Build a Tool-Using AI Agent
Design and ship one LLM-powered agent that calls tools, plans, and remembers — end to end.
tech · AI · agents · LLM · tool calling · hands-on · By 哈哈哈 · 1 learner
What you will learn: Agent mental model and the perceive-think-act loop; LLM-as-brain intuition; tool/function calling mechanics (schemas, parsing, errors); ReAct and plan-and-execute patterns; short-term memory and context window management; basic guardrails (input filters, output validation, max steps, cost limits); one framework hands-on (LangChain, AutoGen, or CrewAI); observability basics (logs, traces); one small end-to-end capstone agent.
Out of scope: Multi-agent orchestration systems, production deployment and scaling, fine-tuning or custom model training, evaluation frameworks at scale, full RAG system design, agent safety/alignment research, voice/audio agents, GUI/UI agents.
Knowledge map
Agent Mental Model
Explain the perceive-think-act loop and why an LLM works as the decision-making brain of a tool-using agent.
Sets the conceptual foundation for the rest of the course: what an agent is, the perceive-think-act loop, and why LLMs are a natural fit as the reasoning core. This vocabulary is reused in every later module.
Tool Calling Mechanics
Describe how an LLM is given a tool schema, how it emits a structured call, and how the host parses, executes, and feeds the result back.
Covers the mechanical core of any tool-using agent: declaring tools as schemas, getting the LLM to choose and parameterize one, parsing its output safely, executing the tool, and returning results. This is the engine every later pattern sits on top of.
Reasoning Patterns
Compare the ReAct and plan-and-execute patterns and choose the appropriate one for a given task complexity.
Builds on tool-call mechanics to show how the agent decides what to do across multiple steps. ReAct interleaves thinking and acting; plan-and-execute separates upfront planning from execution. Knowing the tradeoffs is essential before reaching for a framework.
Memory and Guardrails
Manage an agent short-term memory within a context window and apply basic guardrails that keep a single agent safe, bounded, and affordable.
Turns a working loop into something that does not run away. Covers what fits in a context window, how to trim and summarize, and the minimum guardrails, from input filtering and output validation to step and cost caps, that a beginner must apply.
Framework and Observability
Use a single agent framework to assemble a tool-using agent and add basic logs and traces so its behavior can be inspected and debugged.
Bridges raw mechanics to a real codebase. Picks LangChain as the default first framework, walks through the abstractions it exposes for tools, agents, and memory, then adds print-style logs and proper traces so the learner can actually see what the agent is doing each step.
Capstone Agent Project
Ship a small end-to-end tool-using agent that takes a user goal, calls real tools, stays within guardrails, and produces a logged trace of its run.
Integrates the mental model, tool mechanics, a reasoning pattern, memory and guardrails, the chosen framework, and observability into one project. The learner designs a small task, picks tools, and ships a working agent they can demo and debug.