AI agents vs AI assistants: what’s the difference
The two terms get used interchangeably, but they describe different jobs. An assistant answers a question. An agent completes a task that involves more than one step, and often takes action on your behalf.
What an assistant does
An assistant is reactive: you ask, it retrieves relevant information, and it responds with an answer grounded in that information, sources attached. Fenlo’s assistant works this way, using retrieval-augmented generation to pull from your connected tools rather than answering from general training data.
The assistant’s job ends at the answer. It does not update a ticket, send a message, or change anything in a connected system. If the question needs more than one document to answer, it can run deep research, querying multiple sources and reconciling conflicting information before responding, but the output is still information, not action.
What an agent does
An agent is built to complete a task, which usually means multiple steps and, often, taking action in a connected system. An agent scoped to your Jira project might read a bug report, check whether a related fix already shipped, and update the ticket status, instead of only telling you what it found.
Three things distinguish an agent from an assistant:
A defined scope. An agent is configured to specific knowledge: a set of repositories, a Jira project, a folder in Google Drive, plus custom instructions for how it should behave.
The ability to act. An agent can execute actions like drafting a document, updating a ticket, or triggering a workflow, rather than only describing what it would do.
Judgment about when to ask. A well-built agent runs non-destructive actions on its own and pauses for human approval before anything sensitive, like merging code or sending an external message. If a step fails, it reads the error and either self-corrects or stops and asks for help instead of failing silently.
Side by side
| Assistant | Agent | |
|---|---|---|
| Primary output | An answer, with citations | A completed task, sometimes with side effects |
| Interaction pattern | One question, one response | Multiple steps, possibly over time |
| Takes action in other systems | No | Yes, with approval gates for sensitive steps |
| Scope | Whatever the user has access to | Configured to a specific set of sources and instructions |
| Best for | “What does this error mean” | “Triage this bug and update the ticket” |
When to use which
Use an assistant when you need an answer to move forward yourself: understanding a decision, finding a document, checking what a ticket says. It is faster to set up because there is nothing to configure beyond what you already have access to.
Use an agent when the same multi-step task happens repeatedly and the steps are well-defined enough to describe in advance: triaging a category of support ticket, drafting a first-pass status update from connected sources, running a recurring check across a set of repositories. Agents can be shared with a team or the whole workspace once they are configured, so the setup cost is paid once.
Most teams end up using both. The assistant handles ad hoc questions, and agents handle the small number of tasks that come up often enough to be worth defining once. Read more about what Fenlo is to see how the two fit together.