Language models are impressive reasoners, but without tools they can only generate text. They can’t check real-time data, perform precise calculations, or interact with external systems. In this post, I’ll explore how to extend agents with tools through function calling, and ensure reliable outputs using Pydantic for structured data validation.
Evaluator-Optimizer and Orchestrator-Worker Patterns
Some tasks require iteration - generating, evaluating, and refining until quality standards are met. Others need dynamic orchestration - a central coordinator breaking down novel problems and delegating to specialists. In this post, I’ll cover two sophisticated patterns that enable these capabilities: the evaluator-optimizer loop and the orchestrator-worker architecture.
Routing and Parallelization Patterns for AI Agents
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.
Step-by-Step Reasoning - How AI Learns to Think
Language models are great at many things, but complex reasoning isn’t always their strong suit. Ask a straightforward question and you’ll get a decent answer. Ask something that requires multiple logical steps, and things get shaky. In this post, I’ll share two powerful techniques that transform how AI approaches problem-solving: step-by-step reasoning and action-oriented thinking.
From Chatbots to Agents - Understanding Intelligent AI Systems
Have you ever wondered why some AI assistants feel genuinely helpful while others just regurgitate generic responses? The difference often comes down to how we guide and structure their behavior. In this post, I’ll share what I’ve learned about building AI systems that go beyond simple question-answering to become true problem-solving partners.
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.