AI agents and agentic AI systems
How generative AI, single AI agents and multi-agent “agentic” systems differ, what each can do, and a staged way to bring them into an application.
"AI agent" is one of the most used terms in AI right now, and one of the least clearly defined. This post separates three ideas that are often mixed up: generative AI, AI agents and agentic AI. It then shows a step-by-step way to bring them into a real application.
Three levels, side by side
| Generative AI | AI agent | Agentic AI | |
|---|---|---|---|
| What it does | Answers a prompt | Completes a task | Pursues a larger goal |
| Who decides the steps | You | The agent | A team of agents |
| Uses tools (search, code, APIs) | No | Yes | Yes |
| Memory between steps | No | Short-term | Shared, long-term |
| Example | A chatbot writing an email | An assistant that books a meeting | Agents that research, write and review a report together |
Generative AI: the foundation
Generative models such as large language models (LLMs) and image generators react to a prompt. They are excellent at producing text, images, code or audio, but on their own they do not plan several steps ahead, remember earlier conversations or act in the outside world.
AI agents: an LLM that can act
An AI agent wraps a language model in a loop and gives it tools: web search, a database, a calculator, an email API. Instead of answering once, it repeats a cycle until the task is done:
$$ \text{Goal} \;\rightarrow\; \text{Think} \;\rightarrow\; \text{Use a tool} \;\rightarrow\; \text{Observe the result} \;\rightarrow\; \text{Think again} \;\rightarrow\; \cdots \;\rightarrow\; \text{Answer} $$
Typical single-agent jobs: customer support that can look up an order, searching a company's internal documents, sorting emails, or scheduling meetings.
Agentic AI: a team of agents
Agentic AI goes one step further: several specialised agents work together on a goal that is too big for one. A planner splits the goal into subtasks, workers handle them, and a reviewer checks the result:
$$ \text{Goal} \;\rightarrow\; \text{Planner} \;\rightarrow\; \begin{Bmatrix} \text{Researcher} \\ \text{Coder} \\ \text{Writer} \end{Bmatrix} \;\rightarrow\; \text{Reviewer} \;\rightarrow\; \text{Result} $$
What makes this work is task decomposition (splitting the goal), shared memory (agents see each other's results) and coordination (clear rules for who does what).
Examples include multi-agent research assistants, several robots dividing up a warehouse, and decision support where one agent gathers evidence and another checks it.
Bringing AI into an application, step by step
It is tempting to start with a team of agents. It is usually better to grow into it:
| Stage | Add | Examples |
|---|---|---|
| 1. Generative AI | Text or image generation in one place | Product descriptions, FAQ chatbot, translation, summaries |
| 2. Single agents | An LLM that can call your APIs and data | Personal recommendations, automatic replies, order lookup |
| 3. Agentic system | Several agents with shared memory | Complex workflows, long research tasks, planning across teams |
Move to the next stage only when the previous one works reliably and you can measure that it helps your users.
Problems you will meet, and what helps
| Problem | What happens | What helps |
|---|---|---|
| Hallucination | The model invents facts | Retrieval-augmented generation (RAG): give it real documents to answer from |
| Prompt sensitivity | Small wording changes give very different results | Fixed prompt templates, tests that run on every change |
| Coordination failures | Agents repeat work or contradict each other | Clear roles, structured messages, a final reviewer |
| Unexpected behaviour | Agents together do something no one planned | Logging, human approval for important actions, testing in a sandbox first |
On top of these come questions of trust: can you explain why the system decided something, who is responsible for its actions, is it fair, and is user data protected?
Good practice when building agents
- Ground answers in data with RAG instead of relying on what the model remembers.
- Keep each agent small and focused, with one clear job.
- Plan for failure: time limits, retries and a safe fallback when a tool call fails.
- Test in a sandbox before an agent can send emails, spend money or move a robot.
- Measure: log every step, collect user feedback and compare versions with A/B tests.
- Keep a human in the loop for anything that cannot be undone.
Where it is heading
Better long-term memory, more reliable reasoning, agents that work across devices and apps, and closer links to the physical world, for example agents that plan tasks for robots. The technology is moving fast, but the principles above stay the same: start simple, ground the model in real data, and keep people in control of the important decisions.