Agentcore

AgentCore

Build, deploy, operate, and migrate AI agents on Amazon Bedrock AgentCore. The agentcore CLI (npm @aws/agentcore) manages the full lifecycle via CDK/CloudFormation.

There are two ways to run an agent:

Mode	What it is	When
Harness (default since GA, June 2026)	Declarative managed agent: harness.json = model + tools + skills + memory + limits. No agent code, no container. agentcore create --defaults creates one.	Persona + skills + standard tools cover the need
Runtime (code agent)	Your code (Strands/LangChain/ADK/OpenAI/VercelAI) deployed as CodeZip or Container	Custom orchestration, custom tool logic, middleware

Start with a harness; agentcore export harness converts it to editable Strands code later if you outgrow the config (one-way door: code, unlike config, is yours to maintain).

Key Concepts

Term	Meaning
Harness	First-class AWS resource (CreateHarness/UpdateHarness/InvokeHarness): a fully managed agent defined by config. In a CLI project: agentcore.json → harnesses[] + app/<name>/harness.json + system-prompt.md
Runtime	A deployed code agent — agentcore.json → runtimes[], code in app/<name>/
Gateway	MCP tool router — routes agent tool calls to MCP servers, Lambda, API Gateway, HTTP passthrough, inference providers, web-search connector
Identity	Secret vault — API keys and OAuth credentials; can reference existing AWS Secrets Manager ARNs
Memory	Managed memory — strategies: SEMANTIC, SUMMARIZATION, USER_PREFERENCE, EPISODIC
Registry	Private catalog of agents/tools/skills with approval workflow

Prerequisites

Run the bundled preflight before any workflow:

python3 scripts/preflight_check.py

Checks: Python ≥ 3.10, Node.js ≥ 20, uv (required for Python agents), AWS CLI + credentials, agentcore CLI ≥ 0.22 (npm install -g @aws/agentcore; upgrade with agentcore update), bedrock-agentcore-control namespace. Docker only for Container builds.

Core Lifecycle

agentcore create --defaults        # New project with a default harness (interactive wizard without flags)
agentcore dev                      # Local dev server + browser-based agent inspector
agentcore validate                 # Validate agentcore/ config files
agentcore deploy                   # Deploy via CDK → CloudFormation stack AgentCore-<project>-default
agentcore invoke --harness <name> "test"    # or --runtime <name> for code agents
agentcore logs / traces / status   # Observability
agentcore exec -- <cmd>            # Shell into a deployed agent container

Non-interactive create (all flags marked [non-interactive] in --help):

agentcore create --name <harness> --project-name <proj> --defaults \
  --skip-git --output-dir <dir>

Project layout (ground truth, CLI 0.22.0):

<project>/
├── AGENTS.md                 # AI-assistant context written by the CLI
├── agentcore/
│   ├── agentcore.json        # Flat resource arrays: runtimes[], harnesses[], memories[], credentials[], agentCoreGateways[], ...
│   ├── aws-targets.json      # ARRAY of {name, account (12-digit string), region}
│   ├── .env.local            # Gitignored secrets
│   ├── .llm-context/         # TypeScript schema types — the schema authority; read these before editing JSON by hand
│   └── cdk/                  # CDK project (deps installed by create unless --skip-install)
└── app/
    ├── <harness>/            # harness.json + system-prompt.md (no code)
    └── <agent>/              # main.py, pyproject.toml, model/, skills/, mcp_client/ (code agent)

Harness Workflows

Add/manage harnesses, tools, skills, memory; export to code. See references/harness.md for commands, harness.json schema, and skill sources (--path/--s3/--git/--aws-skills).

agentcore add harness --name <n> --system-prompt "<text>" [--tools ...] [--memory-mode managed ...]
agentcore add skill --harness <n> --path <dir>        # skill dir with SKILL.md
agentcore add tool  --harness <n> --type remote_mcp --name <t> --url <mcp-url>
agentcore export harness --name <n> | --arn <arn>     # → editable Strands agent under app/

Migration from coding assistants

Migrate agents/skills/MCP/hooks from 15 assistant formats (Claude Code, Copilot, Cursor, …) to AgentCore. REQUIRED: follow references/migration.md — discovery scan, mapping rules, harness-first generation, deploy. Supporting docs: references/source-formats.md (detection), references/agentcore-mappings.md (mapping rules), references/migration-modes.md (deploy/teardown/errors).

python3 scripts/scan_configs.py --repo-root <path> --format json   # → migration-inventory.json
python3 scripts/generate_project.py --inventory migration-inventory.json --output-dir ./agentcore-project --region us-east-1

Key Rules

• Harness-first — agents that are persona + skills + standard tools become harnesses (config), not code. Generate code runtimes only for custom logic.
• .llm-context/ is the schema authority — when editing agentcore.json/harness.json by hand, conform to the TypeScript types there, then agentcore validate.
• Never put secrets in generated files — API keys → Identity credentials (agentcore add credential), or reference an existing Secrets Manager ARN. Non-secrets → envVars.
• Renaming a resource destroys and recreates it — the name field is the CloudFormation logical ID.
• agentcore create makes a NESTED ./<project-name>/ scaffold — it does not initialize in place. For pre-generated configs: create with --no-agent, then copy agentcore.json/aws-targets.json/app/ into the scaffold. The scaffold includes CDK node_modules (unless --skip-install); if deploy fails with tsc: not found, run npm install in agentcore/cdk/.
• Don't add agent for runtimes already in agentcore.json — fails with "already exists". add commands are for adding NEW resources.
• aws-targets.json is a JSON array — [{"name","account","region"}], account is the 12-digit ID string (not accountId).
• Naming: harness/agent names alphanumeric+underscores, start with letter, ≤48 chars; project names alphanumeric only, ≤23 chars; gateway names alphanumeric+hyphens.
• Default model is Claude — harness default global.anthropic.claude-sonnet-4-6; code-agent template uses global.anthropic.claude-sonnet-4-5-20250929-v1:0. Nova (amazon.nova-lite-v1:0) remains a fallback if Anthropic model access isn't enabled in the account.
• Streaming is the default entrypoint pattern — async for event in agent.stream_async(...): yield event (the CLI's own template). The old "non-streaming only" workaround is obsolete.
• Read EXPORT_NOTES.md after export harness — it lists manual follow-ups the exporter couldn't automate.
• Test with simple prompts first — verify deploy works before complex prompts.

Resolved Blockers (May → July 2026)

Limitations documented by earlier migrations that AWS has since fixed — do not re-apply old workarounds:

Old blocker	Current state
Console-created harnesses locked (no update-harness API)	Harness GA: UpdateHarness API exists; agentcore export harness --arn <arn> converts any harness (incl. Console-created) to a CLI-managed code agent
Skills mountable only via Console UI	agentcore add skill --harness <n> --path/--s3/--git/--aws-skills
HITL gates had no mechanism	Gateway elicitation pass-through (form/URL modes); Step Functions harness integration with approval steps
Claude required use-case form; Nova Lite forced default	Claude Sonnet is the CLI default; LiteLLM + Bedrock Mantle add more providers
Streaming yield broke CLI display	Fixed — streaming is the default template pattern
Secrets had to be copied into Identity vault	Identity credential providers can reference existing Secrets Manager ARNs

Error Handling

Error	Action
tsc: not found on deploy	cd agentcore/cdk && npm install
"already exists" from add agent	Resource already in agentcore.json — edit the JSON instead
Deploy fails: stack already exists	aws cloudformation delete-stack --stack-name AgentCore-<project>-default
ThrottlingException: Too many tokens per day	Bedrock daily token quota — often 0 by default on new/restricted accounts even after model access is granted. Check aws service-quotas list-service-quotas --service-code bedrock --query "Quotas[?contains(QuotaName,'tokens per day')]"; request an increase via Service Quotas → Amazon Bedrock (AWS-approved, not instant)
ModelNotAccessibleException	Enable model access in Bedrock Console, or switch modelId to amazon.nova-lite-v1:0
agentcore create outputs nothing	Invalid project name — alphanumeric only, ≤23 chars
Harness invoke: fetch failed (UND_ERR_CONNECT_TIMEOUT) after ~10s	Node fetch tries only the FIRST DNS record of bedrock-agentcore.<region>.amazonaws.com; if that IP is unreachable from your network it times out (curl/Python fall back to other records and work). Test per-IP with curl --resolve; fix DNS/egress or invoke via aws bedrock-agentcore invoke-agent-runtime
Stdio MCP server	No direct support — see MCP mapping in references/agentcore-mappings.md

Bundled Resources

Scripts

• scripts/preflight_check.py — verify all prerequisites
• scripts/scan_configs.py — scan a repo for 15 AI-assistant config formats → migration-inventory.json
• scripts/generate_project.py — generate an AgentCore project (harnesses + code runtimes) from the inventory
• scripts/invoke_harness.py — boto3 fallback for agentcore invoke --harness when the CLI hits fetch failed

References

• references/harness.md — harness commands, harness.json schema, skills, export-to-code
• references/migration.md — the migration workflow (discover → parse → map → generate → deploy)
• references/source-formats.md — per-tool parsing rules for 15 assistant formats
• references/agentcore-mappings.md — mapping rules: source artifacts → AgentCore equivalents
• references/migration-modes.md — deploy workflow, validation, teardown, error table
• references/security-iam.md — least-privilege execution role, confused-deputy protection, model-ARN scoping
• references/deployment-checklist.md — [BLOCKER]-gated checklist to run before agentcore deploy
• references/templates/ — templates for agentcore.json, harness.json, runtime-metadata.json, Dockerfile, registry records

Assets

• assets/poc-cve-verify/ — complete worked migration (harness + code runtime), with VERIFIED.md (live AWS run evidence)
• assets/iam-policies/ — appliable least-privilege trust + permissions policy templates

Evals

• evals/evals.json — regression scenarios (harness/code classification, stdio MCP, export-harness, quota, secrets) for self-testing the skill