An Agentic AI Product Manager isn't just someone who talks about AI agents—they build them. They understand the technical architecture, can implement working prototypes, and bridge the gap between AI capabilities and real business problems.
Traditional PM skills (user research, roadmaps, stakeholder management) remain essential, but agentic AI adds a new dimension: you need to think in terms of autonomous systems that reason, use tools, and make decisions. You're not just defining features—you're designing agent behaviors, tool ecosystems, and orchestration patterns.
This repo represents that philosophy: theory backed by working code.
I built this playground after noticing a gap in how agentic AI is presented online. Most resources fall into two camps:
- Academic papers - Fascinating research, but disconnected from production reality
- Marketing content - Bold claims about what agents "can do," with no code to back it up
What was missing? Working implementations that you can run, modify, and deploy.
I wanted to create something that would help me (and others) move from "I understand agents conceptually" to "I've built and deployed agent systems." Each demo solves a real problem with actual code—not pseudocode, not architecture diagrams, but Python you can execute locally or deploy to AWS.
The playground is organized into five categories:
Learn the major frameworks by building the same multi-agent workflow three different ways:
- LangGraph - State-based orchestration with conditional routing
- CrewAI - Role-based agents working as a crew
- AutoGen - Conversational agents with code generation
These aren't toy examples. Each implements a researcher → writer → reviewer pipeline that actually produces content.
AWS Bedrock AgentCore provides infrastructure for production agents. These demos show how to use it:
- Gateway Integration - Connect agents to external tools and APIs
- Memory Demo - Maintain conversation context across sessions
- Code Interpreter - Let agents write and execute code safely
- Browser Tool - Enable agents to interact with web pages
Real business problems solved with agents:
- Customer Support - Multi-agent system with routing, FAQ lookup, and escalation
- DevOps Agent - Monitor infrastructure, analyze logs, suggest fixes
- Data Analysis - Natural language to SQL queries and insights
Connect agents to your existing systems:
- Slack Bot - Deploy agents where your team already works
- API Gateway - Expose agents as REST APIs with auth and rate limiting
- EventBridge - Trigger agents from AWS events (S3 uploads, CloudWatch alarms)
Production-grade patterns for serious deployments:
- RAG Knowledge Base - Semantic search with source citations
- Multi-Modal Agent - Process images and text together
- Evaluation Framework - Test agent quality systematically
- Policy Controls - Fine-grained access control with Cedar policies
Plus three practical applications: Meeting Assistant, Code Review Agent, and Research Assistant.
Start with the framework demos. Run them locally, see how agents make decisions, modify the prompts, add new tools. The code is commented and structured to be readable.
Each README includes:
- Quick start commands
- Expected output
- How it works explanations
- Customization examples
Use these as templates. The patterns are production-ready:
- Error handling
- Logging and monitoring
- Security best practices
- Deployment instructions (Docker, Lambda, ECS)
Copy the code, adapt it to your use case, deploy it.
Run the demos to understand what's actually possible vs. marketing hype. See how different frameworks compare. Use the evaluation framework to test agent quality systematically.
# Install Python 3.8+
python --version
# Install dependencies (each demo has its own requirements.txt)
pip install bedrock-agentcore strands-agents- Clone the repo:
git clone https://github.com/ndgbg/agentic-playground.git
cd agentic-playground- Pick a demo (I recommend starting with Gateway Integration):
cd agentcore-features/gateway-integration
pip install -r requirements.txt
python gateway_agent.py- See it work:
Gateway Integration Demo
============================================================
Query: What's the weather in Seattle?
Response: Weather in Seattle: Rainy, 52°F
Query: Search for customer Alice
Response: Found customer: Alice Smith (Premium, 2 orders)
============================================================
- Modify it:
Open
gateway_agent.py, add your own tool:
@tool
def your_custom_tool(param: str) -> str:
"""Your tool description"""
# Your logic here
return resultThe agent will automatically discover and use your tool.
Each demo includes deployment instructions. For example, to deploy to AWS Lambda:
# Package
pip install -r requirements.txt -t package/
cp gateway_agent.py package/
cd package && zip -r ../function.zip .
# Deploy
aws lambda create-function \
--function-name my-agent \
--runtime python3.11 \
--handler gateway_agent.invoke \
--zip-file fileb://function.zipEvery demo has been tested. You can run them locally right now. No "coming soon" or "conceptual example" disclaimers.
These aren't scripts with if/else logic pretending to be agents. They use:
strands.Agentwith tool calling- LangGraph with state management
- CrewAI with role-based collaboration
- AutoGen with conversational agents
The agents reason about user requests and autonomously choose which tools to invoke.
Each includes:
- Error handling
- Input validation
- Security considerations
- Deployment guides
- Best practices
Comprehensive READMEs explain:
- What the demo does
- How it works
- How to customize it
- How to deploy it
- Common pitfalls
These patterns power real systems:
Customer Support: Companies use multi-agent systems to handle tier-1 support, routing complex issues to humans only when needed.
DevOps: Teams deploy agents that monitor infrastructure, analyze logs, and suggest fixes—reducing MTTR from hours to minutes.
Data Analysis: Business users query databases in natural language, getting insights without SQL knowledge.
Code Review: Development teams use agents to catch security issues and style violations before human review.
I believe the best way to learn is by doing. Reading about agents is useful, but running agent code, breaking it, fixing it, and deploying it—that's how you truly understand them.
This repo embodies that philosophy:
- Minimal but complete - Each demo is focused but fully functional
- Practical over theoretical - Real problems, real solutions
- Code over slides - Working implementations, not architecture diagrams
- Production-ready - Patterns you can actually deploy
I'm continuing to add demos as I explore new patterns and use cases. Some ideas in progress:
- Multi-agent debate systems
- Agent-to-agent communication patterns
- Long-running agent workflows
- Human-in-the-loop patterns
But the core principle remains: every addition must be a working implementation you can run and deploy.
Found a bug? Have an idea for a new demo? Contributions welcome! The goal is to build a comprehensive resource for anyone building agentic AI systems.
Don't just read about agentic AI—build it:
- Clone the repo:
git clone https://github.com/ndgbg/agentic-playground.git - Pick a demo that matches your use case
- Run it locally
- Modify it for your needs
- Deploy it to production
The gap between understanding agents conceptually and building them in production is smaller than you think. These 20 demos are your bridge across that gap.
Repository: github.com/ndgbg/agentic-playground
More Resources:
- Agentic AI Insights - Strategic content on use cases and patterns
- LinkedIn - Connect with me
Built by an Agentic AI Product Manager who believes the best documentation is working code.