Where to Go Next

You've completed the advanced course. You understand multi-agent systems, long-running sessions, MCP, production deployment, and future patterns. Now what?

Frameworks to Explore

For Python Developers

Framework	Best For	Complexity
LangGraph	Stateful, multi-step workflows	Medium
CrewAI	Role-based agent teams	Low
AutoGen	Conversational multi-agent	Medium
Claude Agent SDK	Direct Anthropic integration	Low
Haystack	RAG + agent pipelines	High

For TypeScript/JavaScript

Framework	Best For	Complexity
Vercel AI SDK	Streaming, React integration	Low
LangChain.js	Full-featured agent toolkit	Medium
MCP SDK	Building MCP servers	Low

Open Source Projects to Study

These codebases demonstrate advanced patterns:

# Agent implementations
- SWE-agent (Princeton): Autonomous software engineering
- Aider: AI pair programming with Git integration
- Claude Code: Production-grade coding agent

# Multi-agent frameworks
- AutoGen (Microsoft): Conversational agents
- CrewAI: Team-based agents

# MCP ecosystem
- Model Context Protocol servers: 10,000+ community servers
- Claude Desktop: Reference MCP client

Build Projects to Practice

Beginner Projects

1. Research Assistant: Agent that searches, summarizes, and cites sources
2. Code Reviewer: Analyzes PRs and suggests improvements
3. File Organizer: Categorizes and moves files based on content

Intermediate Projects

4. Documentation Generator: Reads codebase, generates docs
5. Bug Reproducer: Given an issue, creates minimal reproduction
6. Data Pipeline: Fetches, transforms, and loads data with recovery

Advanced Projects

7. Multi-Repo Refactorer: Coordinates changes across repositories
8. Custom MCP Server: Expose your own APIs to Claude
9. Agent Orchestrator: Build your own mini AutoGen

Key Papers and Resources

Research Papers

• "ReAct: Synergizing Reasoning and Acting" (Yao et al., 2022)
• "Toolformer: Language Models Can Teach Themselves to Use Tools" (Schick et al., 2023)
• "Agent Workflows" (Anthropic, 2025)

Documentation

• Anthropic Claude Documentation
• Model Context Protocol Specification
• Microsoft AutoGen Documentation
• LangChain Agent Documentation

Communities

• Anthropic Discord
• MCP Community GitHub
• AI Agent builders on X/Twitter

Principles to Remember

1. Start simple, add complexity as needed Don't build a 10-agent swarm when one agent with good tools will do.

2. Evaluate obsessively You can't improve what you don't measure. Build evaluation into every agent.

3. Design for failure Everything fails. Plan for it. Checkpoint early, checkpoint often.

4. Human oversight isn't optional Until agents prove trustworthy, keep humans in the loop for critical decisions.

5. Cost awareness Agents can be expensive. Always know what you're spending and why.

Your Next Steps

Week 1-2: Pick a framework, build a simple agent
Week 3-4: Add tools, implement error handling
Week 5-6: Add multi-step workflows with checkpointing
Week 7-8: Deploy to production with monitoring
Week 9+: Iterate based on real usage data

The Bigger Picture

We're at the beginning of the agent era. The patterns you've learned will evolve, but the fundamentals—tool use, multi-step reasoning, recovery, evaluation—will remain relevant.

The best agent developers aren't just coders. They're systems thinkers who understand:

• How LLMs reason and fail
• How to design robust workflows
• How to measure and improve quality
• How to balance automation with human oversight

You now have that foundation. Go build something.

Nerd Note: The agent field moves fast. What's cutting-edge today is table stakes tomorrow. Stay curious, keep building, and share what you learn.

Congratulations on completing Advanced AI Agent Systems! :::