What a local AI model does
A local model runs inference on your hardware. You provide input; it produces a continuation, classification, embedding, image, or other model-specific output. The model itself does not inherently know how to open a file, browse a website, save a report, or decide whether a task is complete.
A chat interface may make a model feel like an assistant, but the underlying interaction is still request and response unless another system adds tools and state.
What a local AI agent adds
An agent supplies the operating structure around one or more models:
- a goal and stopping condition;
- tools with defined permissions;
- memory of the current task state;
- an execution loop that observes outcomes;
- error handling and escalation;
- artifacts that can be saved and reviewed.
The model helps choose or perform an action. The agent runtime determines which actions are available, executes them, and feeds observations back into the next decision.
The practical difference
| Capability | Local model | Local agent |
|---|---|---|
| Generate text or media | Core capability. | Uses a model to generate within a workflow. |
| Use files and tools | Not by itself. | Yes, when explicitly provided and permitted. |
| Work through several steps | Only if an external caller manages the sequence. | The runtime manages an iterative loop. |
| Know when work is done | No independent task state. | Can compare observations to a defined stop condition. |
| Save an artifact | Produces output for another system to save. | Can use a tool to create and organize deliverables. |
| Risk surface | Primarily model input/output and local resource use. | Also includes every tool, permission, and action path. |
Example: analyzing a spreadsheet
A model can answer questions about rows included in a prompt. An agent can open an authorized spreadsheet, inspect its columns, choose an analysis method, calculate results, detect a formatting problem, retry, create charts, and save a report. The agent is not necessarily smarter; it has a process and tools.
That process should remain inspectable. A useful analysis includes the calculation path, assumptions, and a clear distinction between measured results and hypotheses. See the full private data-analysis workflow.
Can one agent use several models?
Yes. Different models can specialize in planning, text, images, video, embeddings, or other tasks. The agent runtime can route a step to an appropriate capability. The choice can also depend on execution location: a smaller model may run locally while an approved trusted device provides another model.
Does a local agent require a local model?
No. A local agent runtime can call a remote model, just as a cloud workflow can call a model running on a private endpoint. For privacy evaluation, record both decisions:
- Where does the agent runtime execute tools and store task state?
- Where does each model process the information it receives?
If the model receives sensitive source content remotely, local tool execution does not make that model request local.
Which do you need?
- Choose a local model interface for direct prompting, offline generation, or a simple private chat.
- Choose a local agent when the work requires tools, iteration, artifacts, or repeatable multi-step execution.
- Choose a private network when approved devices need to contribute capabilities beyond one machine.
If you need an answer, a model may be enough. If you need a result produced through a sequence of actions, you need an agent system.
Continue with the complete local AI agent guide or learn how to structure your first local task.
Move from prompts to inspectable work
Use models inside bounded agent workflows that produce artifacts you can review.