Chatbot, assistant, agent and agentic system
Commercial names can be unclear.
What you will learn
- Explain: ai is not one kind of model
- Apply the idea in an example: Chatbot, assistant, agent and agentic system
- Recognize limitations and verify the exercise outcome
Commercial names can be unclear. Useful distinctions are who chooses steps, which tools exist and which actions have effects.
You can follow this course without an account or programming. Examples use synthetic data and AI outcomes need checking.
How it works, step by step
AI is not one kind of model
Artificial intelligence is a family of methods for recognition, prediction, planning and generation. A rule-based system follows instructions written by people. Machine learning learns relationships from examples; deep learning uses networks with many layers. Generative AI produces new content. An LLM works with language, but product recommendations or defect detection may use other models. Think of a toolbox: choose a tool for the problem. A simple algorithm can be more suitable than an agent for a fixed rule.
A bounded agent loop
An agent combines a model with tools, a goal and state. It observes the request, chooses a step, receives a result and decides whether to continue. A chatbot may only generate text; an agent may request an external action. Commercial definitions vary, so inspect actual control. Define success and stopping: a verified result, a maximum step count, repeated errors or an exhausted budget. Use narrow capabilities and minimal permissions. Useful autonomy means freedom within boundaries enforced by software.
A workflow has explicit structure
A node is a stage, an edge is a transition, and state carries results between stages. A workflow has an application-defined path, although the model may choose some branches. Agentic systems include both workflows and dynamically acting agents. Nymrio offers ReAct, Reflection, Sequential, Orchestrator, Router, Parallel and Plan-and-Execute. Each changes coordination; it does not automatically make a model an expert. More agents mean more calls and contracts between roles. Start with the simplest solution that passes your tests.
The visual map
Follow the solid arrows for the main flow. Dashed blue arrows supply data or context; dashed pink arrows show feedback or returning results. Colors and shapes distinguish models, stores, decisions and outputs. On smaller screens, scroll horizontally to follow the entire diagram.
A complete example
A chatbot writes about stock. An assistant may search a catalog. An agent requests inventory through a tool. An agentic system can coordinate product and delivery specialists.
Try it yourself
- Draw four variants and mark who decides each step.
- Record the input, source and expected outcome before running the experiment. Use only the fictional data in the example.
- Follow the diagram stages. At every step record what information is received and produced; do not confuse intermediate output with the final outcome.
- Repeat after removing necessary information or making the input ambiguous. Check whether the system clarifies, stops or invents an answer.
- Compare with the explained solution. Keep the configuration, date, result and an explanation for differences. Change one thing and retest.
An explained solution
Live stock needs a current authorized source; general writing may need only an LLM. A successful exercise lets you show the connection between input, stages and outcome. When information is missing, a cautious answer is more useful than invented details. Compare more than style: check conditions, sources and operations too.
When it helps and what can go wrong
More agents do not guarantee more truth. Choose this approach when it improves a measured need. Keep a simple baseline and compare outcomes using identical inputs. One successful example does not establish reliability in every situation.
Check your understanding
Is every automation AI?
No. A fixed rule can automate something without learning or flexible reasoning.
Does more autonomy always give a better result?
No. It also increases room for errors, costs and unnecessary actions.
What outcome should this exercise produce?
Live stock needs a current authorized source; general writing may need only an LLM.
Words to remember
- AI: A family of methods, not a synonym for chatbot.
- Agent: A system that can choose steps and use tools toward a goal.
- Nod: A stage in an execution graph.
Sources and your next step
To prepare: F05 — Why can AI be confidently wrong?