AI
AI Agent Development #6: Building Your Own MCP Server
In Part 11 of LLM App Development we connected to MCP servers someone else built. This time we build our own tools as an MCP server. We cover writing a server with FastMCP, wiring it into our agent loop, and the criteria for splitting tools out into a server.
AI Agent Development #5: Dividing Work with Subagents
When one agent does everything, both its context and its responsibilities bloat. We cover why you delegate work to subagents, a delegate tool, the orchestrator-worker pattern with parallel execution, and rules to keep delegation from going too far.
AI Agent Development #4: Context Management for Long-Running Work
The longer an agent runs, the closer its conversation grows to the context limit. We cover techniques for surviving long-running work: capping tool results, clearing old results, summary compression and server-side compaction, and a file-based scratchpad.
AI Agent Development #3: Planning and Self-Correction
To hand an agent multi-step work, it needs rules of behavior and a plan. We cover system prompt design, getting the agent to plan first, mid-task verification and retries, and tuning thinking depth with adaptive thinking.
AI Agent Development #2: Designing Good Tools
Most of the quality gap between agents comes from their tools. We cover the principles of tool design: the description the model reads, schema design, error messages, and classifying dangerous tools with a confirmation step.
AI Agent Development #1: Building a Robust Agent Loop
Take the minimal agent loop from LLM App Development up to production level. Handle every stop_reason, return tool errors as results, plus retries and logging. The starting point of this series.
LLM App Development #13: A Real-World Project — Internal Document Q&A Bot
Bring all the pieces together and build a Q&A bot that answers from internal documents, start to finish. A finale combining RAG, streaming, grounding prompts, and conversation memory.
LLM App Development #12: Cost, Evaluation, and Observability
What you need to actually operate the app you built. Cutting token cost and prompt caching, evaluation that measures quality, and observability that looks into behavior.
LLM App Development #11: Connecting Tools with MCP
MCP (Model Context Protocol), the standard for connecting tools. Instead of writing tools by hand every time, connect Claude to ready-made tool servers.
LLM App Development #10: Building an AI Agent
Build an agent where Claude chooses its own tools and takes multiple steps to get work done. The agent loop, orchestrating several tools, and safe termination conditions.
LLM App Development #9: Conversation Memory and Context Management
How to handle the history that piles up as a conversation grows. Context limits and token cost, sliding windows and summary compaction, and server-side compaction.
LLM App Development #8: Building a RAG Pipeline
Hand the documents found by vector search to Claude so it answers based on our documents. We complete RAG, building the full pipeline of chunking, retrieval, prompt injection, and generation.