Agentforce

This Is Different from a Chatbot

Agentforce is Salesforce's platform for building autonomous AI agents — agents that don't just answer questions, but reason through multi-step workflows and take action inside your Salesforce org. They can close cases, qualify leads, update records, send personalized outreach, schedule appointments, and escalate when human judgment is needed.

These aren't the chatbots you tried in 2019 that frustrated everyone and got turned off in three months. These are agents grounded in your real Salesforce data, governed by the Einstein Trust Layer, and configured with guardrails that keep them on-brand and compliant.

Digital Mass builds Agentforce deployments that actually work — designed around specific, high-value use cases, grounded in Data Cloud, and integrated with your existing Salesforce org.

What We Build

From constrained Tier 1 agents to multi-channel AI workforces, we design, build, and deploy Agentforce agents grounded in your real Salesforce data — with guardrails, escalation logic, and monitoring baked in from day one.

Agent Architecture & Design

We define agent scope, persona, topic configurations, and guardrails before anything gets built. An agent that tries to do everything does nothing well. We start with the highest-impact, most contained use cases and build outward from a working foundation.

Topics & Actions Library

We configure the specific conversation topics, Salesforce Flow actions, Apex callouts, and external API connections that make your agent genuinely useful — not a fancy FAQ lookup. Every action is tested against real scenarios before it goes to production.

Data Cloud Grounding

Agentforce agents without unified customer context give generic responses. We connect your agents to Data Cloud unified profiles so every interaction is informed by the full customer picture — purchase history, support history, behavioral signals, and more.

Multi-Channel Deployment

Deploy agents across Service Cloud messaging channels, Experience Cloud portals, website chat, and voice (with Genesys or Five9 integration). Your customers interact with agents wherever they already are — not in a separate tool they have to find.

Human Escalation Design

The moment when an AI agent should hand off to a human is as important as everything the agent does before that moment. We design intelligent escalation logic so agents transfer with full context — not a cold handoff that makes your customer explain everything again.

Governance, Testing & Monitoring

Prompt auditing, response quality testing, conversation analytics, and guardrail configuration to keep agents accurate, on-brand, and compliant. We don't just build and walk away — we establish the monitoring framework so you can see how agents are performing and where to improve.

Common Use Cases

• Sales Agent: Autonomous prospect follow-up, meeting scheduling, lead qualification, and CRM update based on conversation outcomes
• Service Agent: Tier 1 case resolution, order status, returns, FAQ handling — 24/7 across every channel
• Scheduler Agent: Appointment booking, rescheduling, reminders, and waitlist management integrated with your calendar system
• Onboarding Agent: Guided customer or employee onboarding, document collection, status tracking, and escalation for exceptions
• Field Service Agent: Work order creation, technician dispatch status, parts availability checks, and customer communication

Common Concerns We Address

• "We tried a chatbot two years ago and it was terrible. How is this different?" — Agentforce agents use LLMs and the Atlas Reasoning Engine to understand intent and take action, not keyword matching against a decision tree. The difference in capability is not incremental.
• "We're worried about AI saying the wrong thing to a customer." — The Einstein Trust Layer masks PII, provides audit trails, and applies your configured guardrails to every response. Agents stay within the scope you define.
• "We have Agentforce licenses but no one who knows how to build agents properly." — That's exactly what we do.

Typical Timeline

Initial Agentforce deployments — 1–2 agents with constrained, well-defined use cases — typically run 6–10 weeks. Multi-agent architectures with Data Cloud grounding run 12–18 weeks.

Weeks 1–2: Sprint Zero & Agent Design

Use case scoping, data grounding audit, action library definition, guardrail design, and success metrics. We define what the agent is allowed to do — and what it isn't — before a single topic is configured.

Weeks 3–6: Agent Build & Tuning

Topic configuration, Flow and Apex action development, prompt engineering, Einstein Trust Layer guardrails, and iterative testing against real conversation scenarios — not just happy paths.

Weeks 7–8: UAT & Deployment

User acceptance testing, channel integration (web, Service Cloud messaging, Experience Cloud portal), go-live with monitoring dashboards, and post-launch tuning based on real conversation data.

Weeks 9–18: Scale & Multi-Agent

Additional agent builds, Data Cloud grounding for richer context, advanced multi-channel deployment, and agent performance review cycles that continuously improve accuracy and coverage.

Technology

Salesforce Agentforce · Atlas Reasoning Engine · Agent Builder · Prompt Builder · Data Cloud · Einstein Trust Layer · Salesforce Flow Actions · Apex Actions · MuleSoft API Actions · Genesys / Five9 Integration · Slack AI · Einstein Analytics