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/browser.md
browser
Add the AgentCore Browser tool so your agent can navigate web pages, fill forms, and extract information.
When to use
Your agent needs to interact with a website that has no API
Your agent needs to fill forms, scrape data, or drive a web app
You want an isolated, session-scoped browser for the agent (not a shared one)
You want live-view / recording / replay of what the browser did, for debugging or auditing
Do NOT use this reference for:
Calling an API — use Gateway (
agents-connect)Running code in a sandbox — see
code-interpreter.mdServing browser-based UIs to users — that's a different problem (the AGUI protocol, not the Browser tool)
Mental model
The Browser tool is a managed Chrome instance, one per session, running in an isolated microVM. Your agent connects to it over WebSocket (via CDP — Chrome DevTools Protocol) and drives it with an automation framework. You pick the framework:
| Framework | When to use |
|---|---|
Strands AgentCoreBrowser | Agent-driven browsing inside a Strands agent. Highest-level, tool-use-native. |
| Nova Act | You want an LLM to decide the next action at each step ("click the search box, type X, press enter"). Best for open-ended tasks. |
| Playwright | Deterministic scripted automation. Best when you know the exact steps — login flows, scraping a known page structure. |
If you're adding browsing to a Strands agent, use AgentCoreBrowser and skip the framework decision — it wraps Nova Act under the hood and fits the agent-tool mental model.
If you're not using Strands, pick between Nova Act (reasoning-driven) and Playwright (script-driven) based on whether the task is open-ended or well-defined.
Sessions are ephemeral by default (reset after each use). Default timeout is 15 minutes, max 8 hours. You can run multiple concurrent sessions.
Prerequisites
Python 3.10+
bedrock-agentcoreSDK installedIAM permissions for
bedrock-agentcore:*Browser*actions (scope to your browser resource ARN in production)AWS region that supports Browser — check the docs for the current list
For Strands path: model access for your chosen model (Claude Sonnet 4.x is the common default)
For Nova Act path: a Nova Act API key from nova.amazon.com/act (US-based amazon.com accounts only at time of writing)
IAM policy skeleton (attach to the caller identity — your user, role, or AgentCore Runtime execution role):
{
"Version": "2012-10-17",
"Statement": [{
"Sid": "BrowserAccess",
"Effect": "Allow",
"Action": [
"bedrock-agentcore:CreateBrowser",
"bedrock-agentcore:GetBrowser",
"bedrock-agentcore:ListBrowsers",
"bedrock-agentcore:StartBrowserSession",
"bedrock-agentcore:StopBrowserSession",
"bedrock-agentcore:GetBrowserSession",
"bedrock-agentcore:ListBrowserSessions",
"bedrock-agentcore:ConnectBrowserAutomationStream",
"bedrock-agentcore:ConnectBrowserLiveViewStream"
],
"Resource": "arn:aws:bedrock-agentcore:<REGION>:<ACCOUNT_ID>:browser/*"
}]
}Check current IAM action names against the docs — the list evolves.
Path A — Strands agent with the Browser tool (recommended for most)
from strands import Agent
from strands_tools.browser import AgentCoreBrowser
browser_tool = AgentCoreBrowser(region="<REGION>")
agent = Agent(tools=[browser_tool.browser])
result = agent("Find the release date of the latest AgentCore SDK on GitHub.")
print(result.message["content"][0]["text"])Install: pip install bedrock-agentcore strands-agents strands-agents-tools
The agent decides when to use the browser, opens sessions on demand, and cleans them up. Under the hood, AgentCoreBrowser uses the AWS-managed aws.browser.v1 resource — no resource creation needed.
Dropping into an AgentCore Runtime entrypoint:
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from strands import Agent
from strands_tools.browser import AgentCoreBrowser
from model.load import load_model # scaffolded by `agentcore create`
import os
app = BedrockAgentCoreApp()
REGION = os.getenv("AWS_REGION", "us-west-2")
@app.entrypoint
def invoke(payload, context):
browser_tool = AgentCoreBrowser(region=REGION)
agent = Agent(model=load_model(), tools=[browser_tool.browser])
result = agent(payload.get("prompt", ""))
return {"response": str(result)}
if __name__ == "__main__":
app.run()Path B — Nova Act for reasoning-driven tasks
Use when the task needs an LLM to decide each click/type step.
from bedrock_agentcore.tools.browser_client import browser_session
from nova_act import NovaAct
def run_browser_task(prompt: str, starting_page: str, nova_act_key: str, region: str = "us-west-2"):
with browser_session(region) as client:
ws_url, headers = client.generate_ws_headers()
with NovaAct(
cdp_endpoint_url=ws_url,
cdp_headers=headers,
nova_act_api_key=nova_act_key,
starting_page=starting_page,
) as nova:
return nova.act(prompt)Install: pip install bedrock-agentcore nova-act boto3
The browser_session context manager handles start/stop. Do not leak sessions — always use the context manager or wrap raw BrowserClient calls in try/finally.
Credential handling: the Nova Act API key is a secret. If this is running inside an AgentCore Runtime agent, register it as a credential (agentcore add credential --name NovaAct --api-key ...) and retrieve it with @requires_api_key(provider_name="NovaAct"). Do not put it in runtime env vars. See agents-connect Path D.
Path C — Playwright for scripted automation
Use when the steps are fixed and you want deterministic behavior (logins, scrapes, automated tests).
import asyncio
from bedrock_agentcore.tools.browser_client import browser_session
from playwright.async_api import async_playwright
async def scrape_title(url: str, region: str = "us-west-2") -> str:
async with async_playwright() as pw:
with browser_session(region) as client:
ws_url, headers = client.generate_ws_headers()
browser = await pw.chromium.connect_over_cdp(ws_url, headers=headers)
context = browser.contexts[0]
page = context.pages[0]
try:
await page.goto(url)
return await page.title()
finally:
await page.close()
await browser.close()
print(asyncio.run(scrape_title("https://example.com")))Install: pip install bedrock-agentcore playwright
Sync variant (sync_playwright) is also supported — pick based on whether your agent code is async.
Observability
Browser is observable by default:
Live view — watch a running session in real time from the AWS console (Built-in tools → Browser → your session → "View live session"). You can take over control from the automation interactively.
CloudWatch logs —
/aws/bedrock-agentcore/browser/*Metrics — in
AWS/BedrockAgentCorenamespace
Session recording (DOM, clicks, console logs, network) is opt-in per browser. To enable:
Create a custom browser (not
aws.browser.v1) with recording configuredGive its execution role
s3:PutObjecton your recording bucketRecordings land in your S3 bucket and replay in the AWS console
The managed aws.browser.v1 resource does not record. Use custom browsers when you need audit trails.
Session lifecycle — always close
# Right — context manager
with browser_session(region) as client:
ws_url, headers = client.generate_ws_headers()
...
# Also right — explicit try/finally
client = BrowserClient(region=region)
client.start()
try:
...
finally:
client.stop()
# Wrong — leaked session
client = BrowserClient(region=region)
client.start()
... # if this raises, the session sits idle until its 15-minute timeoutSessions hold a microVM. Leaked sessions cost money until they time out. The context manager is non-negotiable for production.
VPC mode
If your agent runs in VPC mode, the Browser tool can also run in VPC. See vpc.md for the subnet + security group pattern (the same service-linked role covers Browser ENIs). Browser in VPC requires a NAT gateway for public-internet sites — public subnets don't give Browser internet access.
Common failures
"Access denied" starting a session: IAM is missing StartBrowserSession on the browser resource ARN. Check aws sts get-caller-identity matches the identity you attached the policy to.
"Model access denied" from a Strands agent: The browser tool itself is fine, but the agent's model isn't enabled. Go to Bedrock console → Model access → enable your model in the region.
Nova Act errors about API key: The key is US-amazon.com-accounts only at launch. If you're outside the US or using a work account, you can't use Nova Act yet — fall back to Playwright or Strands.
Browser session times out mid-task: Default is 15 minutes of idle time. Pass sessionTimeoutSeconds to StartBrowserSession (max 28800 = 8 hours). Don't use this to cover up agents that are slow — fix the agent or chunk the work.
Live view doesn't show your session: Live view requires ConnectBrowserLiveViewStream IAM permission. The session also has to be Ready, not Starting or Stopping.
Output
Which framework fits (Strands vs Nova Act vs Playwright)
Working code with session lifecycle handled
IAM policy scoped to the browser resource
Observability setup if needed (live view, recording)
Quality criteria
Browser sessions are always wrapped in a context manager or try/finally — never leaked
IAM is scoped to
browser/*in the account, notResource: "*"Nova Act API keys and other secrets use
agentcore add credential+@requires_api_key, not env varsThe code handles the case where the agent runs outside AgentCore Runtime (no
.env.local, no credential provider) — typically by reading a local secret for development and the credential provider for production
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