{"slug": "the-anatomy-of-an-ai-agent-model-loop-tools-memory", "title": "The anatomy of an AI agent: model, loop, tools, memory", "summary": "Overmind, a model training platform for AI teams, published a technical breakdown of AI agent architecture, defining agents as language models wrapped in a loop that plans, calls tools and iterates toward a goal across many steps. The piece identifies five core components — model, harness, tools, memory and trajectory — and contrasts the 2026 agentic default with the single-call chatbots that dominated 2024. It notes that frameworks such as LangChain, Agno, CrewAI and the OpenAI Agents SDK exist to simplify building the harness, and cites OpenAI's June 2023 function calling release and Anthropic's open-sourced Model Context Protocol as milestones in tool integration.", "body_md": "An AI agent is a language model wrapped in a loop that plans, calls tools and iterates toward a goal across many steps. Five components make one up. A model that picks the next action, a harness that runs the loop, tools that reach outside the model, memory that carries state, and a trajectory, the record of the whole run.\n\n*Originally published at [overmindlab.ai](https://www.overmindlab.ai/research/anatomy-of-an-ai-agent?utm_source=devto&utm_medium=syndication&utm_campaign=research-repost).*\n\n*An agent has a model, loop, tools and memory.*\n\nIn 2024, most AI products were chatbots. A single LLM inference call, one prompt in, one completion out. In 2026, the default architecture is an **AI agent**, an agentic system that plans, calls tools, and iterates toward a goal across many steps.\n\n|  | Chatbot (the 2024 default) | AI agent (the 2026 default) | \n|---|---|---|\n| Model calls per task | One | Many | \n| Shape | One prompt in, one completion out | Plans, acts, observes, repeats | \n| Tools | None | APIs, databases, code execution | \n| Execution path | A single step | Varies run to run, non-deterministic | \n| What you get back | The first model response | An outcome, plus a trajectory | \n\nInstead of returning the first model response, teams wrap the LLM in an **agent harness**, expose **tool calling** (APIs, databases, code execution), and let the system work toward an objective. Frameworks such as LangChain, Agno, CrewAI, and the OpenAI Agents SDK exist to make it easier to build the harness.\n\nThis article decomposes an AI agent into its core components. We define each part of the **AI harness** and walk through one complete agent run. Worth stating where we sit. Overmind is the model training platform for AI teams, and the trajectory, component five below, is the raw material it works from.\n\nStrip an agentic system down and you get five components. Four you assemble and the fifth is what they produce.\n\n| Component | What it does | Assembled or produced | How you change it | \n|---|---|---|---|\n| Model | Selects the next action given goal and state | Assembled | Swap it, or train and fine-tune it | \n| Harness | Runs the loop of reason, act, observe, repeat | Assembled | Rewrite prompts, context rules and stop conditions | \n| Tools | How the agent interacts with external systems | Assembled | Add, remove or redescribe tools | \n| Memory | How the agent persists and retrieves state | Assembled | Change what stays in the context window, change retrieval | \n| Trajectory | The record of one complete agent run | Produced by the other four | Not directly. It changes when the other four change | \n\nThe **language model** is the only component that reasons, and the only one you can train or fine-tune. Conditioned on the goal and the observations so far, it chooses the next action. Most teams start on a **frontier model API** and some migrate to an **open-weights model** they host themselves or run through a third-party provider.\n\nMore on that trade-off in [open-weights LLMs vs frontier APIs](https://www.overmindlab.ai/research/open-weights-vs-frontier-apis?utm_source=devto&utm_medium=syndication&utm_campaign=research-repost).\n\nThe harness assembles what the model sees, reads the action it picks, calls the matching tool, and feeds the result back in. It also decides everything the model does not. Which tools exist and how they are described, what stays in the context window, what happens when a tool call fails or comes back malformed, and when to stop, whether that is the goal being met, a step limit, or an error.\n\nThis is what agent frameworks give you. It is the part of an agent you can change immediately.\n\n**Tools** are how the agent reaches outside the model, whether that is querying a database, calling an HTTP API, running code, or sending an email. OpenAI shipped **function calling** in June 2023, letting the model emit a structured request to invoke a function instead of prose. Anthropic open-sourced the **Model Context Protocol (MCP)** in November 2024 to standardise how agents connect to those tools, and now everything is an MCP server.\n\n**Memory** is how the agent carries state across loop iterations. Short-term memory is the **context window**, the running transcript of the goal, prior steps, and tool outputs. Longer-term memory is **retrieval**, pulling documents or past runs from outside the window (often via RAG or a vector store). Without memory, the agent loses its way.\n\nThe **trajectory**, also called the **agent trace**, is the full ordered record of a single run. The goal, every LLM call, every tool invocation, every observation, and the final outcome. It is the agent's stack trace. You do not build it; the other four components produce it. It is also the only unit of analysis at which you can honestly say whether the agent succeeded. Capture it.\n\nWatch a support agent handle a refund. The user goal enters, the agentic loop runs, an outcome exits.\n\n`get_order(id)`; the tool returns the order details.` issue_refund(amount)`; the tool returns a confirmation.\nFour model calls, two tool calls, one outcome. That entire sequence is the **agent trajectory**. Change the goal, the tool responses, or the model's sampling behaviour and the next run takes a different path. Agents are **non-deterministic**. The same prompt does not guarantee the same execution path.\n\nEach layer of a typical AI stack controls an aspect of the agent. Almost none control the whole run.\n\n| Layer | What it sees | Can it update the model? | \n|---|---|---|\n| LLM gateway | One request and response at a time | No | \n| Agent observability | The full trace, after the fact | No | \n| Agent evals | A score for the run | No | \n| Closed training loop | Judged trajectories fed back into training | Yes | \n\nThree of the four tell you what happened. Only the last one changes what happens next time. More on that gap in [so, you have observability. Now what?](https://www.overmindlab.ai/research/so-you-have-observability-now-what?utm_source=devto&utm_medium=syndication&utm_campaign=research-repost).\n\nImproving an AI agent means operating at the level of the **trajectory**, not the individual LLM call. Capture the full run, judge it against your definition of success, and feed judged trajectories back into the system.\n\nThere are two distinct ways to improve an agent.\n\nThis is the loop Overmind is built to close.\n\nA chatbot is one model call. One prompt in, one completion out. An agent runs many model calls inside a loop, calls tools between them, and keeps going until the goal is met or it stops. The comparison table above sets the two side by side.\n\nNo. In a workflow, you decide the order of steps in advance. In an agent, the model decides the next action at each turn given the goal and what it has observed so far. That is why two runs of the same agent can take different paths and a workflow cannot.\n\nBecause the model chooses each action, and its sampling behaviour, the goal, and the tool responses all vary. Change any of them and the next run takes a different path. This is why a single run tells you very little and the trajectory is the unit you have to judge.\n\nNo. Frameworks such as LangChain, Agno, CrewAI, and the OpenAI Agents SDK exist to make the harness easier to build, not to make it possible. The loop is reason, act, observe, repeat, and plenty of teams write their own.\n\nStart with the harness, because it is the part you can change immediately. Prompts, tool descriptions, context rules, stop conditions. Go to the model when the same failure keeps showing up across trajectories after the harness is clean.\n\n*Overmind is the model training platform for AI teams. It turns your production traces into specialised models you own. [Get started](https://console.overmindlab.ai/?utm_source=devto&utm_medium=syndication&utm_campaign=research-repost).*", "url": "https://wpnews.pro/news/the-anatomy-of-an-ai-agent-model-loop-tools-memory", "canonical_source": "https://dev.to/tyler007/the-anatomy-of-an-ai-agent-model-loop-tools-memory-207g", "published_at": "2026-09-23 11:50:46+00:00", "updated_at": "2026-09-23 11:58:56.914599+00:00", "lang": "en", "topics": ["ai-agents", "large-language-models", "ai-tools", "agent-protocols", "ai-infrastructure"], "entities": ["Overmind", "LangChain", "Agno", "CrewAI", "OpenAI", "Anthropic", "Model Context Protocol", "OpenAI Agents SDK"], "alternates": {"html": "https://wpnews.pro/news/the-anatomy-of-an-ai-agent-model-loop-tools-memory", "markdown": "https://wpnews.pro/news/the-anatomy-of-an-ai-agent-model-loop-tools-memory.md", "text": "https://wpnews.pro/news/the-anatomy-of-an-ai-agent-model-loop-tools-memory.txt", "jsonld": "https://wpnews.pro/news/the-anatomy-of-an-ai-agent-model-loop-tools-memory.jsonld"}}