What Is an AI Agent? How Agents Use Tools, and What They Can Do with Your Videos

Key takeaways

• An AI agent is a language model that uses tools in a loop: it decides what to do, calls a tool, reads the result, and continues until the task is done or it needs you.
• A chatbot answers; an agent acts. A workflow follows fixed steps; an agent chooses its own.
• MCP is the common way agents connect to tools. A desktop app with an MCP server, like KwaFlux, lets an agent run real jobs on your computer.
• Agents are good at repetitive, multi-step chores. They're not good at judging how a video looks, so you still check the result.

An AI agent is a language model that can take actions with tools, not just write replies. Given a goal, it decides on a step, calls a tool, reads what came back, and decides the next step, until the job is done or it needs to ask you.

Claude Code, Codex, Cursor's agent, DeepSeek Harness, Hermes Agent, and OpenClaw are all agents in this sense.

For video, that means an agent can do the routine steps for you, such as diagnosing a file, repairing it, and exporting it, as long as it has the right tools.

In this guide, you'll learn what makes something an agent, how agents use tools through MCP, what they can and can't do with video today, and how to keep one under control.

Contents

1. What is an AI agent?
2. Agent, chatbot, or workflow?
3. How an agent uses tools
4. Where MCP fits
5. Local agents and desktop apps
6. What an agent can do with your videos
7. What agents are bad at
8. How to keep an agent under control
9. FAQ

What is an AI agent?

An AI agent is a system in which a language model directs its own process and tool use to reach a goal, observing the result of each action before choosing the next.

Anthropic's engineering team, which builds Claude, put it this way in its guide to building agents: agents are "systems where LLMs dynamically direct their own processes and tool usage," and in practice "they are typically just LLMs using tools based on environmental feedback in a loop."

The loop is the important part. A model that writes one answer and stops isn't an agent. A model that writes a command, runs it, sees an error, fixes the command, and runs it again is.

Agent, chatbot, or workflow?

Anthropic's guide makes a useful point about choosing between them: start with "the simplest solution possible." If a fixed workflow does the job, you don't need an agent. Agents earn their keep on tasks where you can't predict the steps in advance, such as a broken file whose fix depends on what the diagnosis finds.

How an agent uses tools

A tool is a named function with typed inputs, which the agent can call. One step of the loop looks like this:

1. You give a goal: "D:\Phone\clip.mp4 won't open. Fix it."
2. The model picks a tool: a diagnosis tool, with the file's path as input.
3. The tool runs and returns a result: the file can be repaired; quick repair is recommended.
4. The model reads the result and picks the next tool: a repair tool, in quick mode.
5. It repeats until the repair is done, then tells you where the file is.

The model doesn't touch the video itself. It only decides which tool to call with which inputs. The tools, here KwaFlux's, do the actual work.

That's why good tools matter as much as a good model. Anthropic's guide recommends designing tools so they're hard to misuse, and gives an example: requiring absolute file paths stopped the model's mistakes with relative paths. KwaFlux's tools take the same approach; for the design, see why AI agents shouldn't click the screen.

Where MCP fits

The Model Context Protocol (MCP) is an open standard for connecting AI applications to tools. Before MCP, every agent had its own way to add tools. Now an app can ship one MCP server, and any MCP-capable agent can use it.

In MCP terms:

• The host is the agent app you talk to, such as Cursor or Claude Desktop.
• The server is the program that offers tools, such as KwaFlux's MCP server.
• The host starts the server, asks for its tool list, and passes tool calls to it when the model chooses one.

KwaFlux's server works with Cursor, Claude Code, Claude Desktop, Codex, DeepSeek Harness, Hermes Agent, OpenClaw, and other MCP hosts. For which one to pick, see our comparison.

Local agents and desktop apps

Some agents run in the cloud; others run on your computer. For a desktop app, the difference matters.

A local MCP server like KwaFlux's is started by the host as a program on your machine. So the agent's host has to run on the same computer: Cursor's desktop app, Claude Code in your terminal, Claude Desktop, the Codex CLI, or DeepSeek Harness, Hermes, or OpenClaw installed locally. A cloud agent running on someone else's server can't start a program on your PC.

The benefit is that the work stays local: AI processing runs locally on your GPU — media files are not uploaded for processing. Your chat with the agent still goes to the model provider, as usual.

What an agent can do with your videos

With KwaFlux connected, an agent can do these from a sentence in KwaFlux 1.0.14:

Every job appears in KwaFlux's task list, where you can watch, pause, or cancel it. Results are written next to the original file. For a full setup walkthrough, start with the quick start.

What agents are bad at

• Judging the picture. An agent reads task results, not frames. It can tell you an export succeeded; it can't tell you whether faces look natural. Watch the result yourself.
• Anything you'd point at. Removing an object, replacing a sky, or picking a subject needs someone to mark it on the frame. KwaFlux keeps those features in the app rather than let an agent guess coordinates.
• Knowing what you're allowed to use. An agent can find a link, but whether you have the right to download it is your call, not the model's. See why an AI can't approve downloads for you.
• Staying on task with vague goals. "Make it look better" invites guesses. "Run Quality Repair and export 1080p for YouTube" gets you what you meant.

How to keep an agent under control

• Keep approvals on at first. Most hosts ask before running a tool. Allow read-only tools freely and approve the ones that create files yourself until you trust your requests.
• Give full paths and clear goals. It cuts wrong-file mistakes.
• Prefer tools that write new files. KwaFlux writes results next to the original instead of overwriting it.
• Watch the task list. Everything the agent starts in KwaFlux shows up there.
• Check the result. The agent can confirm the job ran; only you can confirm it looks right.

Final thoughts

An agent is a model with tools and a loop. The model decides, the tools act, and the loop continues until the job is done. Connect an agent to a desktop app through MCP and the routine steps of video work can start from a sentence, while you keep the decisions that need human eyes.

Give your agent a video toolkit

KwaFlux includes a local MCP server for Cursor, Claude, Codex, DeepSeek Harness, Hermes, OpenClaw, and other MCP apps. AI processing runs locally on your GPU — media files are not uploaded for processing. It runs on Windows 10 (64-bit, version 1809 or later), Windows 11, and macOS 12 or newer on Apple Silicon. Importing files and exploring the app need no account. Signing in with an active paid plan is required only to export, convert, repair a file, or download a file from a link.

Download KwaFlux · See plans