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, Gateway/MCP tools, inline functions, Skills, versioning + endpoints (prod rollout/rollback), advanced config (truncation, limits, lifecycle, network, inbound auth), Observability (log delivery + tracing), Evaluations, Optimizations, Identity (outbound auth, Token Vault, credential providers), Policy guardrails, Payments, and the Agent Registry. 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".
설치
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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, Gateway/MCP tools, inline functions, Skills, versioning + endpoints (prod rollout/rollback), advanced config (truncation, limits, lifecycle, network, inbound auth), Observability (log delivery + tracing), Evaluations, Optimizations, Identity (outbound auth, Token Vault, credential providers), Policy guardrails, Payments, and the Agent Registry. 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, 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 (~16 regions; 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.
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:
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
Built-ins need noconfig (gateway/MCP/inline do); allowedTools has nobrowser_* 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.mdmust start with YAML frontmatter (name + description) or session start fails; git source has no branch field
Set explicit limits (maxIterations/maxTokens/timeout) and lifecycle (idle/max lifetime); choose network + inbound auth deliberately
Phase 3 — Create or update the harness
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.
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.
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
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:
# 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 (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 source, with valid frontmatter (Phase 2)
Memory — managed (default, just pick strategies) or BYO with 3-step wiring + IAM grant (Phase 4)
Policy — agent guardrails via Policy + Policy Engine (policy.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:
Versions gate everything.boto3 >= 1.43.51 and AWS CLI v2 >= 2.34.57, or the harness ops don't exist.
Harness ≠ Runtime API. A harness has two ARNs; UpdateAgentRuntime/InvokeAgentRuntime are rejected for
harness-managed resources. Use the *Harness family + InvokeHarness.
SKILL.md needs YAML frontmatter (name + description) or the session fails at start. Undocumented.
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).
allowedTools has nobrowser_* 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.
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.
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.
Phase 2 — build a Gateway (turn Lambda/OpenAPI/Smithy/MCP-server/API-GW/Runtime into MCP tools): CreateGateway/Target/Rule, inbound authorizerType, outbound credential providers, then wire into a harness
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)
