# AI Agents for SaaS

> AI agents for SaaS products: in-app agents that act on user data, automate onboarding and operations, and ship as a real feature with guardrails. Built by Sentient Arc.

In-product agents that take action on your users data, a real feature with guardrails, not a bolted-on chat box.

## The challenge in SaaS
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.

## Example workflows

### In-product action agent
- Agent reads the user request within the bounds of that account's data and permissions
- Reasons over the task and the actions available in the product
- Calls typed product APIs to configure, generate, or complete the work
- Confirms the result to the user and logs the action against the account
- Escalates or asks for confirmation on irreversible or high-impact actions

### Onboarding and activation
- Guide a new user through setup, performing the configuration steps for them
- Detect where activation stalls and offer the next concrete action
- Respect plan limits and permissions for every action it takes

## Outcomes
- 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

## FAQ
**What makes an in-app agent different from a support chatbot?**

A support chatbot answers; an in-app agent acts: configuring workspaces, running analyses, completing onboarding, through typed product APIs, inside the user account permissions.

**How do you keep an agent safe in a multi-tenant product?**

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.

**How do we change an AI feature without breaking it?**

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.

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Canonical page: https://www.sentientarc.com/solutions/ai-agents-for-saas