledge via git/s3/path/awsSkills source (incl. the AWS-curated catalog), with valid frontmatter (Phase 2)
- [ ] **Memory** — managed (default, just pick strategies) or BYO with 3-step wiring + IAM grant (Phase 4)
- [ ] **Advanced config** — truncation, maxIterations, maxTokens, timeout, lifecycle, network, inbound auth (Phase 2)
- [ ] **Filesystems** — session storage and/or BYO S3 Files / EFS access-point mounts (VPC required) (`advanced-config.md` §Filesystems)
- [ ] **Versioning + endpoints** — pin prod to a version via a named endpoint; qualifier on invoke (Phase 6b)
- [ ] **Observability** — log delivery + X-Ray tracing + `OTEL_TRACES_SAMPLER=always_on` + dashboards; unified per-agent log group for post-2026-07-20 agents (Phase 5)
- [ ] **Evaluations** — online config and/or batch evaluation over traces (Phase 7)
- [ ] **Optimizations** — recommendations + A/B test via SDK (`start_recommendation`, `create_ab_test`) (Phase 7)
- [ ] **Identity** — outbound auth: Workload Identity, Token Vault, API-key/OAuth credential providers, BYO Secrets Manager secrets (`identity.md`)
- [ ] **Policy** — agent guardrails via Policy + Policy Engine, incl. **temporal (stateful) policies** (`policy.md`)
- [ ] **Integrations** — Step Functions `InvokeHarness` state, `toolResultMetadata` stream handling, export to Strands code (`integrations.md`)
- [ ] **Payments** — payment connector/manager + sessions, if the agent transacts (`payments.md`)
- [ ] **Registry** — publish for org-wide discovery (Phase 8)
- [ ] **Tags** — applied via `TagResource` (not `UpdateHarness`); cost-center/team/env/agent-type (Phase 3)

---

## Critical gotchas (the short list — full detail in `references/gotchas.md`)

These cause the most failures. Keep them in mind even before opening the reference:

1. **Versions gate everything.** `boto3 >= 1.43.68` and AWS CLI v2 `>= 2.36.x` for the full 2026-08 surface
   (capacity providers, rate limits, temporal policies, `apiKeySecretSource`); the absolute floor for harness ops
   alone is boto3 1.43.51 / CLI 2.34.57.
2. **Harness ≠ Runtime API.** A harness has two ARNs; `UpdateAgentRuntime`/`InvokeAgentRuntime` are **rejected** for
   harness-managed resources. Use the `*Harness` family + `InvokeHarness`.
3. **`SKILL.md` needs YAML frontmatter** (`name` + `description`) or the session fails at start. Undocumented.
4. **Memory always needs IAM on the execution role.** Managed memory (the default) auto-creates a Memory named
   `harness_<name>_*` — the role needs event/retrieval actions on `arn:...:memory/harness_*` or `InvokeHarness`
   fails with `AccessDeniedException ... ListEvents` (live-verified; `ManagedMemoryEvents` in the IAM asset covers
   it). BYO memory additionally needs the 3-step wiring (create + attach + per-Memory IAM grant).
5. **`allowedTools` has **no** `browser_*` glob** — match by name (`"browser"`, `"code_interpreter"`) or use `["*"]`;
   the `browser_*` glob matches nothing and hides the tool. Gateway/MCP/inline tools still need `tools[].config`;
   built-ins don't.
6. **`update_harness` payload is field-specific**: `optionalValue` wraps ONLY `memory` / `environmentArtifact` /
   `authorizerConfiguration` (`model`/`environment`/`truncation` pass directly — live-verified); `clientToken` ≥33
   chars; `tags` via `TagResource`; memory uses `strategyId`.
7. **When docs and reality disagree, introspect the live schema** (`scripts/preflight.py` /
   `client.meta.service_model.operation_model("UpdateHarness").input_shape.members`) and trust that.

---

## Reference library

Load these as needed — don't read them all upfront.

| File | When to read |
|---|---|
| `references/decision-guide.md` | Phase 0/1 — Harness vs Runtime vs Bedrock Agents |
| `references/runtime.md` | Phase 1/2 — Runtime build path (code-first sibling of Harness): `BedrockAgentCoreApp`, `@app.entrypoint`, `/invocations` + `/ping` (incl. the `time_of_last_update` gotcha), AG-UI / A2A, `agentcore deploy` |
| `references/harness-config.md` | Phase 2/3 — full field reference + update-payload rules + best-practice defaults table |
| `references/model-and-prompt.md` | Phase 2 — provider/model ids, Converse API, inference config, prompt patterns |
| `references/tools.md` | Phase 2 — browser, code interpreter, gateway/MCP, inline functions, allowedTools |
| `references/gateway.md` | Phase 2 — **build** a Gateway (8 target types: Lambda/OpenAPI/Smithy/MCP-server/API-GW/Runtime + built-in connectors like **web-search** + inference targets): `CreateGateway`/`Target`/`Rule`/`RateLimit`, inbound `authorizerType`, outbound credential providers, then wire into a harness |
| `references/browser-auth.md` | Phase 2/6 — human-in-the-loop browser SSO login, S3-signal handoff, inline-function pause/resume, long read_timeout, retrieving session files |
| `references/code-interpreter.md` | Phase 2 — Code Interpreter deep dive: session lifecycle, the 9 tools (executeCode/executeCommand/read·write·list·removeFiles/startCommandExecution/getTask/stopTask), file+command workflows, custom interpreters (PUBLIC/SANDBOX/VPC + certificates), the arguments-is-a-dict gotcha |
| `references/skills.md` | Phase 2 — skills union, git/s3/path sources, mandatory frontmatter |
| `references/memory.md` | Phase 4 — managed memory (default), BYO strategies/retrievalConfig, the 3-step wiring + IAM |
| `references/versioning.md` | Phase 6b — immutable versions, endpoints, qualifier, prod rollout/rollback |
| `references/advanced-config.md` | Phase 2 — truncation, limits, lifecycle, network, inbound auth |
| `references/observability.md` | Phase 5 — log delivery (CWL vs XRAY), resource policy, dashboards |
| `references/evaluations.md` | Phase 7 — online evaluation configs (control plane) + batch evaluations (data plane), both SDK-scriptable |
| `references/optimizations.md` | Phase 7 — recommendations + A/B tests via SDK ops |
| `references/playground.md` | Phase 6 — Console Playground / Sandbox (interactive endpoint testing; console-only, no SDK ops — the repeatable path is InvokeHarness/InvokeAgentRuntime + Evaluations) |
| `references/identity.md` | Outbound auth — Workload Identity, Token Vault, credential providers (incl. BYO Secrets Manager via `apiKeySecretSource=EXTERNAL`) |
| `references/policy.md` | Agent guardrails — Policy, Policy Engine, resource policy, policy generation, **temporal (stateful) policies** |
| `references/integrations.md` | Consuming a harness from outside — `toolResultMetadata` stream fragments, Step Functions `InvokeHarness` state, export to Strands code |
| `references/payments.md` | Payment connector/manager + payment sessions (if the agent transacts) |
| `references/registry.md` | Phase 8 — publishing/discovering org resources |
| `references/gotchas.md` | Anytime something fails unexpectedly — the consolidated hard-learned facts + verified shapes |

## Assets

- `assets/harness.json.template` — full-featured, mirrors a real production harness; the recommended starting point
- `assets/skill.md.template` — a correctly-formatted SKILL.md with the required frontmatter
- `assets/iam_execution_role.json` — trust policy + base permissions for the harness execution role
- `assets/requirements.txt` — pinned minimum versions for the control-plane tooling

## Scripts

Scripts are idempotent where possible; the ones that call AWS mutation APIs (`create_harness`, `update_harness`,
`wire_memory`, `setup_observability`, `invoke_harness`) accept `--dry-run` to print the calls without executing.
`preflight` and `validate_config` are read-only/offline and need no dry-run. Read a script's `--help` before first use.

- `scripts/preflight.py` — version/region/credential checks + live schema introspection
- `scripts/validate_config.py` — lints a `harness.json` against the best-practice rules *before* you call AWS
- `scripts/create_harness.py` — create a harness from config
- `scripts/update_harness.py` — update with correct payload rules
- `scripts/wire_memory.py` — the 3-step BYO memory wiring
- `scripts/setup_observability.py` — log group + delivery sources/destinations/deliveries + resource policy
- `scripts/invoke_harness.py` — data-plane smoke test (`--qualifier` to hit a specific endpoint/version)
- `scripts/test_offline.py` — offline unit tests for the scripts' pure logic (run after modifying any script)