More tools don't mean better AI agents. Learn why 5 well-designed tools outperform 50 mediocre ones in accuracy, speed, and cost.
Apply the Pareto principle to AI agents. 20% of tools handle 80% of tasks. Build lean, ship fast, and add complexity only when needed.
AI agents stuck in infinite loops? Learn detection strategies and prevention patterns including failure memory, diversity forcing, and escalation.
Model AI agents as state machines for clearer design. Learn states, transitions, and events to build predictable, debuggable agent architectures.
Build an AI email assistant with MCP. Connect Claude to Gmail or Outlook to read, draft, and send emails through natural conversation.
Fix AI agents that refuse to use tools. Debug over-cautious behavior caused by system prompts, tool descriptions, and model training patterns.
Should AI tools do one thing or many? Compare atomic vs compound tool design patterns with trade-offs for flexibility, reliability, and token cost.
Handle long-running AI agent tasks without HTTP timeouts. Implement background jobs, job queues, and status polling for reliable execution.
Chain of thought prompting improves reasoning but adds latency and cost. Learn when to use CoT and when simpler prompts work better.
Control smart home devices with Claude and MCP. Tutorial for lights, thermostats, and sensors using natural language through local shell scripts.
