Agents

Use this page to control which agent should do each type of work. This is where you standardize output quality across your team.

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Questions this page should answer

1. Which specialist should run this task?
2. Do we already have an agent for this use case?
3. Which agents need updates because outputs are weak or inconsistent?

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Before you manage agents

• Define your top use cases first: analysis, technical SEO, content, automation.
• Keep one clear purpose per agent.
• Use simple names based on outcome, not internal jargon.

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What this page gives you

• All agents view for full agent discovery.
• Category tabs to quickly narrow by job type.
• My agents workspace to manage custom agents.
• AI sidebar access using the top-right 3-boxes AI assistant button.
• Chat history to review real usage before editing instructions.

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All agents view (starting point)

Start here when you want to pick the right specialist quickly. How to use it:

• Use All agents for full discovery.
• Use Analytics, Technical SEO, and Content tabs when routing is unclear.
• Check recent chat history to reuse successful prompts before starting from zero.

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AI sidebar (quick actions on Agents)

Use this when you want to stay on the Agents page while running quick AI tasks.

1. From the Agents page, use the top-right 3-boxes AI assistant button.
2. The AI sidebar opens on the right for prompts and quick actions.
3. Keep using the same Agents view while running analysis or content tasks.

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My agents page (management workspace)

This is where you maintain your custom agents. Use My agents to:

• Review your custom agent list.
• Spot duplicates and overlap.
• Identify low-value agents to merge or retire.
• Decide which agents need instruction updates.

Practical rule: if two agents solve the same problem, keep one and improve it.

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Create agent flow (new specialist)

Create a new agent only when an existing one cannot be improved to cover the task. What to fill carefully:

• Name: outcome-focused and easy to route.
• Description: one sentence on what this agent produces.
• Instructions: behavior, boundaries, and response format.
• Use cases: concrete quick actions users can click.
• Allowed tool categories: limit tools to reduce noisy or risky output.

A good instruction block should include:

1. What the agent should do
2. What it should avoid
3. Expected output format
4. Decision rules when data is incomplete

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Edit agent flow (quality improvement)

Editing is where most quality gains happen. When to edit an agent:

• Outputs are too generic.
• Answers are correct but not actionable.
• Team members use long prompt workarounds to get useful results.
• The same mistakes appear across multiple runs.

How to improve fast:

1. Review 5-10 recent runs for repeated failure patterns.
2. Tighten instructions with explicit output structure.
3. Add or remove tool access based on mistakes.
4. Re-test using the same prompt set and compare results.

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Weekly agent operations routine

1. Review top-used agents and weakest outputs.
2. Update one high-impact agent each week.
3. Remove redundant agents.
4. Promote proven prompts into reusable workflows.

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Keep in mind

• More agents do not mean better operations.
• Clear scope beats “do everything” instructions.
• Most performance issues come from vague instructions, not model quality.

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Where to go next

• New chat: run requests with the right agent selected
• Workflows: automate repeated agent tasks
• Schedule: run recurring automation on a fixed cadence