Agentforce, applied where the use case, data and controls are ready
We help teams across Service, Health, Financial Services and Marketing Cloud choose the right Agentforce use cases, prepare the data and actions behind them, and launch agents that stay within defined limits and hand off to people cleanly.
Buyer challenges
Why AI pilots stall before production
- 01
No clear first use case
Ideas are many, but value, effort and risk have not been compared.
- 02
Content not ready
Knowledge is out of date, duplicated or not approved for customer use.
- 03
Data gaps
The records an agent needs are incomplete or spread across systems.
- 04
Unclear limits
No agreed rules on what the agent may read, change or decide.
- 05
Hard to test
Teams cannot show how the agent behaves on difficult or unsafe requests.
- 06
Low adoption
Agents and customers do not trust answers they cannot trace.
Framework
One governed sequence, from use case to measured value
Not every engagement needs Agentforce. When one does, it is designed in from the start.
- 01
Use Case
- Service and Health
- Financial Services and Marketing
- 02
Trusted Context
- Answers from approved Knowledge
- Customer context
- 03
Governed Actions
- Defined actions through Flow and APIs
- Least-privilege access
- 04
Human Oversight
- Guided customer requests
- Handoff to a person, with context
- 05
Measured Value
- Testing and trace review
- Tuning after launch
Agentforce, applied across
Services
From first idea to managed agent
How it works
Grounded, bounded, tested and traceable
Grounded data
Approved Knowledge and Salesforce records as the only sources the agent answers from.
Instructions
Agent design, prompt and instruction design — and clear rules for what the agent is allowed to do.
Actions
Record changes that follow fixed rules, through Flow and integrations, with Apex only where justified.
Human handoff
Handoff through Omni-Channel to the right queue, with conversation and case context attached.
- Least-privilege access
- Confirmation before changes
- Escalation rules
- Approved grounding only
Boundaries
What a governed agent always does, and never does
- Guess which customer record matches
- Invent an answer that is not supported
- Make clinical, financial, legal, eligibility, coverage, benefit, refund, payment or claim decisions
- Report an action as done when it failed
- Use approved grounding only
- Restrict fields and actions to what the use case needs
- Confirm before material record changes
- Say when an answer is not available
- Hand off to a person with context when rules require it
Agentforce use cases
Where Agentforce fits in each area
Service Cloud
- Case summaries and classification
- Answers from approved Knowledge
- Case creation and status updates
- Safe handoff through Omni-Channel
Financial Services Cloud
- Advisor and banker assistance
- Approved policy and procedure answers
- Onboarding and document guidance
- Guarded next actions
Health Cloud
Grounded answers to approved service questions, case actions and handoff — never clinical, eligibility or coverage decisions. The IMR Resolve showcase includes a healthcare overlay on synthetic data.
Agentforce for HealthMarketing Cloud
Help preparing segments, drafting content for human approval and summarizing journey results, inside your communication rules.
Agentforce for MarketingAgentforceAgentforce for ServiceIMR Resolve
Agentforce for Service · Showcase / solution concept
IMR Resolve | Governed Agentforce Service Concierge
IMR Resolve is a solution concept. It shows how a governed service agent on Agentforce and Agentforce Service would answer routine questions, manage Cases and hand complex requests to the right person.
Job 01
Customer record match
Matches a customer record from an identifier or email. Record matching is not authentication or identity verification.
Healthcare service
- Matches the synthetic member's record
- Retrieves approved Knowledge
- Retrieves service and Case context
- Creates or updates a service Case when appropriate
Refuses, then escalates
Clinical, eligibility, benefit, coverage or other restricted decisions.
Banking, financial services and insurance
- Matches the synthetic customer's record
- Retrieves approved account and service context
- Answers only approved, grounded questions
- Retrieves, creates or updates a service Case
Refuses, then escalates
Financial advice, underwriting, eligibility, claims, payment authorization, ownership changes or other restricted decisions.
The overlays are illustrations on synthetic data. Neither makes medical, financial or insurance decisions, and neither claims regulatory compliance.
- 01
Explain the business problem.
- 02
Match a synthetic customer record.
- 03
Retrieve service and Case context.
- 04
Answer a question from approved Knowledge.
- 05
Create or update a Case, with confirmation.
- 06
Show an unsupported or sensitive request.
- 07
Escalate safely to a person.
- 08
Explain testing, traceability and governance.
Planned before the showcase is shown: each scenario is to be checked in preview, regression-tested and inspected through traces.
- Happy path
- Create Case
- Knowledge answer
- Invalid or unmatched identifier
- Unsupported answer
- Sensitive request
- Action failure
- Ambiguous intent
- Request for a person
- Change of topic
- Voice and contact-center extension
- Controlled backend integration
- Unified data grounding
- Observability
Integration and ecosystem
Agents act through the systems you already control
An agent is only as reliable as the actions and data behind it.
- 01
Actions built as Flows with fixed inputs, outputs and rules
- 02
Backend context through secure APIs and named credentials
- 03
Handoff through Omni-Channel to the right queue, with context
- 04
Permissions scoped to the use case
Approach
Prove one bounded use case first
A readiness assessment selects one use case and agrees what success looks like. A proof of concept then shows the agent’s behavior, including refusals and handoffs, before a wider rollout. After launch, managed services covers content updates, test re-runs, trace review and tuning.
Discover
Name the business problem and the job a team already does.
Assess
Review Knowledge, data, permissions, processes and integrations.
Prioritize
Compare value, effort, data readiness and risk. Keep a short list.
Prove
One bounded proof, including refusals and handoffs, before a wider rollout.
Transform
Design the agent into the engagement only where the proof held.
Stabilize
Deployment readiness and enablement for the people who will supervise it.
Optimize
Content updates, test re-runs, trace review and tuning after launch.
Agentforce questions
No. We recommend Agentforce where the use case, data, actions and controls are ready. Many engagements start with service or data foundations that later support it.
No. IMR Resolve is an IMR Soft showcase on synthetic data. It is not a packaged production product, a completed customer deployment, or an AgentExchange listing. We use it to show the approach, then scope work for your use case.
No. We do not resell Salesforce licenses or quote Salesforce pricing. Licensing is arranged with Salesforce.
By limiting it to approved sources, telling it to say when an answer is not available, testing unsafe and unclear requests, and reviewing traces before and after launch.
Next step
See how a governed agent works
Discuss the IMR Resolve showcase, then agree one bounded use case and what a proof of concept should show. We reply within 24 hours.