AI Agent Architecture: Tools, Memory, and Planning
Learn how to build AI agents with tool use, memory systems, and planning capabilities using Python and LLMs for autonomous task completion.
·10 min read · #ai-agents#tool-use#memory
#tool-use
6 posts · page 1 of 1
Function Calling Patterns Across LLM Providers
Compare function calling implementations across OpenAI, Anthropic, and Google, with patterns for routing, chaining, and error handling.
·7 min read · #ai#function-calling#llm
LLM Function Schema Best Practices
How to design tool schemas that LLMs actually call correctly, with naming, description, and parameter patterns that survive real users and adversarial inputs.
·4 min read · #llm#function-calling#tool-use
Structured Outputs with LLMs
How to get reliable JSON out of LLMs using tool use, JSON mode, and grammar-constrained decoding, with patterns that work in production.
·5 min read · #llm#structured-outputs#json
Prompt Engineering with Tools and Functions
How to write prompts and tool definitions that make function calling reliable. Covers schemas, descriptions, examples, error handling, and patterns for multi-tool agents.
·4 min read · #prompt-engineering#tool-use#function-calling
AI Agents and Tool Use Patterns
Practical patterns for building AI agents that use tools well: tool definitions, loops, planning, parallel calls, error handling, and how to keep agents from going off the rails.
·5 min read · #ai#agents#tool-use