AI Governance in Squash
MSPs operate from a position of trust, with privileged access across dozens or hundreds of client environments. As AI begins doing real service desk work, that trust depends on more than accuracy. It depends on clear boundaries, human approval where it matters, fact-grounded execution, and a complete record of what happened. Squash treats governance as part of the platform foundation, not a feature layered on later.
Access conditions
Control what Squash can touch
Squash uses the native permissions MSPs already rely on, then adds policy layers for global controls, client access, ticket context, runbooks, and approvals.
Policy engine
Give Squash a clear operating boundary.
Squash starts with the access you provision just like a technician, then applies more granular policy controls before it acts. Each action can be:
- Always allow
- Require human approval
- Block
Layer 01
Native access boundary
Squash starts with the same tool permissions, roles, and scopes you already use for technicians.
Layer 02
Squash global access controls
Set the default tools, scripts, workflows, and actions Squash is allowed to use across your MSP.
Layer 03
Client-level tool access
Tighten access for specific clients so each tenant has the right operating boundary.
Layer 04
Runbook and ticket guidance
Use ticket context, client notes, runbooks, and approval rules to guide what should happen next.
Layer 05
Planning inside the boundary
Squash plans the work only inside the boundary created by your permissions and policies.
In Squash
Policy engine
Squash productizes this through a policy engine that layers granular tool, script, workflow, client, ticket, and approval controls on top of the native MSP permissions already used for human technicians.
Reasoning and execution
Ground decisions before action
Squash verifies live context, uses existing MSP or client documentation where available, and evaluates policy before crossing sensitive lines.
Step 01
Gather facts
Squash starts with read actions across the ticket, client state, system records, prior history, available runbooks, and policy constraints.
Step 02
Match the right path
Where a runbook or known process exists, Squash uses that path to constrain execution while still diagnosing and adapting inside the workflow.
Step 03
Check constraints
Before crossing a sensitive line, Squash evaluates policy, approval requirements, confidence boundaries, and the current client environment.
Step 04
Act, ask, or escalate
Low-risk work can continue, approval-gated work pauses for review, and unclear cases escalate with the reasoning and supporting facts attached.
In Squash
Planning agent
Squash productizes this through an execution planner that grounds action in ticket context, system state, client environment, runbooks, and policy before sensitive work proceeds.
Visibility and audit
See what happened and why
Squash records the context around tool calls, approvals, policy changes, execution outcomes, and AI rationale. MSPs can monitor Squash like a technician by piping events into existing tools, or use Squash's built-in monitoring for patterns like high-risk tool use, cross-client anomalies, and repeated approval denials.
Audit record
Every action has context
Logins, chat submissions, tool calls, approvals, integration changes, runbook changes, permissions, and policy updates are captured with context.
Every decision is reviewable
MSPs can inspect what Squash saw, what it decided, which policy checks ran, what approval happened, and what action followed.
Useful for debugging and trust
When something unexpected happens, the record shows the AI rationale, tool input, approval decision, and surrounding ticket context.
In Squash
Audit and telemetry
Squash productizes this through audit and telemetry that makes actions, decisions, approvals, policy changes, and monitoring signals reviewable for operations, debugging, client communication, and compliance-oriented review.
Coming soon
Reversibility as a first-class control
The next layer of governance is not only whether Squash may act. It is whether the system can prepare recovery paths before material changes are made.
Pre-action recovery plan
Capture the state needed to understand and reverse a change before execution.
Cross-tool state recovery
Capture fragmented state, dependencies, and side effects so changes can be reversed across tools.
Agent-led rollback
Squash verifies the intended result and automatically undoes its actions if needed.
See Squash governance in action
Schedule a demo to see how access conditions, approval gates, fact-grounded execution, audit trails, and recovery planning come together in real service desk workflows.
Schedule a demo