Upgrade AI with Real-World Data: an Agentforce Architecture Guide

What is Upgrade AI with Real-World Data in Agentforce architecture?

In Agentforce architecture, ground agents with verified knowledge sources and scoped retrievers. This guide explains the design decisions, controls, and implementation checks needed to apply the pattern in production.

A general-purpose model does not know the current truth of your business. Grounding supplies relevant, verified material at the moment of the request so the answer can reflect approved policies, products, and customer context.

Grounding quality depends on the whole retrieval path: source quality, ingestion, chunking, indexing, retrieval scope, permissions, and the prompt that uses the retrieved passages.

Treat grounding as a supply chain

• Prefer authoritative, current, owned sources over broad collections of convenient content.
• Chunk content around complete ideas so retrieved passages remain meaningful without missing context.
• Scope retrievers to the agent's job and preserve record-level access controls throughout retrieval.
• Measure answer quality together with retrieval relevance; a good prompt cannot repair consistently poor evidence.

Prepare trustworthy knowledge

1. Inventory sources and assign an owner, refresh schedule, and retirement process to each one.
2. Remove duplication, obsolete versions, secrets, and content the agent should never surface.
3. Test realistic queries for both correct retrieval and intentional non-retrieval.
4. Monitor unanswered and poorly grounded questions to guide the next content improvements.