FAST: Fullstack AgentCore Solution Template for AI Agent Applications

What Problem FAST Solves and Who It Targets

Building a full-stack application on Amazon Bedrock AgentCore from scratch requires wiring together a React frontend, Cognito authentication, API Gateway, CDK or Terraform infrastructure, and the AgentCore runtime itself. Each piece is non-trivial, and the combination can take a small team several weeks before any agent logic is written. FAST packages that boilerplate into a fork-and-deploy starting point.

The intended users are delivery scientists and engineers who need to build an AgentCore application for a specific use case, and who do not want to spend their time on frontend or infrastructure code. The README describes the philosophy as making FAST 'vibe-codable': best practices are written into the documentation in the repository so an AI coding assistant can read them and generate correct customizations, rather than baking them into the code itself. The project is agnostic to the agent SDK: both Strands Agents and LangGraph are supported as optional dependency sets.

Deploying the Baseline Application with CDK

Once you have forked the repository and configured your AWS credentials, deploying the full-stack baseline is a sequence of four commands inside the infra-cdk/ directory, followed by a Python script that publishes the frontend:

cd infra-cdk
npm install
cdk bootstrap
cdk deploy

The `cdk bootstrap` step runs once per AWS account and region. `cdk deploy` provisions all backend infrastructure including the AgentCore runtime, API Gateway, Cognito User Pool, and Amplify hosting for the frontend. After the CDK deployment finishes, the frontend is published with:

python scripts/deploy-frontend.py

The detailed deployment guide is in docs/DEPLOYMENT.md. For teams preferring Terraform, an infra-terraform/ directory provides the equivalent deployment. The README recommends choosing one infrastructure tool and deleting the other directory from your fork.

Authentication Architecture: Four Cognito Flows

The baseline application implements four authentication flows that cover the full request path from browser to agent tool.

Flow 1 handles user login to the frontend using the Cognito Authorization Code grant. The user authenticates through the Amplify-hosted web app, and Cognito issues a JWT access token.

Flow 2 covers the frontend to AgentCore Runtime call: the frontend passes the user's JWT in the Authorization header, and the Runtime validates it against the Cognito User Pool.

Flow 3 is machine-to-machine (M2M) authentication between the AgentCore Runtime and the AgentCore Gateway. It uses the OAuth2 Client Credentials grant, with user identity propagated into the M2M token via a Cognito V3 Pre-Token Lambda. The Gateway then evaluates Cedar policies against the user's claims to enforce fine-grained access control.

Flow 4 handles direct API Gateway calls from the frontend, also validated using the same Cognito User Pool JWT from Flow 1.

This four-flow design is codified in the template so teams inheriting it get a documented authentication pattern without having to design it from scratch.

Baseline Tools: Gateway Tools and the Code Interpreter

The out-of-the-box baseline agent ships with two categories of tools. The first category, Gateway Tools, are Lambda functions behind AgentCore Gateway with authentication. The default tool in this category is a text analysis tool that counts words and letter frequency. These tools demonstrate the Gateway integration pattern without being specific to any real use case.

The second category is a direct integration with the Amazon Bedrock AgentCore Code Interpreter. It provides secure Python code execution in an isolated sandbox, session management with state persistence, and a pre-built runtime with common libraries. The README suggests asking the baseline agent to execute Python code to see this integration in action.

Both tools exist as minimal, replaceable examples. The README makes clear that the baseline is 'intentionally kept very, very simple' and is the starting point, not the destination. Customizing these tools for a specific domain, or replacing them entirely, is the expected path.

Vibe-Coding and AI Coding Assistant Integration

FAST is designed around the assumption that developers will use AI coding assistants to do most of the frontend and infrastructure customization. The repository includes assistant-specific configuration directories: .amazonq/ for Amazon Q rules, .kiro/ for Kiro, and .clinerules for Cline. The vibe-context/ directory at the top level holds documentation written specifically to be included in an AI coding assistant's context window.

The pattern is: open the coding assistant, describe what you want the agent to do, and let it read the documentation in the repository to generate correct implementations. This keeps the framework's best practices in documentation rather than code, which means they can be updated without breaking existing forks and can be adjusted by anyone comfortable with Markdown.

Python 3.10 through 3.13 are supported. The pyproject.toml defines separate optional dependency groups for local development: agent-strands installs strands-agents and bedrock-agentcore with Strands support, agent-langgraph installs langgraph and its dependencies, and dev installs pytest, ruff, and mypy.

Limitations: Proof-of-Value, Not Production-Ready

The README contains a direct caveat that is easy to miss: 'this asset represents a proof-of-value for the services included and is not intended as a production-ready solution.' The README also states that the team must determine how the AWS Shared Responsibility model applies to their specific use case and implement the needed controls themselves.

The pyproject.toml classifies the project as Development Status :: 4 - Beta. There are no GitHub releases. The repository's CHANGELOG.md and CONTRIBUTORS.md indicate it is actively evolving, with the last push on 2026-09-16.

Practical limitations: the baseline agent is a simple multi-turn chat agent, and adapting it to a domain-specific use case requires understanding the AgentCore SDK. The text analysis Gateway Tool is a placeholder with no real utility. Teams expecting a production-hardened infrastructure module will need to extend the template substantially, including adding monitoring, logging, WAF rules, and environment-specific configuration management.

Comparison with Writing from Scratch and CDK Constructs

The nearest alternative for teams building on AgentCore is starting from the official AWS CDK Constructs library for Bedrock (aws-cdk-lib/aws-bedrock) and wiring the React frontend manually. This approach gives full control and no inherited design decisions, but requires the developer to understand each integration point: Cognito flows, Amplify hosting, AgentCore Gateway Cedar policies, and the agent SDK. FAST collapses that into a working baseline that a team can fork and override incrementally.

A second alternative is using AWS Amplify Gen 2 with a custom backend, which is better suited for teams with an existing Amplify workflow. FAST is specifically tied to AgentCore as its central dependency; teams not building on AgentCore have no reason to use it.

Editorial conclusion

Delivery teams building proof-of-concept or pilot applications on Amazon Bedrock AgentCore will find FAST a useful starting point: it removes the infrastructure boilerplate and lets an AI coding assistant drive most of the customization. The README explicitly states that FAST is a proof-of-value asset and is not intended as a production-ready solution, so teams moving toward production must apply additional security and operational controls beyond what the template provides. Before forking, confirm your AWS account has AgentCore access enabled, review the DEPLOYMENT.md guide, and decide between CDK and Terraform so you can delete the other infrastructure directory from your fork.

Frequently asked questions

Official sources

1. awslabs/fullstack-solution-template-for-agentcore on GitHub
2. Issues
3. License: Apache-2.0
4. Project website
5. README