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AI Video Library

18 free video lessons on generative AI – from how large language models work to RAG, AI agents and fine-tuning. Watch here, then practise with the lesson code.

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About these lessons: the videos and course materials come from Generative AI for Beginners by Microsoft – a free, open-source course published under the MIT License. Videos are streamed from Microsoft Learn. Course and videos © Microsoft Corporation. Epitomsoft Academy uses them as recommended self-study alongside our live, mentor-led programme.

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How an AI agent works – animated lesson

Our presenter Epi explains the full AI agent loop with a 3D animated diagram and a real example: finding internships and drafting a cover email.

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Welcome: Hi, I am Epi from Epitomsoft Academy! In this lesson you will see how an AI agent works, step by step. We will follow one real task: find three remote Python internships, and draft a cover email.
Step 1 · Instructions: Everything starts with instructions. The system prompt and your request define the agent’s goal, its rules and its limits. For example: find three remote Python internships posted this week, and never apply without my approval.
Step 2 · Memory: Next, the agent checks its memory. Memory lives outside the model, in persistent storage such as a database, and the relevant memories are added to the current context. Here, it remembers that you are a final-year student who knows Python and SQL.
Step 3 · Context: Before every model call, the context is built fresh. The instructions, memories, tool descriptions and earlier results are assembled into one package. Remember: the model only knows what is in its context.
Step 4 · Model: Now the model interprets the situation and decides the next steps. Planning and reasoning are done together by the language model and an agent controller, the code that runs the loop. The plan: search job boards, filter for remote Python roles, then draft the email.
Step 5 · Action: The agent executes the selected action by calling a tool. Tools are external capabilities, like web search, GitHub, Slack, email or a company database. Here, it calls a job search tool with: Python intern, remote, posted this week.
Step 6 · Evaluation: The results are checked with rules, validators or tests. Are there really three roles? Are they all remote? Was each one posted this week? Good agents never assume their results are correct.
Step 7 · Re-plan: If the results are incomplete, the agent creates a new plan and loops again. Only two roles matched, so it widens the search to include data and automation internships that use Python.
Step 8 · Fill the gaps: When information is missing, the agent identifies the gap and searches again, in memory or in other sources. Here, it looks up your résumé summary, so the email can highlight your strongest project.
Step 9 · Update memory: Finally, the agent updates its memory with what it learned, such as useful sources and your preferences, so the next task is faster and more personal. Then it returns the answer: three internships, and a draft email waiting for your approval.
Recap: That is the agent loop: instructions, memory, context, model, planning, action and evaluation, repeated until the goal is met, with a human in control of important actions. You will build your own agent in Module 2 of the Epitomsoft Academy. See you there!