Building Effective AI Agents

This post introduces common patterns for building agentic systems, distinguishing between 'workflows' and 'agents'. It details patterns such as prompt chaining, routing, parallelization (sectioning and voting), orchestrator-workers, and evaluator-optimizer. It also describes autonomous agents, emphasizing their use for open-ended problems and the importance of clear tool design and environmental feedback. The Model Context Protocol (MCP) is presented as a method for integrating agents with tools. The post advises developers to start with simple LLM APIs and progressively increase complexity, considering the trade-offs between latency/cost and performance.