Comparison / AIGIS resources
Agentforce is strongest when the center of gravity is Salesforce. AIGIS is built for the harder enterprise reality: Salesforce plus SAP, ServiceNow, data warehouses, and custom databases that all need one governed AI layer.
Executive read
Use Agentforce when Salesforce is the only system that matters.
Use AIGIS when questions and actions need to span Salesforce, SAP, ServiceNow, and databases.
AIGIS enforces field stripping before the LLM sees context, rather than relying only on downstream response controls.
AIGIS is model-agnostic and priced as a platform, not a per-conversation toll.
Analysis
Agentforce is a native Salesforce AI product. That makes it powerful for organizations that already standardize work inside Salesforce and want AI embedded directly into Salesforce workflows.
The tradeoff is strategic dependency. The more systems a company runs, the more every cross-system request becomes an integration, identity, and governance problem layered around the Salesforce center.
AIGIS starts from the assumption that no single vendor owns the enterprise. It routes intent across systems, resolves identity per system, enforces object, record, and field permissions, then strips inaccessible fields before prompt construction.
That means the LLM receives only data the user is allowed to see. The model does not receive masked placeholders for hidden fields, so it cannot infer that those fields exist.
Salesforce AI pricing can be attractive for narrow use cases, but the multi-vendor stack becomes expensive when companies also buy Now Assist, Joule, Copilot, and an integration platform to bridge them.
AIGIS is the consolidation layer: one governed platform across the systems you already run, with your cloud and your LLM keys as the default deployment model.
Comparison
Keep reading
Use case
AIGIS routes questions across Salesforce, SAP, ServiceNow, databases, and warehouses while enforcing each system's native permissions.
Architecture
Why LLM field-level security should remove inaccessible fields before prompt construction instead of sending masked placeholders to the model.
Architecture
A practical architecture for enterprise AI governance middleware: identity resolution, live record checks, field stripping, model routing, write approvals, and audit provenance.