AI Agents for Beginners

Explore how prompt design shapes AI output by managing the context window, using system, user, and assistant roles, and applying few-shot and chain-of-thought prompting for concise, structured results.

Write a Python program that calls an LLM via OpenAI's API using the SDK, prints the response, and learns how tokens, temperature, and context window affect output.

Sign up for the labs enrollment page, access free first-half labs with preloaded API keys and tokens monthly, and explore model keys from Anthropic, OpenAI, Google, DeepSeek, plus sample projects.

Run your api call to an llm in the terminal, connecting your code to openai with an api key and sdk, and observe how temperature shapes prompts, tokens, and outputs.

Discover when to use an LLM versus code in building AI agents; differentiate deterministic tasks from non-deterministic ones, and learn how to decide which tool to call.

Explore how chat conversations use user, system, and assistant roles to drive context. Learn how to craft system messages, feed history, and manage multi-turn conversations for accurate responses.

Discover how to make LLMs act by adding tools like web search, databases, and APIs, learn tool calling, and differentiate AI workflows from agents to build your first agents.

Learn how tools extend LLMs beyond text by calling functions to access calendars, emails, and web data; define tools by name, description, and parameters, and decide when to call them.

Explore tool integration and the tool use loop, showing how a language model issues tool calls, the app executes them, and returns results through sequential or parallel function calls.

Build a Python tool calling app where the language model decides when to call tools. Define tool schemas, write handlers, and wire a tool call loop for calendar data.

Discover ai workflows that orchestrate llm calls and tools via predefined steps set by developers, with sequential tasks, routing, parallelization, and voting for predictable debuggable outputs.

Compare workflows and agents by contrasting predefined steps with the LLM deciding the next action. Use tools and guardrails to handle on-the-fly decisions and consider cost, latency, and debugging.

Choose the simplest solution first: use a single LLM call or a workflow for well-defined steps, and only add an agent for open-ended tasks when needed.

Define agents and their core loop, then build your first working agent and layer memory, planning, reasoning, error handling, and multi-agent collaboration, guided by four ghost characters and production frameworks.

Define an llm-powered personal assistant that runs in a loop, using tools to pursue a goal through the perceive-reason-act cycle based on feedback.

Build your first ai agent loop in python by writing a while loop that calls the model, checks the finish reason, and repeats with multi-turn memory.

Discover the five components of any agent—model, tools, memory, orchestration loop, and system prompt—and how they enable the perceive-reason-act cycle and reasoning.

Wire five core agent components—model, tools, memory, system prompt, and an orchestration loop—into a single Python script to call a calendar tool and display events.

Discover how AI agents plan before acting by using reasoning, chain-of-thought, and the react pattern. Learn guardrails like iterations, token budgets, and structured output to ensure reliable, adaptive planning.

Practice a multi-turn agent and implement a sliding window memory trim to keep history under control, with a system prompt and trim_history enforcing a max messages limit.

Learn how context windows constrain AI agents and how context compression preserves essential facts while long-term memory enables persistence across sessions.

Learn the react pattern to reveal an AI agent's reasoning by adding thought lines before tool calls and observation lines after results, using check_calendar and search_contacts.

Split tasks across specialized agents with an orchestrator coordinating parallel and pipeline patterns to handle memory and code execution. Use multi-agent systems when subtasks require separation, specialization, or different models.

Apply agentic patterns to a React agent by adding structured output, input guardrails, and human-in-the-loop, using a preconfigured Python lab environment with a calendar tool and test loop for hands-on learning in ai agents for beginners course topics on lab practices and tool integration like patterns_agent.py and system prompts and loops with finish_reason checks and calendar tool usage. </s> No, we must remove trailing stray text. Ensure clean JSON without extraneous content. The summary should be concise (15-30 words). I will correct it. The final summary should be: Apply agentic patterns to a React agent by adding structured output, input guardrails, and human-in-the-loop, using a preconfigured Python lab environment with a calendar tool and test loop.

Learn agentic patterns for building reliable AI agents, including augmented LLM with retrieval, tools, and memory; structured output, guardrails, human-in-the-loop, and model fallback.

Compare Anthropx tools, memory, and planning with Google agent patterns. Study grounding, an explicit orchestration layer, and the use of extensions, function calling, and grounding via search or data stores.

Compare major agent frameworks, including LangChain, LangGraph, Anthropic SDK, OpenAI Agents SDK, Google ADK, Mastra. Learn when to build from scratch or use frameworks, grasp agent loop, memory, and OpenClause.