Developing AI Agents

AI Agent Development Skill

This skill provides expertise in building AI agents using modern frameworks like LangChain, LangGraph, CrewAI, and AutoGen.

Core Capabilities

1. Agent Architecture Design

• Choose appropriate agent patterns (ReAct, Plan-Execute, Reflection)
• Design multi-agent systems with proper coordination
• Implement state management and memory
• Structure agent workflows for complex tasks

2. Tool Integration

• Create custom tools for specific domains
• Integrate external APIs and services
• Implement tool error handling and fallbacks
• Optimize tool selection and usage

3. LangChain Expertise

• Build agents with LCEL (LangChain Expression Language)
• Implement RAG systems with vector databases
• Use LangSmith for observability and debugging
• Deploy with LangServe

4. LangGraph Workflows

• Design state graphs for complex workflows
• Implement checkpointing and persistence
• Add human-in-the-loop capabilities
• Build supervisor and worker patterns

5. Multi-Agent Orchestration

• Coordinate multiple specialized agents
• Implement communication protocols
• Handle agent delegation and task routing
• Manage shared state and context

Key Patterns

ReAct Pattern

# Reasoning + Acting loop
# 1. Thought: What should I do?
# 2. Action: Execute tool
# 3. Observation: See result
# 4. Repeat until done

Supervisor Pattern

# Central coordinator delegates to workers
# - Supervisor: Routes tasks
# - Workers: Execute specialized tasks
# - Synthesis: Combine results

Plan-and-Execute

# Strategic planning before execution
# 1. Create comprehensive plan
# 2. Execute steps sequentially
# 3. Replan if needed
# 4. Synthesize results

Tools and Technologies

• LangChain: Core framework for agents
• LangGraph: State machine workflows
• CrewAI: Role-based multi-agent systems
• AutoGen: Microsoft's agent framework
• Vector DBs: ChromaDB, Pinecone, Qdrant
• LLMs: GPT-4o, Claude Opus 4.6, Gemini 3 Pro
• Observability: LangSmith, Weights & Biases

Best Practices

1. Start Simple: Begin with basic agents, add complexity gradually
2. Use Structured Outputs: Leverage Pydantic models for reliability
3. Implement Logging: Track all agent actions and decisions
4. Set Limits: Max iterations, token limits, timeouts
5. Error Handling: Graceful degradation and fallbacks
6. Test Thoroughly: Unit tests, integration tests, end-to-end tests
7. Monitor Costs: Track API usage and optimize
8. Human Oversight: Add human-in-the-loop for critical decisions

Common Challenges and Solutions

Challenge: Agent Loops

Solution: Set max_iterations, implement loop detection, add explicit termination conditions

Challenge: High Costs

Solution: Use cheaper models for planning, cache results, implement rate limiting

Challenge: Unreliable Outputs

Solution: Use structured outputs, add validation, implement retry logic

Challenge: Context Overflow

Solution: Implement memory management, use summarization, offload to vector DB

Challenge: Tool Selection Errors

Solution: Improve tool descriptions, add examples, implement fallbacks

Example Projects

1. Research Assistant: Multi-agent system for comprehensive research
2. Code Review Agent: Automated code analysis and suggestions
3. Customer Support: Intelligent ticket routing and response
4. Data Analysis: Automated data exploration and insights
5. Content Creation: Multi-step content generation pipeline

Resources

• LangChain Documentation: https://python.langchain.com
• LangGraph Documentation: https://langchain-ai.github.io/langgraph/
• LangSmith: https://smith.langchain.com
• Example Repositories: See github-agent-templates.json

Usage in Antigravity

This skill is automatically available when using the ai-agent-development or multi-agent-systems blueprints. It provides:

• Template selection guidance
• Architecture recommendations
• Code generation assistance
• Debugging support
• Best practices enforcement

When to Use

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Prerequisites

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Process

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Source: gitwalter/antigravity-agent-factory — distributed by TomeVault.