Building Effective AI Agents

This post provides practical advice for building effective AI agents, distinguishing between 'workflows' (predefined code paths) and 'agents' (dynamic LLM-directed processes). It details common patterns for agentic systems: prompt chaining (sequential LLM calls), routing (directing input to specialized tasks), parallelization (sectioning for speed, voting for confidence), orchestrator-workers (dynamic task delegation), and evaluator-optimizer (iterative refinement with feedback). The post emphasizes starting with direct LLM API usage and understanding underlying abstractions when using frameworks, and highlights the Model Context Protocol (MCP) for tool integration. It also discusses the trade-off between latency/cost and task performance when deciding between workflows and agents.