AI agents are moving from demos to production systems. However, most developers face challenges bridging this gap. This learning path shows you how.
Interest in AI agents continues to grow in 2025. Developers are building autonomous systems that reason, use tools, and make decisions. However, McKinsey’s “The State of AI 2025" survey reveals that no more than 10% of respondents report scaling AI agents in any individual business function. The gap between building a demo and deploying a production-ready agent remains significant.
If you want to move beyond proof-of-concept demos and build AI agents that work at scale, a comprehensive learning path can help. This four-course series takes you from fundamentals to production deployment, and it’s free.
Why Most AI Agents Never Make It to Production
Moving from prototype to production involves challenges beyond LLM prompt engineering:
- Tool orchestration complexity: Production agents must reliably interact with external APIs, databases, and services
- Memory management: Stateful conversations require sophisticated context handling and storage strategies
- Multi-agent coordination: Real-world problems often demand multiple specialized agents working together
- Infrastructure and scaling: Serverless deployment, security, session isolation, and observability
- Cost management: Production workloads require careful monitoring and optimization
You cannot solve these problems with a single tutorial or documentation page. Structured, hands-on experience with production-grade frameworks and AWS infrastructure is required.
A Complete Learning Path: Four Courses, One Goal
This four-course series built around Strands Agents and Amazon Bedrock AgentCore provides this progression. The series differs from scattered tutorials with structured, progressive content.
Course 1: Getting Started with Strands Agents
What you build: A functional AI agent with tool integration and multi-model flexibility
This course goes beyond basic examples. You learn to:
- Configure agents with custom prompts and behaviors that match real use cases
- Connect to multiple model providers such as Anthropic Claude, Amazon Bedrock, LiteLLM, and Ollama
- Integrate AWS services such as Amazon S3 and Amazon DynamoDB directly into agent workflows
- Implement Model Context Protocol (MCP) and Agent to Agent (A2A) to extend capabilities beyond basic prompts
- Add observability with LangFuse and evaluation with RAGAS from the start
Prerequisites: Basic Python knowledge (no AI/ML background required)
Course 2: Advanced Strands Agents with MCP
What you master: The internal architecture that separates prototypes from production systems
This intermediate course covers production system internals. You learn:
- The agent class, agentic loop, and execution lifecycle—understanding what happens under the hood
- Implementing hooks to control agent behavior at specific execution points
- Managing stateful conversations with sliding window and summarization techniques
- Configuring persistent storage for session data across interactions
- Building sophisticated tool orchestration with Model Context Protocol
- Tracking costs and monitoring agent performance in real time
Course 3: Building Multi-Agent Systems
What you architect: Collaborative agent networks that solve complex problems through coordination
Single agents have limitations. This advanced course explores architectural patterns for multi-agent coordination:
- Swarm intelligence: Parallel agent groups that process tasks collectively for faster throughput
- Graph-based coordination: Managing complex dependencies and workflows between agents
- Agents as tools: Wrap entire agents into reusable components that other agents can invoke
- Workflow orchestration: Coordinating distributed agent networks at scale
You learn how autonomous agents communicate, negotiate, and exhibit emergent behaviors.
Course 4: Deploying with Amazon Bedrock AgentCore
What you deploy: Serverless agent deployments with enterprise-grade security and monitoring
This course addresses production deployment challenges:
- Operating agents at scale with serverless infrastructure
- Understanding AgentCore architecture: runtime, gateway, and memory systems
- Implementing session isolation and production-grade security protocols
- Adding comprehensive observability for monitoring and debugging
- Managing credentials and integrations with AWS services securely
AgentCore handles infrastructure complexity, which lets you focus on agent logic. However, you must understand operational aspects when users depend on your system.
Who Should Take This Learning Path?
This learning path is for:
- Backend developers adding AI capabilities to their applications
- Data scientists and ML engineers who want to deploy models as autonomous agents
- Solution architects designing AI-powered systems for production environments
- Tech leads evaluating frameworks and deployment strategies for their teams
You benefit most if you have:
- Solid Python programming skills
- Basic understanding of APIs and web services
- Familiarity with cloud concepts (AWS experience helps but is not required)
- A desire to build production systems, not demos
What Makes This Different
Hands-on and practical: Every course includes labs with code samples available on GitHub. You work with AWS services, model providers, and production-grade frameworks in real environments.
Progressive complexity: The four courses progress from fundamentals to advanced patterns. Each course builds on the previous one to develop your breadth and depth.
Production-focused: Every concept applies directly to building systems that serve users at scale.
Free: No paywalls, no upsells—comprehensive, quality technical education.
AWS-backed: Built by AWS Developer Advocates with deep expertise in AI/ML and production systems.
The Model Context Protocol Advantage
Many tutorials teach hardcoded tool integrations. MCP offers:
- Standardized communication between agents and external systems
- Reusable tool definitions that work across different agent frameworks
- Dynamic capability discovery without code changes
- Better separation of concerns between agent logic and tool implementation
Production systems require maintainability and flexibility. MCP is becoming the standard protocol for agentic AI. Learning it now gives you a competitive advantage.
Real-World Applications
After completing this learning path, you can build these applications:
- Automated customer support systems with multi-agent coordination (tier-1 agents route to specialized agents)
- Data analysis pipelines where agents autonomously query databases, generate insights, and create reports
- Development assistants that understand codebases, suggest improvements, and generate documentation
- Content workflows with agents for research, writing, editing, and publishing coordination
- Business intelligence agents that monitor metrics, detect anomalies, and alert stakeholders
These patterns scale from personal projects to enterprise deployments.
Getting Started
All four courses are self-paced with no deadlines or enrollment windows. You can start anytime, progress at your own speed, and return to material as needed.
The complete curriculum includes:
- Video lessons with real-world examples
- Hands-on labs with starter code and solutions
- Comprehensive documentation and reference materials
- Links to GitHub repositories with production-ready code samples
- Additional reading for deep dives into specific topics
The Bottom Line
AI agents are moving from concept to production in 2025. However, the transition from prototype to production remains challenging for developers. Most developers continue building demos that never scale. Some master the architectural patterns, deployment strategies, and operational practices that differentiate production systems.
This free four-course learning path helps you build production-ready AI agents. You work with modern frameworks (Strands Agents), learn emerging standards (Model Context Protocol), and deploy on AWS infrastructure (Amazon Bedrock AgentCore) that enterprises use.
Building production-ready AI agents requires more than prompting an LLM. These courses provide practical, hands-on experience with production patterns.
Ready to Go Deeper?
This article provides an overview of the learning path. The comprehensive guide includes additional details about each course, reading recommendations, links to all GitHub repositories, and insights from AWS Developer Advocates building production agent systems.
👉 Read the full learning path guide: AI Agents from Prototype to Production
The guide includes direct links to all four courses, curated resources for each topic, and a roadmap for your learning journey from fundamentals to production deployment.
Are you building AI agents for production? What challenges have you faced moving from prototype to deployment? Share your experiences in the comments.