Agents Build
AWSOfficialCreated by aws-agentcore
- Skill
- Workflow
- Cloud and DevOps
- Apache-2.0
- Low risk
- Has scripts
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/aws/agents-build/downloadUnzip 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-connectScaffolding a new project → use
agents-get-startedDeploying → 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:
Check if the developer is in the wrong directory. Look for
agentcore/agentcore.jsonin parent directories (up to 3 levels). If found, tell them: "Found an AgentCore project at<path>. Are you working in that project?"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,
cdinto 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 buildIf the developer says something is broken (wrong answers, errors, tool failures) → this is
agents-debug, not buildBuild 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 intent | Reference to load |
|---|---|
| Add memory, remember things, user preferences, cross-session | references/memory.md |
| Call agent from app, invoke from code, streaming, SDK client, agent URL, execute shell in session | references/integrate.md |
| VPC, private network, RDS, internal API, subnet, security group | references/vpc.md |
| Multi-agent, orchestrator, specialist, A2A, delegation, agent handoff | references/multi-agent.md |
| Custom headers from caller to agent, header allowlist, tenant ID/correlation ID/trace propagation | references/request-headers.md |
| Migrate Bedrock Agent, import agent, move to AgentCore | references/migrate.md |
| Browser tool, web navigation, form filling, scraping, Nova Act, Playwright, live view | references/browser.md |
| Code Interpreter, execute code, sandbox, run Python/JS/TS, data analysis in agent, pandas | references/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/connector | references/payments.md |
| Delete agent, remove resource, tear down, clean up, destroy, start fresh | references/teardown.md |
| Change model, switch model, use Haiku/Sonnet/Nova, different model | Inline — 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:
Open
app/<AgentName>/model/load.pyChange the
model_idparameter in theBedrockModel()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 ARNRun
agentcore devto test locally, thenagentcore deployto 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:
| Resource | Max chars | Allowed | Starts with |
|---|---|---|---|
Agent (add agent) | 48 | alphanumeric + _ | letter |
| Memory, gateway, gateway-target, credential, evaluator, online-eval, policy, policy-engine, payment-manager, payment-connector | 48 | alphanumeric + _ | 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
- ATTRIBUTION.mdAdded by AutoGineer1011 B
- LICENSELicence file added by AutoGineer9.9 KiB
- NOTICELicence file added by AutoGineer67 B
- SKILL.md8.9 KiB
- references/browser.md10.0 KiB
- references/code-interpreter.md8.9 KiB
- references/integrate.md19.1 KiB
- references/local-vs-deployed.md4.2 KiB
- references/memory.md27.7 KiB
- references/migrate.md5.5 KiB
- references/multi-agent.md16.2 KiB
- references/payments.md71.4 KiB
- references/request-headers.md6.8 KiB
- references/teardown.md5.3 KiB
- references/vpc.md12.5 KiB
- scripts/process_payment_tool.pyScript7.0 KiB
- scripts/setup_payment_user.pyScript5.3 KiB
references/migrate.md
migrate
Move an existing Amazon Bedrock Agent to AgentCore Runtime.
When to use
You have an existing Bedrock Agent (created via the Bedrock console or API) and want to run it on AgentCore Runtime
You want to add AgentCore capabilities (Memory, Gateway, Observability) to an existing agent
You want to move from the declarative Bedrock Agents model to a code-first framework
Input
$ARGUMENTS is optional:
/migrate # interactive — walks through the migration
/migrate strands # migrate targeting Strands framework
/migrate langgraph # migrate targeting LangGraph frameworkWhat migration does
The agentcore create --type import command reads your existing Bedrock Agent's configuration and generates an AgentCore project that reproduces its behavior in a code-first framework. Specifically:
System prompt → copied into the generated
main.pyAction groups (Lambda-backed) → converted to Gateway targets with
--type lambda-function-arnKnowledge bases → referenced in the system prompt with a note to wire retrieval manually (AgentCore doesn't auto-import KB bindings)
Guardrails → noted in comments but not auto-converted (AgentCore uses Cedar policies, not Bedrock Guardrails)
Agent alias / version → the import targets a specific alias, not the draft
What migration does not do:
It does not delete or modify the original Bedrock Agent — the source agent keeps running
It does not migrate conversation history or session state
It does not convert Bedrock Guardrails to Cedar policies (different authorization model)
It does not auto-wire Knowledge Base retrieval — you'll need to add that as a tool or direct SDK call
Prerequisites
The Bedrock Agent must exist and have at least one alias
Your AWS credentials must have
bedrock:GetAgent,bedrock:GetAgentAlias, andbedrock:ListAgentActionGroupspermissionsYou need the agent ID, alias ID, and region
Process
Step 1: Run the import
agentcore create \
--type import \
--agent-id <AGENT_ID> \
--agent-alias-id <ALIAS_ID> \
--region <REGION> \
--name <ProjectName> \
--framework StrandsThe --framework flag determines which code-first framework the generated project uses. Strands is recommended for the closest mapping to Bedrock Agent behavior.
Project name rules apply: max 23 characters, alphanumeric only, starts with a letter.
Step 2: Review the generated project
cd <ProjectName>
cat app/<AgentName>/main.py
cat agentcore/agentcore.jsonCheck:
The system prompt matches your original agent's instructions
Action groups appear as Gateway targets in
agentcore.json(underagentCoreGateways)The model ID is correct for your target region
Step 3: Fill in what migration doesn't cover
Knowledge Bases: If your Bedrock Agent used Knowledge Bases, you have two options:
Keep using the KB via boto3 — call
bedrock-agent-runtime:RetrieveAndGenerateorRetrievedirectly from your agent code as a toolReplace with AgentCore Memory — if the KB was used for user-specific context, AgentCore Memory with SEMANTIC strategy may be a better fit. See memory.md.
Guardrails → Cedar policies: Bedrock Guardrails (content filters, denied topics, word filters) don't have a 1:1 mapping to Cedar policies. Cedar policies control which tools the agent can call and with what parameters — they're authorization rules, not content filters. If you need content filtering, keep the guardrail logic in your agent code (pre/post-processing) or use Bedrock Guardrails as a standalone API call.
Custom orchestration: If your Bedrock Agent used custom orchestration (return-of-control, custom Lambda orchestrators), you'll need to rebuild that logic in the framework's native patterns — Strands tool chains, LangGraph graph nodes, etc.
Step 4: Test locally and deploy
# Test locally (memory and gateway won't be available yet)
agentcore dev
# Deploy when ready
agentcore deploy -y
# Verify
agentcore invoke "Hello, what can you do?"
agentcore statusStep 5: Cut over traffic
Once the AgentCore agent is working correctly:
Update your application to invoke the AgentCore Runtime instead of the Bedrock Agent
See integrate.md for the invocation patterns (SigV4, JWT, SDK)
Keep the original Bedrock Agent running as a fallback until you're confident
Delete the Bedrock Agent only after the AgentCore agent has been stable in production
Common migration issues
"Model not available in target region"
The imported agent may reference a model ID that isn't available in your AgentCore deployment region. Edit model/load.py to use a cross-region inference profile or a model available in your region.
"Action group Lambda in a different region" Gateway targets can invoke Lambda functions cross-region, but latency increases. Consider deploying the Lambda in the same region as your AgentCore agent, or accept the latency trade-off.
"Agent behavior differs after migration" The most common cause is prompt format differences between Bedrock Agent's orchestration and the code-first framework. Bedrock Agent injects structured XML around tool results; Strands/LangGraph use different formats. Tune the system prompt to compensate.
Output
A working AgentCore project that reproduces the Bedrock Agent's behavior
A list of what was auto-converted and what needs manual work
Guidance on cutting over traffic from the old agent to the new one
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
AutoGineer is not affiliated with or endorsed by aws-agentcore or AWS.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download agents-build.zip