AI Tools & Tool Calling: The Missing Piece Behind Powerful AI

#AITools #ToolCalling #AgenticAI #Technology #Programming
What if AI could do more than just generate text?

In this video, we break down AI Tools and Tool Calling and explain how AI assistants can connect language models to external systems, APIs, databases, and real-world actions.

You’ll learn how a model can understand a user's request, choose the right tool, generate structured tool arguments, and receive the result back from the application.

We use practical examples such as:

• Checking an order status
• Creating a support ticket
• Searching for flights
• Understanding tool schemas and parameters
• Handling missing information
• Deciding when a tool is actually needed
• Authentication, authorization, and human approval
• Reading vs. changing data through tools
• The complete model → tool → application → result loop
• How tool calling becomes a foundation for Agentic AI

We also explore an important challenge: what happens when an AI system has thousands of tools with different schemas, permissions, and capabilities?

By the end, you’ll have a clear mental model of how modern AI applications move from simply generating answers to interacting with external systems and taking meaningful actions.

Function calling/tool use is a core pattern for connecting AI models with external tools and systems.

Topics covered

AI Tools, Tool Calling, Function Calling, Tool Schemas, Function Schemas, AI Agents, Agentic AI, LLMs, APIs, Databases, Tool Selection, Structured Arguments, Permissions, Authentication, Authorization, Human-in-the-Loop, AI Workflows.

If you're learning AI engineering, LLM application development, AI agents, or agentic systems, this is an important concept to understand.

Like, share, and subscribe for more practical AI and machine learning concepts.

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