Rollout Playbook

The recommended path from a pilot Mac to enforced policy across a fleet — what to do in each phase, what evidence to look for, and how to promote rules without breaking developer workflows.

A rollout that starts with blocking generates a queue of angry tickets and gets switched off. A rollout that starts with watching produces the evidence you need to block the right things. This is the sequence that works.

This page is for whoever owns AgenShield for the organization. Developers should read Working with your agents.

The five phases

1. Prepare the tenant

   Configure posture in the Frontegg Portal before any Mac enrolls.

2. Pilot on a handful of Macs

   Prove install, approvals, and protection work in your environment.

3. Roll out in monitor mode

   Deploy widely with nothing blocked. Collect two weeks of real activity.

4. Turn evidence into rules

   Approve what is legitimate; write narrow rules for what is not.

5. Promote to enforcement, one rule at a time

   Never fleet-wide in one step.

Phase 1 — Prepare the tenant

Decide these in the Frontegg Portal first. None of them touch an endpoint.

Decision	Recommendation for a first rollout
Default enforcement mode	monitor. Change this only when you have evidence.
Which agents to govern	Start with the one your team actually uses most — Claude Code, Cursor, Codex CLI, Gemini CLI, or any other agent from the catalog.
Redaction patterns	Add any organization-specific credential formats now, so they are stripped from day one.
Network inspection	Leave off initially. Turn it on later if you need to see request contents.
Skill governance	Start in observe-only; move to approval once you know what is in use.

Then create an install campaign. It produces the install link your rollout channel distributes, plus the ready-made configuration profile and installer package for a managed fleet. The token only authorizes enrollment, is revocable, and is not an admin credential — so it is safe to hand to a device management system or an onboarding doc.

Phase 2 — Pilot on a handful of Macs

Pick 3–5 developers who use AI agents heavily and will tell you when something feels wrong.

Follow the Quickstart on each, or the MDM guide if the fleet is managed. What you are proving:

• the install completes and the Mac enrolls
• the three macOS approvals land — pre-approved by MDM, or granted by hand
• the agents on the Mac work normally under the received policy
• activity from that Mac appears in the Frontegg Portal

Fix anything that surfaces here. It is far cheaper than fixing it across 200 machines.

Phase 3 — Roll out in monitor mode

Deploy to the wider group with the default mode still monitor. Nothing is blocked. Let people work normally for at least two weeks — long enough to capture a release cycle, a dependency upgrade, and someone’s unusual workflow.

What you are collecting:

Question	What the evidence tells you
What do agents actually execute?	The real command surface, not the one you assumed
Which hosts do they need?	Package registries, model APIs, and internal services
Which files do they read?	Whether they are near credentials, keys, or customer data
Which skills and connectors are loaded?	The extension surface you did not know you had
Does inspected traffic carry credential patterns?	Where secrets are actually leaking

Telemetry is where all of that is collected, and Enrolled devices tells you whether the fleet is actually reporting. Resist the urge to write rules during this phase — incomplete evidence produces brittle rules.

Phase 4 — Turn evidence into rules

Now work through what you saw. The principle: narrow rules scoped to a specific agent, never broad global blocks.

What you observed	What to do
Agent reaches required vendor and registry APIs	Add agent-scoped allow rules for those hosts
Agent reaches something unexpected	Add the deny in monitor first; confirm nothing legitimate matches
Agent reads credential files	Deny scoped to that agent, that path, that operation
Agent launches a risky child tool	Rule scoped to the agent, not a global command ban
An unknown skill appears on many Macs	Review it once, then approve or block it for everyone
Lots of harmless denials in the evidence	Narrow the rule’s scope — do not widen the allow

Use the trace view rather than event counts. One trace shows the root agent, the child command, the file it read, the request it made, and the decision — which is usually enough to tell “legitimate” from “worth blocking” at a glance.

A trace — one agent run as a tree of process, file, and network events, each with its decision.

Rules and policy covers how a rule is built from grants; Agent resources covers the skills and connectors, which are approved rather than ruled on.

Phase 5 — Promote to enforcement

Promote one rule at a time. A rule’s own mode overrides the fleet default, so you can leave everything in monitor and turn on exactly one rule.

For each rule:

1. Promote it to enforce in the Frontegg Portal.
2. Wait a full working day.
3. Check the Frontegg Portal for blocks against that rule.
4. Legitimate blocks mean the rule is too broad — narrow it and repeat.

Once a cluster of rules is stable, consider moving the fleet default from monitor to audit — deny rules block, anything unmatched is still allowed. Move to enforce, where only explicitly permitted activity is allowed, only when your allow rules genuinely cover normal work.

See Enforcement modes for the exact semantics of each mode.

Keeping it running

The loop does not stop after the first enforced rule.

Review	Trigger	Action
Open alerts	New alert, or a daily security review	Acknowledge, resolve, or convert into a rule
New or changed skills	A skill appears or its content changes	Approve, block, or ask for a re-review
Network outliers	An unexpected host, a volume spike, a secret pattern	Allow, deny, inspect, or extend redaction
Endpoint health	Missing check-in, inactive extension, stale policy	Repair the endpoint, or re-enroll it
Policy impact	High denial counts, or a developer complaint	Narrow the rule’s scope or step it back to monitor

Common mistakes

• Starting in enforce. You will block legitimate work you did not know existed, and lose the room to iterate.
• Broad global rules. “No agent may run curl” breaks more than it protects. Scope to the agent and the specific case.
• Skipping the pilot. macOS approval behavior varies with your MDM and any other security software present. Find that out on 5 machines, not 200.
• Promoting several rules at once. When something breaks you will not know which one did it.
• Treating monitor as “not secure yet”. Visibility into what your AI agents are doing is already more than most organizations have.

Next

Enforcement modes

Exact semantics of monitor, audit, and enforce.

MDM deployment

Zero-touch rollout with no prompts on the endpoint.

Rules and policy

Building a rule out of grants, and getting it to the fleet.

Telemetry

The evidence every phase above depends on.