Novelty without a use case
Agents switched on without a defined task, measure or owner.
Salesforce Agentforce & AI
Put trusted AI into Salesforce workflows
Agentforce puts AI agents into service, sales and employee workflows on Salesforce. Making that safe and useful takes more than enabling a product: it needs trusted data, well-designed agents, governance, testing and adoption. BD-S provides the engineering behind it.
The business problem
What this solves
AI in the CRM fails for ordinary reasons: unclear use cases, untrusted data and no one accountable for what the agent does.
Data the agent cannot trust
Duplicate accounts, stale cases and knowledge nobody maintains.
No guardrails
What the agent may access, say and do has not been defined or tested.
Adoption left to chance
Staff are not shown where the agent fits their work, so it is ignored or misused.
What we deliver
How BD-S delivers Agentforce
AI readiness and use-case identification
Assessment of data, processes and skills, and a prioritised backlog of service, sales and employee use cases.
Data readiness and Data Cloud
Unified, deduplicated customer and case data as the foundation agents reason over.
Agent, prompt and instruction design
Topics, actions, instructions and knowledge grounding designed for the task and the audience.
Workflow integration
Agents connected to flows, MuleSoft APIs and records so they complete work rather than just answer.
Security, governance and testing
Access boundaries, human oversight, evaluation sets and scoring before agents meet customers.
Adoption and optimisation
Rollout, enablement, usage measurement and ongoing tuning under the managed service.
Salesforce technology
Salesforce AI capabilities
Also covered here
Data Cloud
Data Cloud
Data Cloud unifies customer, case and interaction data from Salesforce and external systems into profiles that agents, analytics and journeys can rely on. It is usually the first piece of work in an Agentforce program.
- Identity resolution and deduplication across Sales, Service and external sources
- Unified profiles and calculated insights available to Agentforce and flows
- Data ingestion from MuleSoft, files and connectors with lineage
- Consent, retention and access controls applied to unified data
Implementation
Where agents fit
AI-assisted service
Case summaries, suggested replies, knowledge answers and self-service agents on portals.
AI-assisted sales
Account research, email drafting, meeting preparation and next-step recommendations.
Employee assistance
Internal agents for policy, HR, IT and process questions grounded in approved knowledge.
Agentic workflows
Multi-step tasks such as triage, eligibility checks and status updates completed with human oversight where it matters.
Integration
Agentforce draws on Data Cloud, Service Cloud, Sales Cloud and knowledge, and acts through flows and MuleSoft APIs. BD-S also engineers AI on the Microsoft platform; Agentforce and Microsoft Copilot are separate ecosystems and we treat them that way, applying the same engineering discipline to each.
Governance and security
Security and governance
- Agent access limited to classified, permitted data and actions
- Human review points defined per use case
- Evaluation sets, scoring and regression testing before and after release
- Usage, quality and risk reporting to a governance forum
Ongoing support
Ongoing optimisation
Agents need tuning as data, policy and behaviour change. Under the managed service BD-S monitors quality, refreshes knowledge and adds topics and actions.
Salesforce Managed ServicesUse cases
Use cases
- Service agent for case status, updates and common requests
- Agent-assisted case summaries and replies
- Sales research and follow-up drafting
- Internal policy and procedure assistant
- Eligibility and triage workflows with human approval
Explore Salesforce AI opportunities
Tell us which service or sales tasks take the most time and we will identify where an agent is worth building.