A useful definition
A language model can generate an answer to a prompt. An agent goes further: it interprets a goal, decides what to do next, uses permitted tools, checks the result, and continues until it reaches a stopping condition. A local AI agent performs that orchestration on hardware you control.
The distinction matters because an agent can interact with files, browsers, code, images, spreadsheets, or other applications. That makes it more capable than a chat response, but also raises the importance of permissions, isolation, evidence, and human review.
A model produces intelligence. An agent turns that intelligence into a sequence of actions. A local agent keeps the execution loop close to your files, tools, and hardware.
The parts of a local agent
1. A model
The model interprets instructions and proposes the next action. It may run directly on the device, or the agent may be configured to use another approved model endpoint. Model location and agent location are separate decisions.
2. An execution loop
The loop turns a broad request into iterative work: understand the objective, choose an action, observe what happened, adjust, and stop when the requested outcome is reached or intervention is needed.
3. Tools
Tools give the agent controlled capabilities: reading a file, querying a dataset, operating a browser, generating an image, or saving an artifact. A trustworthy system should make those capabilities explicit instead of silently granting unlimited machine access.
4. Working memory and artifacts
The agent needs enough state to track the task. Durable outputs—reports, tables, images, documents, and source links—are more useful than a long chat transcript because they can be reviewed and reused.
5. Permissions and isolation
Running locally does not automatically make an agent safe. The important questions are what it can read, what it can change, whether risky actions require approval, and how its environment is isolated from the rest of the computer.
What happens when you give an agent a task?
Define the outcome
You describe the result you want, the files or sources it may use, and any constraints it must respect.
Plan and act
The agent selects tools, performs a step, and records the observation instead of trying to invent the whole result at once.
Check progress
It compares the current state with the goal, corrects errors where possible, and asks for help when a decision exceeds its authority.
Produce an artifact
The final result should be inspectable: a report, edited file, analysis, image, or other deliverable with evidence where appropriate.
Human review
You verify important claims and approve consequential actions. Local execution changes control; it does not make model output infallible.
What “local” can mean
Products often use the word loosely. It helps to separate three layers:
- Local interface: the app runs on your computer, but requests may still go to a remote service.
- Local agent runtime: planning, tool execution, and artifacts are managed on your computer.
- Local model inference: the model itself runs on your hardware.
A system can be local at one layer and remote at another. Before using sensitive data, check where the model runs, where task state is stored, whether tools leave the device, and which network services are involved.
Why run an agent locally?
- Control: you choose the machine, files, models, and permissions involved.
- Data proximity: large or sensitive local files do not need to be uploaded merely to be available to the workflow.
- Predictable access: local models can keep working without a per-message cloud dependency once installed and configured.
- Custom workflows: the agent can work close to the tools and artifacts already on the machine.
- Choice: local-first systems can still allow an explicit move to trusted devices or broader compute when the task calls for it.
Limits and risks
Local agents inherit the limitations of their models and tools. Smaller local models may be less capable on difficult reasoning. Hardware limits affect speed and model size. Agents can misunderstand instructions, choose the wrong tool, or produce confident errors.
Do not treat local execution as a substitute for backups, access control, sandboxing, source verification, or human approval. The safest agent is one whose authority matches the task.
How Local AGI approaches it
Local AGI starts with agents on your own machine and gives you three places to run work: local AI for maximum control, a Private Network of approved devices, and an optional public network for general tasks. You decide when work moves beyond your device.
For a deeper architectural comparison, read local vs cloud AI agents. If you are ready to try a workflow, follow the macOS and Windows getting-started guide.
Give a local agent a real task
Download Local AGI, choose where the work should run, and review the artifact before using it.