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/teardown.md
teardown
Remove individual resources from your project or tear down the entire deployment.
When to use
You want to remove a gateway, memory, credential, evaluator, or other resource from your project
You want to delete a deployed agent and clean up all AWS resources
You're iterating in a sandbox account and want to start fresh
You need to remove a resource that's stuck or no longer needed
Process
Removing individual resources from your project
Use agentcore remove to remove a resource from agentcore.json. This marks the resource for deletion — the actual AWS resource is removed on the next agentcore deploy.
# Remove a memory resource
agentcore remove memory --name MyMemory
# Remove a gateway target
agentcore remove gateway-target --name WeatherTools --gateway MyGateway
# Remove a gateway (remove all its targets first)
agentcore remove gateway --name MyGateway
# Remove a credential
agentcore remove credential --name MyAPIKey
# Remove an evaluator
agentcore remove evaluator --name ResponseQuality
# Remove an online eval config
agentcore remove online-eval --name production_monitor
# Remove a policy
agentcore remove policy --name SpendingLimit --engine MyPolicyEngine
# Remove a policy engine (remove all its policies first)
agentcore remove policy-engine --name MyPolicyEngineAfter removing, deploy to apply the changes:
agentcore deploy -yCheck what's pending removal before deploying:
agentcore status --state pending-removalRemoving an agent from a multi-agent project
If your project has multiple agents (runtimes), you can remove one:
agentcore remove agent --name SecondAgent
agentcore deploy -yThis deletes the agent's runtime, endpoint, and associated resources from AWS. The agent's code in app/<AgentName>/ is not deleted — remove it manually if you no longer need it.
Tearing down the entire deployment
To remove all deployed AWS resources for a project:
# Preview what will be destroyed
agentcore deploy --diff
# Destroy all resources
npx cdk destroy --app "npx ts-node agentcore/cdk/bin/cdk.ts" --forceAlternatively, delete the CloudFormation stack directly:
# Find the stack name
aws cloudformation list-stacks \
--stack-status-filter CREATE_COMPLETE UPDATE_COMPLETE \
--query "StackSummaries[?contains(StackName, '<ProjectName>')].StackName"
# Delete it
aws cloudformation delete-stack --stack-name <StackName>
# Wait for deletion to complete
aws cloudformation wait stack-delete-complete --stack-name <StackName>What gets deleted and what doesn't
| Resource | Deleted by cdk destroy | Notes |
|---|---|---|
| AgentCore Runtime(s) | ✅ | Includes all endpoints and versions |
| Memory resource(s) | ✅ | Memory data is deleted permanently |
| Gateway(s) and targets | ✅ | |
| Credentials | ✅ | Secrets Manager entries are removed |
| Policy engine(s) and policies | ✅ | |
| Evaluator definitions | ✅ | |
| Online eval configs | ✅ | |
| IAM roles | ✅ | Created by CDK |
| CloudWatch log groups | ❌ | Persist after deletion — delete manually if needed |
| ECR images (Container builds) | ❌ | Persist — delete the repository manually |
| CDK bootstrap stack | ❌ | Shared across projects — don't delete unless you're done with CDK entirely |
| Local project files | ❌ | agentcore/, app/ — delete manually |
Cleaning up CloudWatch log groups
Log groups persist after stack deletion. To clean them up:
# List AgentCore log groups
aws logs describe-log-groups \
--log-group-name-prefix /aws/bedrock-agentcore/ \
--query "logGroups[].logGroupName"
# Delete a specific log group
aws logs delete-log-group --log-group-name /aws/bedrock-agentcore/runtimes/<AGENT_ID>-DEFAULTCleaning up ECR repositories (Container builds)
# List AgentCore ECR repositories
aws ecr describe-repositories \
--query "repositories[?contains(repositoryName, 'bedrock-agentcore')].repositoryName"
# Delete a repository and all its images
aws ecr delete-repository --repository-name <repo-name> --forceHandling stuck resources
If a runtime is stuck in DELETING state for more than 30 minutes, see the "Runtime stuck in DELETING" section in agents-debug. The short version: don't keep retrying — open an AWS Support case with the runtime ARN and the original delete request ID from CloudTrail.
Common issues
"Can't remove gateway — targets still attached" Remove all gateway targets first, then remove the gateway:
agentcore remove gateway-target --name Target1 --gateway MyGateway
agentcore remove gateway-target --name Target2 --gateway MyGateway
agentcore remove gateway --name MyGateway"Can't remove policy engine — policies still attached" Remove all policies first, then remove the engine:
agentcore remove policy --name Policy1 --engine MyEngine
agentcore remove policy-engine --name MyEngine"Resource shows pending-removal but deploy doesn't delete it"
Check agentcore status --state pending-removal and verify the resource is listed. If deploy completes without removing it, check the CDK output for errors — the deletion may have failed silently due to a dependency.
Output
CLI commands to remove the specific resource(s)
Guidance on what persists after deletion and how to clean it up
Warnings about irreversible data loss (memory data, credentials)
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