Building AI Agents with Tool Use: A Practical Architecture
At its core, an AI agent follows a simple loop: observe, think, act, repeat. Tools should be focused, well-documented, and handle errors gracefully.
12 articles
At its core, an AI agent follows a simple loop: observe, think, act, repeat. Tools should be focused, well-documented, and handle errors gracefully.
Functions are defined as JSON schemas that describe parameters and their types. Well-designed function schemas and clear descriptions ensure the model calls…
Well-designed function calling creates powerful AI applications that interact safely with real systems.
The evolution of tool use in AI represents a fundamental shift from constrained function execution to general-purpose computer interaction. This trajectory…
Tool choice control is essential for building AI applications that are predictable, safe, and aligned with your business logic. Use these patterns to guide…
Parallel function calling dramatically improves response times when multiple independent operations are needed. Use it wisely to build faster, more…
Function calling is the foundation of agentic AI. These patterns help you build reliable, type-safe tool integrations that scale.
Tools extend what AI agents can do beyond text generation. Today I'm exploring patterns for effective tool use in production agents.
Parallel function calling in GPT-4 Turbo is the DevDay improvement to function calling that changes what's architecturally practical with AI agents.…
The most consequential design decision in a function-calling agent isn't the model you choose — it's how you write the tool definitions. The model selects…
Function calling patterns enable sophisticated AI agents. Tomorrow, I will cover building AI agents in more depth.
Function calling transforms GPT models into powerful agents that can interact with the real world. Tomorrow, I will cover Azure AI Studio in more detail.