LLM agents are the engine behind agentic AI. A technical-yet-accessible guide to how they work, why they matter, and how MedGAN AI builds on them.
What an LLM agent actually is
An LLM agent is a large language model running inside a control loop that lets it call tools, observe results, and decide what to do next — until a goal is reached. The model is the brain; the loop is the body. Together they're the engine behind every agentic AI system shipping in 2025.
If you want the strategy view of why this matters, see agentic AI vs generative AI. This article is the technical view.
The ReAct loop in 5 steps
Almost every modern LLM agent runs a variant of the ReAct (Reason + Act) loop:
- Thought. The model emits a short reasoning step about what to do next:
Thought: I need the customer's order status before replying. - Action. The model emits a tool call:
Action: get_order(id="12345"). - Observation. The runtime executes the tool and feeds the result back into the model:
Observation: {status: "shipped", carrier: "Aramex"}. - Reasoning step 2. The model decides whether the goal is met. If not, return to step 1 with the new context.
- Final answer. When the model decides the goal is met, it emits a structured response and the loop exits.
That's it. Add memory, tool authentication, retries, and evaluation around this loop and you have a production system.
The components under the hood
| Component | What it does | Common choices in 2025 |
|---|---|---|
| Base model | Reasoning + language generation | GPT-4.x, Claude 3.5/4, Gemini 1.5/2, Llama 3 |
| Tool layer | Lets the model call external functions | Function calling, MCP, OpenAPI bridges |
| Memory | Short-term + long-term state across turns | Vector store, episodic memory, scratchpads |
| Planner | Decomposes goals into steps | Tree-of-thought, ReAct, Reflexion, planner-executor split |
| Critic | Reviews the agent's own output | Self-critique prompts, separate critic agent |
| Runtime | Executes the loop, handles errors, logs | LangGraph, OpenAI Agents SDK, custom |
When one model isn't enough, you graduate to a multi-agent system where each component above can itself be a specialized agent.
Why this works *now* and didn't work in 2022
Three capability jumps changed everything:
- Native tool / function calling in frontier models, so the loop doesn't have to be glued together with brittle string parsing.
- Long context windows (200k+ tokens) so the agent can carry a full conversation, document set, or task history.
- Reliable JSON / structured output so the runtime can trust what the model returns and act on it programmatically.
Without those three, you got a demo. With them, you get production agents. That's why every major platform suddenly looks similar at the architectural layer.
FAQ
Is an LLM agent the same as an AI agent?
In 2025, yes — almost every "AI agent" people deploy is LLM-powered. Other agent types (RL agents, symbolic planners) exist but aren't what's driving the current wave.
Do I need fine-tuning?
Usually not. Tool calling + good prompting + retrieval over your own data covers 90% of business cases. Reserve fine-tuning for high-volume, narrow tasks where it pays off.
Where does this all break?
Long-horizon tasks (50+ steps), tasks needing precise math without tools, and high-stakes decisions without human review. Architect around these limits — see the automation playbook.
How do I see this in action?
Easiest path: a real workflow on your real data. The use cases hub and customer service guide are good starting points. For the simpler chatbot-vs-agent framing, see AI agents vs ChatGPT.
How MedGAN AI helps
MedGAN AI is the team you call when you need LLM agents operated, not just demoed. We pick the right base models for your domain, design the planner-critic architecture, integrate the tools your agents need (CRM, ERP, helpdesk, custom APIs), and run the evaluation, observability, and governance that keep agents trustworthy in production. Developers get clean APIs; executives get outcomes.
Email contact@medgan.co to build with MedGAN's LLM-agent platform — or request a working pilot on your data, free, in under 30 days.