> ## Documentation Index
> Fetch the complete documentation index at: https://docs.markifact.com/llms.txt
> Use this file to discover all available pages before exploring further.
# AI Agents
> Learn how AI agents work in Markifact — from task execution and approvals to custom agents and credit usage.
An **AI Agent** in Markifact is a conversational interface that can execute real marketing operations on your behalf. Unlike workflows, which follow a fixed sequence of steps, agents interpret your request and decide which operations to run, then ask for your approval before making changes.
Agents handle any marketing task you throw at them, from building a campaign and adding negative keywords to generating a presentation or pulling a quick report. Run them on demand or on a schedule.
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## How agents work
1. You describe a task in natural language (e.g. "Create a Meta campaign for our spring sale").
2. The agent determines which operations are needed and executes them.
3. Before any changes go live, the agent surfaces the results for your review.
4. You approve, reject, or ask the agent to revise.
This human-in-the-loop model means the agent handles the heavy lifting while you stay in control of what actually gets published or modified.
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## What agents can do
Agents have access to 500+ operations across all major ad platforms and marketing tools. These are the same building blocks that power workflows, so anything you can do in a workflow node, an agent can do on demand.
Common tasks include:
* Creating and modifying ad campaigns across Google Ads, Meta Ads, TikTok Ads, and others
* Pulling performance reports and data summaries
* Adding or removing negative keywords
* Building Google Slides presentations from data
* Uploading creatives and managing assets
* Running account audits and QA checks
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## Channels
You can interact with agents through multiple channels:
* **Markifact app** — the default web interface
* **Slack** — via a whitelabeled Slack app
* **WhatsApp** — chat directly with your agent
* **API** — integrate agent access into your own tools
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## Custom agents
You can create multiple agents, each configured for a specific purpose. Every custom agent has its own:
* **Name** — to identify it across your workspace
* **Instructions** — a system prompt that defines the agent's scope and behavior
* **Connected tools** — which platforms and operations the agent can access
This is useful when you want dedicated agents for different functions — one for reporting, another for campaign management, another for a specific client account, and so on.
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## AI models
Agents support multiple AI providers. You can choose which model powers each agent:
* **OpenAI**
* **Google Gemini**
* **Anthropic Claude**
You can also bring your own API keys to use your preferred provider directly. See [Bring Your Own Keys](/core-concepts/bring-your-own-keys) for setup details.
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## Agent credits
Agent credit usage is separate from workflows and is based on token consumption.
**Task Agent** costs are based on the **tokens consumed** multiplied by a **model-specific multiplier**. For example, gpt-5.2 may be treated as 1x, while higher-cost reasoning models like Claude Opus can be 5x or more.
### Long conversations
Because each message includes conversation history as context, longer threads consume more tokens per response. Markifact automatically compacts history to reduce this, but very long threads will still cost more over time. Start a fresh task when switching objectives.
### Using your own API keys
When you bring your own API keys:
* **Task Agent AI tokens** are billed directly through your provider — no Markifact credits consumed for AI usage.
* The **AI Agent node** in workflows costs a flat **1 credit per run**, regardless of tool count.
* All other AI workflow nodes (Ask, Structure, Analyze Data, etc.) cost **0 credits**.
For full credit tables and model pricing, see [Credits & Usage](/core-concepts/credits).
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## Agent vs. Workflow
| | Agent | Workflow |
| --------------- | ---------------------------------------- | ------------------------------------ |
| **Best for** | Tasks where AI decides the steps | Structured, multi-step automation |
| **Execution** | Conversational, you describe the task | Visual, you build a flow on a canvas |
| **Flexibility** | Adapts to your request dynamically | Follows a fixed sequence every time |
| **Approval** | Human-in-the-loop before changes go live | Runs automatically once triggered |
Both agents and workflows share the same underlying operations and platform connections. Choose agents when you want the AI to figure out the steps, and workflows when you want full control over every step.
Also weighing MCP? See [Agent vs MCP vs Workflow](/core-concepts/agent-vs-mcp-vs-workflow) for a full decision guide across all three.
***
## Related
Learn when to use skills, how they load on demand, and how to structure them for repeatable work.
Use an AI agent inside a workflow to add dynamic decision-making to your automations.
Full credit tables, model pricing, and usage tracking.