Use case
AI support with approvals for teams that need automation without losing control
ResolveKit is built for teams that want an AI support agent to do useful work, but not at the cost of operator visibility or risk control. Approvals are part of the support motion, not a bolt-on afterthought.
If your support automation can touch customer state, explain an account issue, or trigger product actions, the question is not just whether the model can answer. The question is whether the workflow stays commercially safe when the model is wrong, uncertain, or operating in a sensitive moment.
Why teams look for this
Most AI support tools force a bad tradeoff
Either the assistant is too weak to do anything beyond generic deflection, or it is powerful enough to be risky without good approval boundaries.
Teams often lack a clean policy layer for what can auto-run, what should stop for consent, and what must always route to a human.
When something does go wrong, operators may not have a clear trace of what the assistant saw, proposed, or executed.
Why ResolveKit fits
ResolveKit makes approvals part of the operating model
Approval requirements can be tied to the action, the workflow, and the product context instead of handled informally.
The assistant can explain what it wants to do and why before any sensitive step runs.
Operators can review session traces afterward to understand what happened and refine the policy boundary.
What changes
Safer automation rollout
Teams can start with tighter approval boundaries and loosen them only where the workflow is proven and acceptable.
What changes
Clearer operator trust
Approvals, traces, and visible action paths give support and product teams something concrete to trust and refine.
What changes
Less policy improvisation
Instead of deciding case by case in Slack or internal docs, the workflow itself carries the approval logic.
Where approvals matter
Examples of support actions that should not be a black box
Refreshing entitlements, resending account flows, or changing account-linked state that affects the user experience.
Support actions that may reveal sensitive information or have billing, access, or security implications.
Any step where the team wants a human-in-the-loop boundary before the action executes in a customer-facing session.
What ResolveKit adds
Control without killing usefulness
The assistant can still explain the issue, recommend a next step, and propose the action path without blindly auto-running everything.
Approval checkpoints keep the workflow transparent instead of burying risk behind vague promises of 'guardrails'.
Trace data helps support, product, and engineering review the exact session rather than argue over hypotheticals.