#tutorial
7 posts
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Tool Use Patterns: Building Reliable Agent-Tool Interfaces
Learn how to design, implement, and harden tool use in AI agents — covering schema design, result handling, parallel calls, and failure recovery.
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Debugging and Observability in Autonomous Agent Systems
Building production-ready observability into AI agent workflows — logging, tracing, and debugging strategies for autonomous systems.
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Agent Error Recovery: 5 Patterns for Production Reliability
Master 5 error recovery patterns to build autonomous agents that handle failures, retry intelligently, and stay reliable in production environments.
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Building Agents at Home for Free: Open Source Tools and Models
A practical guide to building agentic systems using open source LLMs, frameworks, and tooling—no paid APIs required. Run everything locally on your machine.
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State Machines and Agents: Building Reliable Workflows with LangGraph
Learn how to build structured, reliable agent workflows with LangGraph—state graphs, conditional routing, human checkpoints, and production patterns.
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Agent Memory Systems: Giving Your AI Persistent Context
Learn how to implement persistent memory for AI agents—from simple conversation buffers to vector-based semantic recall—with practical TypeScript examples.
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Building Your First MCP Server
A practical walkthrough of creating an MCP server that exposes real data sources to AI agents — covering protocol mechanics, tool schemas, and common patterns.