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The rest of the dashboard tells you what is wrong. Agents does some of the work. An agent takes your own Cited data — your citations, your gaps, your competitors, your sentiment — runs a fixed multi-step pipeline over it, and produces a finished artifact: a document, a report, a spreadsheet, or a block of code you can paste. The distinction that matters is the grounding. A general-purpose AI writing tool can produce a content brief, but it is guessing at your category. A Cited agent building the same brief already knows which prompts you lose, which sources AI actually cites in your category, and which competitor holds the position you want. The output is specific to your measured reality rather than to a plausible-sounding average.

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The library

Agents are grouped by where they sit in the marketing lifecycle.

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Research

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Strategy

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Create

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Optimize

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Distribute

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Measure

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How a run works

An agent is a deterministic pipeline, not a single prompt. Each one executes a fixed sequence of steps, and you watch it run as a workflow graph — every step visible, so you can see which inputs produced which part of the output. That visibility is the point. When an agent tells you a source is worth pursuing, you can trace the step that decided it. An artifact you cannot interrogate is one you have to take on faith. Every run is saved. Artifacts land in a versioned repository, so re-running an agent next month gives you a new version alongside the old one rather than overwriting it — and you can see what changed.

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Two ways in

  • From the Agents tab — browse or search the library and run an agent directly. The search covers what an agent does and what it outputs, not just its name.
  • From a task — tasks that an agent can execute carry a Run agent action, so you can go from “this is the gap” to a finished brief without restating the problem.
The second is the loop the product is built around: measurement finds the gap, Tasks ranks it, an agent does the first draft of the work.

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Credits

Agent runs are metered in credits. Different agents cost different amounts, reflecting how much work the pipeline does — a schema generator is cheap, a full article is not. Your balance is shown in the Agents tab. Credits meter runs, not artifacts: an artifact you generated stays available in the repository regardless of your current balance.

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How to get a good result

Point agents at real gaps, not hypotheses. The agents that read your data are only as good as the question. Running Content Brief on a topic you already dominate produces a competent brief for work you did not need. Start with a Research agent. Top Cited Sources and Citation Gap Analysis are cheap ways to find out where the actual opportunity is, before spending a larger run on producing something. Treat the output as a first draft with evidence. These artifacts are grounded in your data, which makes them a much better starting point than a blank page — but they are a starting point. The judgment about whether a topic is worth your quarter is still yours.