AI Agent Development Guide: Build Intelligent Workflows with LLM APIs

Building AI Agents with LLMs

AI agents represent the next evolution in software — systems that can plan, use tools, and execute multi-step tasks autonomously. Unlike simple chatbots, agents maintain state, make decisions, and interact with external systems. Building effective agents requires understanding both the capabilities and limitations of the underlying LLMs.

Agent Architecture Patterns

• ReAct Pattern: Reasoning + Acting — the model alternates between thinking about what to do and executing actions
• Plan-and-Execute: The model first creates a plan, then executes each step, updating the plan as needed
• Multi-Agent Systems: Multiple specialized agents collaborate, each handling different aspects of a complex task

Choosing the Right Model

For agent workflows, prioritize models with strong tool-calling capabilities and large context windows. Claude models excel at structured tool use, while GPT-5.6 variants offer flexible function calling. Start with a mid-tier model and upgrade as your agent's complexity grows.