AI value in healthcare and public programs depends less on a polished conversation and more on the quality of the underlying retrieval, action, permission, and evaluation design. SmithBix maps the real operating decision before selecting an agent pattern.
Agentforce use-case design
We define the user, intent, source material, authorized actions, escalation path, failure conditions, and success measures for each proposed agent. This produces a delivery backlog that can be tested instead of a broad promise that cannot be governed.
Retrieval and knowledge architecture
Useful answers require authoritative, current, permission-aware sources. We design content ownership, chunking, metadata, retrieval filters, citations, and feedback loops so the answer can be traced and improved.
Evaluation before scale
We establish representative test sets, expected-answer criteria, safety cases, human review, and production monitoring. The goal is not to eliminate human judgment. It is to use automation where it is reliable and make escalation explicit where it is not.
Workflow and integration
Agents become operational when they can read the right context, create or update records, initiate governed flows, and hand work to people with the evidence intact. We connect the AI layer to Salesforce data, OmniStudio, APIs, and enterprise services.
Reference architecture should follow Salesforce platform guidance and the organization’s own security, privacy, records, and model-risk policies. See Salesforce Well-Architected.