Sequential chains work well when tasks have a clear order, but real-world problems often require more flexibility. Sometimes you need to route tasks to different specialists based on their content. Other times, multiple agents should work simultaneously on different aspects of a problem. In this post, I’ll cover two powerful patterns: routing for intelligent task dispatch and parallelization for concurrent processing.
Prompt Chaining Workflows - Sequential Task Decomposition
When a single prompt isn’t enough, we chain them together. Prompt chaining is one of the most practical patterns for building AI workflows - breaking complex tasks into focused steps where each agent’s output feeds into the next. In this post, I’ll explore how to design, validate, and implement effective prompt chains.
Anatomy of an AI Agent - Building Blocks and Workflows
Moving beyond simple prompting techniques, it’s time to examine what actually makes an AI agent tick. In this post, I’ll break down the core components that transform a language model from a sophisticated autocomplete into an autonomous problem-solver, and explore how to model and implement agent workflows.
Building Reliable AI - Chains, Gates, and Self-Improvement
AI systems that work once under ideal conditions are interesting. AI systems that work reliably in production are valuable. In this final post of the series, I’ll share techniques for building robust AI workflows - connecting multiple reasoning steps, validating outputs along the way, and creating systems that improve through iteration.
From Prototype to Production - LangGraph Systems
A working prototype is maybe 20% of production effort. The remaining 80% involves error handling, monitoring, deployment, scaling, and building systems that fail gracefully. This post covers the gap between “it works on my machine” and “it handles thousands of users reliably.”
Multi-Agent Architecture with LangGraph
Single agents hit a ceiling. When tasks require diverse expertise - research, coding, analysis, writing - a single agent either becomes overloaded with tools or produces mediocre results across domains. Multi-agent systems solve this by decomposing work among specialized agents, each focused on what it does best.
Agentic RAG and Human-in-the-Loop with LangGraph
Traditional RAG is a one-shot process: retrieve documents, generate answer, done. Agentic RAG breaks this limitation - agents can evaluate retrieval quality, reformulate queries, and iterate until they find what they need. Combined with human-in-the-loop patterns, you build systems that are both autonomous and controllable.
Connecting LangGraph Agents to APIs and Databases
Agents become truly useful when they can interact with the real world - fetching live data from APIs, querying databases, and writing results back. This post covers building production-grade integrations: API tools with proper error handling, SQL agents that translate natural language to queries, and security patterns that prevent disasters.