AI Agents Explained - How They Actually Work

Key Takeaways

Search code error debug fix that just happened autonomously. No human touch the keyboard between the request and the result. There's no new AI here. It's the same type of language model you've been using. The difference isn't the brain, it's the scaffolding around it. In this video, we'll see exactly what's happening. the loop. The tool calls where it breaks. Let's open the hood. You know how this works already. You ask a question, you get an answer. Done. The model is like a brilliant expert locked in a room. You slide questions under the door. They slide answers back. Smart, but isolated. They can't check anything, do anything, or see the results of anything. An agent is what happens when you give that expert a phone, a computer, and a system that lets them take multiple turns, building on what they learn from each step. Now they can say, "Let me look that up." Actually, look it up. See the results. Say, "Hm, I need more info." Search again. Okay, now I'll write the code. Write it. Let me test it. Run it. Sees an error. I'll fix that. Fix it. Same expert, same brain, completely different capability. An agent is an LLM in a loop with tools. That's the entire concept. The model thinks, acts, observes the result, and repeats until the task is done. Everything else is implementation. Every agent runs the same fundamental loop. Researchers call this pattern react, reasoning, and acting together. It's now the foundation of virtually every agent framework. Let's watch one work while I break it down. Step one, perceive or gather context. Some frameworks call this the thought phase. The agent is given everything it needs. The user's goal, what tools are available, any relevant memory from past conversations. Step two, reason. The LLM looks at all this and decides, what should I do? Do I know the answer or do I need to do something first? Step three, act. The model outputs a specific structured request for a tool. Step four, observe. The tool executes, actually runs, and returns results to the model. Real search, real data. Now the model has new information. Now the loop repeats. The model has new information. It reasons again. I have the LTS version. Now I need migration info. Another search, another observation, and now it has enough to answer. Two loops, two tool calls, task complete. Simple queries might finish in one cycle. Complex tasks might take 10, 20, more. The model keeps going until it decides it's done. Wait, the model only outputs text. So, how does text become an actual web search? The model doesn't execute anything. Your code does. The model just asks in a format your code understands. You define your tools with a name, description, and what inputs they need. The description matters. The model reads it to decide when each tool is appropriate. These definitions go into the system prompt. The model sees these are your tools. When the model wants to use a tool, instead of plain text, it outputs structured JSON like this. Your orchestration layer, that's the wrapper code you write around the LLM, the thing that manages the loop, catches this, parses it, sees it wants to search, and runs the actual search. Real API call, real results. Those results get formatted and fed back to the model as an observation. Model proposes, your code executes, model sees results. That's the handshake. Every loop iteration is an API call. More loops means more latency and more cost. A complex task with many iterations can add up fast. This is an engineering constraint, not just a footnote. So, what happens when a conversation gets really long? or when you want the agent to remember something from last week. Language models have a context window, a limit to how much they can see at once. Think of it as desk space. Current conversation takes up room. Tool results take up room. Eventually, you run out of space. When that happens, you have to make choices. summarize old content, drop less relevant parts, or use a sliding rimo that only keeps recent history. That's working memory, the current session, bounded by context limits. Long-term memory, remembering across sessions is harder. The common pattern, when something important comes up, you extract it. You convert it to an embedding. Think of it as a numerical fingerprint that captures the meaning. These embeddings live in a highdimensional space. Similar concepts cluster together. Related memories are geometrically close. When the agent needs something relevant, it doesn't search by keywords. It searches by meaning. Semantic similarity. Those vectors get stored in a database. Later, when it might be relevant, you search that database and pull matching memories back onto the desk. Some systems give the model explicit tools for this, a remember this and recall function. The agent manages its own memory. Recent research shows this hybrid approach, small context window plus external memory significantly outperforms stuffing everything into context. 26% accuracy improvement, 91% lower latency, 90% fewer tokens. This isn't optional. It's how production agents work. True long-term learning that improves over time. Still an open research problem, but the primitives exist. That's how they work. Now, how they fall apart. If you're building with agents, these failure modes will find you. Infinite loops. The model keeps searching, keeps trying, never converges. It's stuck. You need maximum iteration limits and loop detection. Hallucinated tool calls. The model invents tools that don't exist or malforms the JSON. Your parser needs graceful error handling. Error cascades. A tool fails or returns bad data and the model treats it as truth. Garbage in, confident garbage out. Context bloat. 20 loops in. The context is packed with tool results. The model loses track of the original goal. Cost surprises. Each iteration costs tokens. Complex tasks can hit dollars, not cents. Monitor and set budgets. Wrong tool selection. The model misunderstands what a tool does and picks the wrong one. Vague tool descriptions cause this. Clear, precise descriptions reduce it significantly. Be explicit about what each tool does and when to use it. These aren't reasons to avoid agents. There are engineering problems with engineering solutions, but you need to know they exist. The short version, agents are LLMs in a loop. Same models, different architecture. The loop, perceive, reason, act, observe, repeat. Tools are structured requests. The model proposes, your code executes. Memory is bounded. Working memory is the context window. Long-term needs external systems. Agents fail predictably. Six failure modes we covered. Loops, hallucinations, cascades, bloat, cost, wrong tools. Design for all of them. If you want to internalize this, build a minimal agent from scratch. One tool, one loop under 100 lines of code. That exercise is worth more than any tutorial. That's agents under the hood. If this helped, please like and share. Thanks for watching and see you in the next