Tool Use & Function Calling
AGAI 201
Learn how AI agents extend their capabilities through tools. Explore function calling APIs, tool design patterns, and how agents decide which tools to use and when — turning language models into systems that can act on the real world.
Beyond Text: Agents That Act
A language model on its own can only produce text. Tools transform it into a system that can search the web, run code, query databases, call APIs, manage files, and interact with external services. Tool use is what makes the difference between a chatbot and a capable AI agent.
The Function Calling Revolution
Modern LLM APIs include a function calling interface that allows the model to request the execution of predefined tools. The model decides when to use a tool, what arguments to pass, and how to incorporate the result into its reasoning. This section explores how function calling works under the hood and how to design tool interfaces that models use reliably.
What You Will Learn
You will learn how to design and implement tools for AI agents: writing schemas that models call reliably, handling errors and edge cases gracefully, securing tool-using agents against misuse, and evaluating whether your agent is making good tool-selection decisions. You will work through practical examples with search, code execution, database, and API tools.
Who This Course Is For
This course is for developers building AI-powered applications who want to give their models the ability to act in the world — not just respond with text. If you have built chatbots or basic LLM integrations and are ready to extend your agent with real capabilities, this is the course that bridges the gap between conversation and action.
What you will learn
- Explain how function calling works in LLM APIs
- Design clear and reliable tool schemas
- Implement a tool-using agent with standard frameworks
- Handle tool errors and edge cases gracefully
- Identify security risks in tool-using agents
- Evaluate the performance of tool-augmented systems
Major topics
Why this course matters
Tool use is the bridge between language model intelligence and real-world action. Understanding how to design and implement tool-using agents is essential for building any practical AI application beyond simple Q&A.
Course modules
Function Calling Foundations
Learn what tools are in agentic AI systems and how function calling turns language models from passive text generators into systems that can request real actions. This module introduces the tool-call lifecycle, API message flow, and the difference between model reasoning and application execution.
Designing and Building Tool-Using Agents
Move from basic function calls to practical tool-using agents. This module teaches schema design, tool selection, orchestration patterns, and implementation strategies using standard application architecture.
Production Tool Use: Reliability, Security, and Evaluation
Prepare tool-using agents for real users and real systems. This module covers error handling, retries, prompt injection, permission boundaries, audit logs, and practical evaluation methods for tool-augmented agents.
Common misconceptions
Function calling means the model executes code directly
More tools always make an agent more capable
Tool-using agents are inherently unsafe
Function calling is only for code-related tasks
Ask your AI guide
Ask anything about Tool Use & Function Calling, or choose a suggested question below.
AI responses are educational and may not be perfectly accurate. Press Enter to send, Shift+Enter for new line.
Related courses
Prompt Engineering
Master the art and science of writing effective prompts. Learn how prompt structure, context, and framing dramatically affect model outputs, and explore advanced techniques like chain-of-thought, few-shot prompting, and system prompt design.
Agent Architectures
Survey the major architectural patterns for building AI agents. From simple ReAct loops to structured planning systems, learn how different architectures trade off capability, reliability, and interpretability.
Building Production Agents
Move from prototype to production. Learn the engineering practices required to build AI agents that are reliable, observable, cost-effective, and maintainable at scale — including evaluation, tracing, error handling, and CI/CD for AI systems.