Quality

Agent Control: Skills, guards, human in the loop & traces

Letting AI talk to your users is a risk when you cannot decide how it behaves or see what it did.

Skills define how your agents behave in each situation, guards stop what they must never do, human in the loop brings your team in before sensitive actions, and traces show every step they took and why.

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Draft guard

Reply blocked · #4834

Can you guarantee a full refund regardless of usage?
Attempt
2 / 3retry_guarantee
Draft blockedPolicy violation

We guarantee a full refund no matter how much you've used the product.

Suggested rewrite

Refunds follow playbook §2.4 — manager approval required for exceptions. I can escalate your case to our billing team.

How it works

Skill · Refund requests

Rules + actions

Rules

When the user asks for a refund
Check plan and usage before answering
Refunds over the policy limit go to the team

Actions

Look up subscription
Billing
Create refund request
Helpdesk

What you get

  • Skills that combine rules and actions for each situation
  • Guards on policy, scope, tone and language, plus your own rules
  • Retry or escalation to your team when a guard fails
  • Human approval before the actions and replies you choose
  • Approve, reject, edit or answer from Slack, WhatsApp, Telegram and other chat apps
  • Step-by-step traces: reasoning, plan, knowledge searches, skills and actions
  • A shareable link to any trace
  • Adjust skills and guards from Claude or ChatGPT

Where it’s used

ActivationThe AI team that gets every user to value and to paid: onboarding and free-to-paid Plays on your stack.ExpansionThe AI team that grows revenue from the users you already have: upsell and cross-sell Plays on your stack.SupportAn AI team for support operations: N1 ticket Plays, QA and Voice of Customer, and incident handling. Copilot-first, grounded in your knowledge, on your stack.RetentionKeep the customers you have and recover the ones you lost: Success copilot, churn prevention, and churn recovery Plays on your stack.

Common questions

What if the AI makes something up?

Agents answer from your knowledge and each user’s context, and guards check every reply before it is sent. When a check fails, the agent retries or escalates instead of sending it.

Can it promise refunds or discounts we don’t offer?

Not if your guards say otherwise. Policy guards block replies that go against your rules, such as guarantees or offers you don’t make.

Can my team check an action before the agent takes it?

Yes. Mark the actions and situations that need a person, like refunds above a limit or a quote request. The agent sends the request to your team in Slack, WhatsApp, Telegram or another chat app, and only continues once someone approves, edits or answers.

How do I know what the agent did?

Every result has a trace with the reasoning, plan, knowledge searches, skills and actions, step by step. You can share it with a link.

Who decides how the agent behaves?

Your team. You define the skills and guards, start in copilot mode and move to autonomous only when you trust the results.

See Agent Control on your stack

Add to Claude
Book your demoAvailable for teams with 100k+ users
Agent Control: Skills, guards & approvals | Zentesimal