This guide shows how to use the Agent Control Center to turn recurring documentation work into structured AI tasks. It focuses on a safe rollout: quality scans f...
This guide shows how to use the Agent Control Center to turn recurring documentation work into structured AI tasks. It focuses on a safe rollout: quality scans f...
This guide shows how to use the Agent Control Center to turn recurring documentation work into structured AI tasks. It focuses on a safe rollout: quality scans first, scheduled tasks second, and autonomy only after the team understands the results.
Who this is for
- teams with recurring docs maintenance work
- admins and leads configuring project-level automation
- organizations that want AI assistance without uncontrolled autonomous behavior
What the Agent Control Center covers
The Agent Control Center brings together:
- workspace AI policy context
- content scope and automation safety
- task creation
- scheduled runs
- quality scan reporting
- review-oriented automation behavior
It is the right surface when you want AI to work on documentation continuously, not just interactively.
Step 1 — Confirm the policy and permissions first
Before creating tasks, verify:
- the workspace AI policy allows the needed surfaces
- the project permits the required automation behavior
- the team understands the review model that applies to the generated work
If the policy is still being defined, finish that work first. Automation should inherit clear policy rather than forcing policy decisions mid-rollout.
Step 2 — Start with a quality scan, not a scheduled task
Use the quality-scan workflow first because it gives the team a low-risk way to see how the agent evaluates the documentation without immediately putting recurring automation into motion.
In the Agent Control Center:
- choose the project
- set the scan cadence context you want to evaluate
- decide whether to include drafts
- run the quality scan
The resulting report typically helps you answer:
- how many pages were scanned
- average score and category averages
- how many pages need work
- what the high-priority issues are
- what the recommended focus areas should be
Step 3 — Use the scan report to choose automation candidates
Good first automation tasks are recurring, structured, and low-ambiguity.
Examples:
- freshness checks
- quality audits
- link checks
- SEO audits
- terminology checks
Poor first tasks are broad, high-risk, or open-ended, such as autonomous rewriting across sensitive sections without an established review process.
Step 4 — Create the first scheduled task conservatively
When you create a task, define:
- the task name
- the task type
- the output mode
- the schedule
- the instructions
- whether drafts are included
- which locales are in scope
- whether notifications go to email or in-app
Recommended first-task pattern:
- task type focused on quality or freshness
- output mode set to suggestion-oriented behavior
- a weekly cadence
- notifications enabled
This creates a sustainable signal without overwhelming the team or introducing too much automation too early.
Step 5 — Write instructions that are specific and editorial
The agent performs better when the task instructions define what good output looks like.
Strong task instructions should specify:
- what to scan or generate
- which quality dimensions matter
- what counts as high priority
- whether the agent should prefer comments, suggestions, or draftable changes
- any terminology or safety constraints that must be respected
Avoid vague instructions such as “improve the docs.” Prefer concrete instructions such as “prioritize weak headings, thin introductions, and pages with poor SEO snippets.”
Step 6 — Keep output mode aligned with risk
The task output mode matters as much as the task itself.
Use:
- suggestion-oriented output for broad audits and early rollout
- draft-oriented output only when the team has a clear review queue
- more autonomous behavior only for low-risk, well-understood tasks
Step 7 — Decide whether drafts belong in scope
Including drafts can be useful when:
- the team wants earlier detection of structural quality issues
- major sections are being authored actively
- the workflow should catch problems before publication
Exclude drafts when:
- the team only wants stable, reader-visible quality signals
- draft churn would create too much noise
Step 8 — Use notifications so the work gets seen
A good automation task is useless if the result disappears into a surface no one checks.
Turn on:
- email notifications for accountable owners
- in-app notifications for operational visibility
Route the outputs to the people who can actually act on them.
Step 9 — Review the first few runs manually
For the first automation cycles:
- inspect the report or suggested output closely
- note false positives
- tighten task instructions
- adjust cadence if the task is too noisy or too sparse
This is normal. Early tuning is part of building a reliable automation layer.
Good starter tasks
- weekly content quality scan
- weekly stale-content sweep
- scheduled link-health review
- monthly terminology consistency pass
- periodic SEO cleanup for key sections