In-product agents that take action on your users data — a real feature with guardrails, not a bolted-on chat box.
SaaS teams feel pressure to ship an AI feature, and most ship a chat box that answers questions but cannot do anything inside the product. The opportunity is an agent that acts on the user account — configures a workspace, runs an analysis, completes an onboarding step, triggers the right internal action — with the tenancy, permissions, and guardrails a multi-tenant product demands. That is a feature engineering problem, not a prompt.
Users get an agent that does the work in-app, not a chat box that only answers
Onboarding and activation steps complete with the agent doing the setup
Every action respects tenancy, permissions, and plan limits by construction
The feature ships with evals and guardrails, so you can change it without fear
A support chatbot answers; an in-app agent acts — configuring workspaces, running analyses, completing onboarding — through typed product APIs, inside the user account permissions.
It is bounded to the calling account data and permissions, with confidence thresholds, confirmation gates on high-impact actions, and full logging — tenancy and limits are enforced by construction.
We ship the agent with an eval suite and guardrails, so model behavior is measured and regressions are caught before users see them — the equivalent of tests for a non-deterministic feature.
