Agentforce, Einstein, or any LLM-backed integration executes inside Salesforce as a user — with a profile, permission sets, and your sharing model, but without a human's judgment about what it shouldn't touch. We assess whether your org, as configured today, could adopt AI agents safely and usefully — and what has to change first.
Assessed from your org's actual metadata against a fixed rubric — not a workshop, not a slide deck.
We identify every identity an AI feature would execute under — and flag the ones holding admin-grade access, where an agent's actions become indistinguishable from an administrator's.
Broad org-wide defaults and permission sprawl mean any agent user sees the whole org the moment it's enabled. We map which revenue and PII objects an agent would inherit, regardless of how carefully its own permissions are scoped.
If your permission architecture can't describe "read-only, these five objects, nothing else," then granting an agent anything means granting it a profile built for humans. We test whether the boundary is expressible — and design it if not.
An LLM grounding on undescribed fields and near-duplicate picklists produces confidently wrong answers. We measure description coverage, picklist hygiene, and record-type sprawl on the objects agents would actually use.
AI & agent readiness is the twelfth domain of our architecture audit rubric — buy it standalone or get it included.
Read-only metadata access or a self-serve export, then a scored report: execution identities, inherited access, scopability verdict, grounding-quality metrics, and a sequenced path to safe enablement. Buy now →
The full audit covers the same AI-readiness domain plus permissions, security, automation, data, licenses, and governance — the context that determines whether the AI answer is actually safe. Audit pricing →
Request the assessment through the audit form — pick "AI readiness" in the notes — or start with the free 12-domain self-audit checklist to see where you stand.