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Agents Build

AWSOfficialCreated by aws-agentcore

  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Use to extend an existing agent project with memory, app integration, VPC, multi-agent, migration, model, browser, code interpreter, payments, or resource removal. Triggers: "add memory", "remember across sessions", "call agent from app", "invoke agent from code", "agent auth", "streaming", "VPC", "VPC connectivity", "can't reach from VPC", "multi-agent", "A2A", "A2A auth", "orchestrator not delegating", "specialist not called", "migrate Bedrock Agent", "migration issue", "change model", "browser tool", "code interpreter", "delete agent", "tear down", "agentcore remove", "cross-account memory", "add payments capability to my agent", "wire payments plugin", "integrate x402 payments with the agent I'm building", "add MPP payments", "Machine Payments Protocol". External APIs via Gateway: use agents-connect. New project: use agents-get-started. CLI/dev-server errors: use agents-debug. Runtime x402/MPP payments: use agents-pay. Migration-specific Strands vs LangGraph routes here.
— the description by aws-agentcore

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The agents-build skill provides a structured workflow for adding advanced features to existing AgentCore projects. It acts as a central hub for configuring infrastructure and functional capabilities, such as cross-session memory, VPC connectivity, multi-agent delegation, and sandboxed code execution. The skill ensures project integrity by verifying the environment, checking for existing project configurations, and guiding the user through the appropriate implementation path based on their specific requirements.

What it does

  • Adds cross-session memory to maintain user context.
  • Configures VPC networking for secure access to private resources.
  • Enables multi-agent systems with orchestrator-specialist patterns.
  • Integrates browser and code interpreter tools for enhanced agent capabilities.
  • Facilitates the migration of existing Bedrock Agents to AgentCore.
  • Handles resource removal and project teardown.

When to use it

  • When you need to expand an existing agent's functionality.
  • When setting up private network connectivity for your agent.
  • When transitioning from a single-agent to a multi-agent architecture.
  • When you need to add sandboxed code execution or web browsing capabilities.
  • When you need to clean up or remove specific agent resources.

Advantages

  • Provides a consistent, guided workflow for complex configuration tasks.
  • Prevents common errors by validating project context and naming conventions.
  • Reduces manual setup time for infrastructure-heavy features like VPCs.
  • Centralizes documentation and implementation steps for diverse agent capabilities.

Usage examples

“add memory to my agent”
The skill reads the project configuration and provides the necessary steps to implement cross-session memory storage.
“I need to add a code interpreter to my agent”
The skill guides you through the integration of the AgentCore Code Interpreter tool for sandboxed Python or JS execution.
“how do I set up VPC connectivity?”
The skill loads the VPC reference documentation and walks you through configuring subnets and security groups for your agent.
“tear down my agent”
The skill initiates the resource removal workflow to safely destroy the specified agent deployment.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/aws/agents-build/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip agents-build.zip -d .agents/skills

Documentation by aws-agentcore

build

Add capabilities to your AgentCore agent project.

When to use

  • Adding cross-session memory to your agent

  • Calling your deployed agent from a web app, mobile app, or backend service

  • Configuring VPC networking for private resources (RDS, internal APIs)

  • Building multi-agent systems with orchestrator/specialist patterns

  • Migrating an existing Bedrock Agent to AgentCore

  • Adding the Browser tool so the agent can navigate websites

  • Adding the Code Interpreter so the agent can execute code in a sandbox

  • Adding AgentCore Payments so the agent can pay for x402- or MPP-protected APIs, tools, or content

  • Removing resources from your project or tearing down a deployment

Do NOT use for:

  • Connecting to external tools/APIs via Gateway (OpenAPI specs, Lambda, MCP servers, credentials, policies) → use agents-connect

  • Scaffolding a new project → use agents-get-started

  • Deploying → use agents-deploy

Input

$ARGUMENTS can be:

  • A capability: "memory", "integrate", "vpc", "multi-agent", "migrate", "browser", "code-interpreter", "payments", "teardown"

  • A description of what they want: "remember user preferences", "call from React app", "scrape a website", "run pandas in the agent", "delete my agent", "clean up resources"

  • Empty — the skill will determine the workflow from context

Process

Step 0: Verify CLI version

Run agentcore --version. This skill requires v0.9.0 or later.

If older: "Run agentcore update to get the latest version."

Step 1: Read project context

Read agentcore/agentcore.json to understand the current project — framework, existing resources, agent configuration.

If agentcore/agentcore.json is not found:

  1. Check if the developer is in the wrong directory. Look for agentcore/agentcore.json in parent directories (up to 3 levels). If found, tell them: "Found an AgentCore project at <path>. Are you working in that project?"

  2. If no project exists anywhere nearby, ask what capability they wanted to add. Then offer two paths:

    • "I can walk you through creating a project first and then adding CAPABILITY — want to do that?" (run the get-started flow inline, then continue with the build workflow)

    • "If you already have a project elsewhere, cd into it and try again."

Do not just say "go use agents-get-started" and stop — that loses the developer's context about what they actually wanted to do.

Step 2: Determine the workflow

Important disambiguation — before routing to a build reference, check if the prompt is actually a connect or debug concern:

  • If the phrase mentions external APIs, Lambda functions, OpenAPI specs, gateways, credentials, MCP servers, or policies → this is agents-connect, not build

  • If the developer says something is broken (wrong answers, errors, tool failures) → this is agents-debug, not build

  • Build is for adding new capabilities to a working project, not fixing broken ones

Based on the developer's prompt and $ARGUMENTS, load the appropriate reference:

Developer intentReference to load
Add memory, remember things, user preferences, cross-sessionreferences/memory.md
Call agent from app, invoke from code, streaming, SDK client, agent URL, execute shell in sessionreferences/integrate.md
VPC, private network, RDS, internal API, subnet, security groupreferences/vpc.md
Multi-agent, orchestrator, specialist, A2A, delegation, agent handoffreferences/multi-agent.md
Custom headers from caller to agent, header allowlist, tenant ID/correlation ID/trace propagationreferences/request-headers.md
Migrate Bedrock Agent, import agent, move to AgentCorereferences/migrate.md
Browser tool, web navigation, form filling, scraping, Nova Act, Playwright, live viewreferences/browser.md
Code Interpreter, execute code, sandbox, run Python/JS/TS, data analysis in agent, pandasreferences/code-interpreter.md
Payments, pay for x402 or MPP content, 402 Payment Required, Machine Payments Protocol, WWW-Authenticate: Payment, microtransactions, paid API/tool, payment manager/connectorreferences/payments.md
Delete agent, remove resource, tear down, clean up, destroy, start freshreferences/teardown.md
Change model, switch model, use Haiku/Sonnet/Nova, different modelInline — see "Changing the model" below

If the developer asks about the difference between local dev and deployed (e.g., "why does my memory work after deploy but not locally?"), load references/local-vs-deployed.md alongside the specific workflow reference.

Read the matching file into context and follow its Process section step by step — do not summarize.

If the intent is ambiguous, ask the developer which capability they want to add.

Changing the model

The model is configured in app/<AgentName>/model/load.py (scaffolded by agentcore create). To change it:

  1. Open app/<AgentName>/model/load.py

  2. Change the model_id parameter in the BedrockModel() constructor

# Default (scaffolded by CLI)
return BedrockModel(model_id="global.anthropic.claude-sonnet-4-5-20250929-v1:0")

# Switch to Haiku for cost savings
return BedrockModel(model_id="us.anthropic.claude-3-5-haiku-20241022-v1:0")

# Switch to Nova Lite
return BedrockModel(model_id="amazon.nova-lite-v1:0")

Cross-region inference profile prefixes (us., eu., apac., global.) control where inference runs. Use global. for maximum throughput, or a geographic prefix for data residency. Not all models support all prefixes — check the Bedrock inference profiles docs.

After changing the model:

  • Verify the model is enabled in your region: AWS Console → Amazon Bedrock → Model access

  • For cross-region profiles, enable in all destination regions

  • If using agents-harden, update the IAM policy to scope to the new model ARN

  • Run agentcore dev to test locally, then agentcore deploy to update the deployed agent

No agentcore.json change is needed — the model is configured in code, not in the project config.

Pre-flight: validate any --name before generating the CLI command

Whichever reference you load, most end up producing an agentcore add <resource> --name <something> command. The CLI fails late on invalid names — you'll see the error after walking through prompts, not before running the command. Validate up front:

ResourceMax charsAllowedStarts with
Agent (add agent)48alphanumeric + _letter
Memory, gateway, gateway-target, credential, evaluator, online-eval, policy, policy-engine, payment-manager, payment-connector48alphanumeric + _letter

Count the characters before constructing the command. If the name is over the limit or contains hyphens, dots, or spaces, push back: "<name> is N characters / uses -, which the CLI rejects. How about <suggestion>?" Never run the command with an invalid name hoping the CLI message will be clear.

Note: agentcore create --name (the project name) has a stricter 23-char limit and does not allow underscores. That's covered in agents-get-started; if you see the developer re-running create, flag the 23-char limit specifically.

Output

Depends on the workflow — see the loaded reference for specific outputs.

Quality criteria

  • The correct reference was loaded based on the developer's intent

  • All output follows the loaded reference's quality criteria

  • Cross-references to other skills (agents-connect, agents-deploy) are included where relevant

Files

references/local-vs-deployed.md

Local vs. Deployed — What Works Where

AgentCore has a local dev server (agentcore dev) and a deployed runtime. They don't have feature parity. This reference tells you what works where so generated code and troubleshooting handle both environments correctly.

Quick reference

Featureagentcore dev (local)Deployed runtime
Agent invocation✅ via curl on localhost:8080✅ via invoke_agent_runtime or HTTPS
Framework model calls✅ if Bedrock creds are available✅
Python/JS function tools (framework-native)✅✅
Credentials (@requires_api_key, @requires_access_token)✅ from agentcore/.env.local✅ from Secrets Manager
Memory❌ env var not set locally✅ MEMORY_<NAME>_ID injected
Gateway❌ env var not set locally✅ AGENTCORE_GATEWAY_<NAME>_URL injected
Cedar policy evaluation❌ policies only enforced at gateway✅
Traces (X-Ray)✅ agentcore dev emits OTEL to CloudWatch by default; disable with --no-traces✅ auto-enabled
CloudWatch logs✅ via ADOT / OTEL wiring (same path as traces)✅ if using logging module + OTEL
Evaluator definition (agentcore add evaluator, writing the instructions/code)✅ — writes to agentcore.json; custom code is unit-testable locally✅
agentcore run eval (on-demand eval over traces)✅ — operates on CloudWatch spans; local-dev spans land there if OTEL is on (default)✅
Evaluate API with hand-constructed spans (boto3)✅ — no runtime needed at all; submit SessionSpans directly✅
Dataset runner (OnDemandEvaluationDatasetRunner)❌ invokes an AgentCore Runtime agent in its pipeline✅
Online eval monitoring (agentcore add online-eval)❌ ingests traces continuously from deployed runtime✅
Observability dashboards✅ once Transaction Search is on and local spans are flowing✅ in CloudWatch console
VPC networking❌ local always has internet✅ subject to networkMode: VPC
Inbound auth (AWS_IAM, CUSTOM_JWT)❌ no auth required locally✅ enforced on every request

Implications for generated code

Always guard features that aren't available locally:

# Memory pattern
MEMORY_ID = os.getenv("MEMORY_MYMEMORY_ID")
if MEMORY_ID:
    # deployed — wire up memory
    session_manager = AgentCoreMemorySessionManager(...)
else:
    # local — agent runs without memory
    session_manager = None
# Gateway pattern
GATEWAY_URL = os.getenv("AGENTCORE_GATEWAY_WEATHER_URL")
if GATEWAY_URL:
    # deployed — use gateway tools
    tools = get_gateway_tools(GATEWAY_URL)
else:
    # local — agent runs without external tools or with local stubs
    tools = []

Credentials work in both, but read from different sources. The @requires_api_key decorator handles this automatically — don't try to read env vars directly.

Testing workflow

Because memory, gateway, and policies don't work locally, the realistic test loop is:

  1. Local: agentcore dev to verify the agent's code structure, framework wiring, system prompt, and any in-code logic

  2. Deploy to a staging target: agentcore deploy --target staging to test with real memory, gateway, and policies

  3. Production: only after staging validation

Don't expect agentcore dev to reproduce a production failure involving memory recall, gateway tool calls, or policy denials — those require a deployed environment.

Common "works locally, fails deployed" causes

  • Missing MEMORY_<NAME>_ID guard — code crashes because the env var is unexpectedly present

  • Hardcoded localhost URLs for gateway — replace with AGENTCORE_GATEWAY_<NAME>_URL

  • IAM permissions that work for your dev credentials but not the execution role

  • Region mismatch between aws configure (used locally) and aws-targets.json (used in deploy)

  • Tool call auth that works with your personal credentials but not with gateway SigV4 from the execution role

Common "works deployed, fails locally" causes

  • Code that assumes memory/gateway env vars are always set

  • Direct SDK calls that expect the deployed execution role's permissions

  • Hardcoded deployed-only URLs or ARNs

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs an MCP server, Needs Python

Security

What our scanner noticed· Low risk

  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
aws-agentcore
Publisher
AWS (Official)
Source
aws/agent-toolkit-for-aws · plugins/aws-agents/skills/agents-build · commit 0a218f8
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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