AI Agents
Agents that act inside boundaries you set.
AutoMSP deploys task-focused agents with defined tools, explicit permissions, human approval gates on consequential actions, and documented escalation paths. Capable where it's safe — stopped where it isn't.
Where agents earn their keep
Four roles we deploy most.
Customer support agents
Classify requests, draft grounded replies citing the knowledge source used, update systems of record, and escalate billing disputes, security reports, or angry customers to a human — never guess.
Internal knowledge assistants
Permission-aware answers grounded in your documents and policies. When retrieval can't support an answer, the assistant says so instead of improvising.
Revenue operations agents
Prospect research with verifiable facts only, outreach drafts personalized around something true about the recipient, CRM hygiene, and follow-up preparation — sending stays a human decision.
Research and analysis agents
Market monitoring and competitive research that separates verified facts from inference, labels assumptions explicitly, and dates every observation.
The control model
Autonomy is earned, not assumed.
Every agent operates inside the same control model, tuned per workflow: least-privilege tool access, approval gates on consequential steps, sandbox-first rollout, and complete run transcripts you can audit at any time.
- 01
Scoped grant
The agent receives exactly the tools its role requires — enforced server-side, not prompted.
- 02
Approval gate
Anything consequential pauses with exact arguments pinned for review. Approve, reject, or edit before it runs.
- 03
Sandbox rollout
New workflows start fully internal. External dispatch is a deliberate owner decision, never a default.
- 04
Audited operation
Every run is versioned and transcribed — who did what, why, with which inputs and outputs.
FAQ
The hard questions first.
- What stops an agent from doing something harmful?
- Three layers. Agents hold least-privilege tool grants — a version without a scope physically cannot execute that tool. Consequential actions (spending, publishing, sending, deploying) require explicit human approval with exact arguments recorded. And sandbox mode keeps everything internal until you deliberately enable external dispatch.
- How do you keep agent quality from degrading over time?
- Agents are versioned like software: every instruction, model choice, and permission change creates a new auditable version. Evaluation suites run recorded pass rates against golden cases before and after changes, so regressions are caught before production.
- What happens when an agent encounters something outside its scope?
- It escalates. Agents are instructed to route ambiguous, sensitive, or out-of-scope situations to a human rather than improvise — and every escalation lands in an approvals queue with full context for review.