An AI chat tool, or what is better known as a chatbot, responds to a prompt with text. You ask a question, it answers, and the task returns to you. An AI agent takes a prompt and works through it, planning the steps, calling the tools those steps require, and returning a finished artifact. These can be spreadsheets, documents, presentations, and more.
The interface is often similar or even identical. But while a chat tool produces one response and stops, an agent runs a loop of plan → act → verify and repeats until the task is complete.
The Ajelix interface available at chat.ajelix.com is agentic, meaning you are talking to an AI agent.
AI Chat
An AI chat tool generates a response to a prompt using a language model, with little to no ability to act beyond producing text. It is conversational by design: you can refine, re-ask, and redirect within the same message thread.
Key features:
- A single response per turn;
- General knowledge, explanation, drafting, and summarization;
- Iteration by re-prompting;
- Text output only; no file creation or tool use;
- Basic visuals rendered inline, such as tables and simple diagrams.
| Task category | Example prompts |
|---|---|
| Explanation | Explain how compound interest works, with a worked example. |
| Writing | Draft a follow-up email to a client who missed a deadline. |
| Editing | Cut this paragraph to 50 words without losing the argument. |
| Brainstorming | Give me ten names for an internal reporting tool. |
| Quick research | Summarize the main arguments in this article. |
| Code explanation | Why does this Python loop return an IndexError? |
When To Use: When the answer is the deliverable and you can act on it yourself
AI Agent
An AI agent is a system that plans and executes multi-step work, calling tools and producing artifacts. It takes care of the loop on your behalf and returns something finished.
Key features
- A plan generated before execution, presented for approval;
- Tool and skill access: files, code execution, web retrieval, spreadsheets, documents, presentations;
- Deliverable output: .xlsx, .docx, .pptx, PDF, and interactive HTML assets;
- Self-verification, including retrying a failed step;
- Multi-step workflows where one action feeds the next;
- Code execution in an isolated environment.
| Task category | Example prompts |
|---|---|
| Data analysis | Analyze this sales CSV, flag the anomalies, and build a summary dashboard. |
| Spreadsheets | Build a twelve-month cash flow model in Excel with formulas and a chart. |
| Documents | Turn this meeting transcript into a formatted project proposal in Word. |
| Presentations | Create a six-slide Q3 review from this data, with speaker notes. |
| Research | Compare these three vendor reports and summarize the gaps. |
| Visual assets | Build an interactive pricing calculator as a single HTML page. |
| Multi-step workflows | Extract the tables from this PDF, clean them, and chart the totals. |
| File operations | Merge these invoices into one PDF, ordered by date. |
When To Use: When completing the task requires tools, files, or more than one step.
AI Chat vs. Agent: Quick Comparison
| AI chat | AI agent | |
|---|---|---|
| Output | A response | A result: file, chart, deck, or updated sheet |
| Steps per request | A single turn | Multiple steps, planned before execution |
| Tools | Text generation | Code execution, file access, web retrieval, integrations |
| Control | The user directs every turn | The agent runs the loop; the user approves it |
| Failure handling | The user notices and re-asks | The agent detects, retries, and verifies its output |
| Effort as tasks grow | Rises with complexity | Stays constant; complexity is absorbed by the agent |
| Best for | Exploration, drafting, learning | Completed deliverables |
