Agentforce vs Einstein Copilot: What’s the difference (and what should you deploy first?)

Agentforce is Salesforce’s agent platform designed for AI that can reason and take actions using your data, workflows, and APIs. Einstein Copilot is the older name for what Salesforce now calls Agentforce Assistant—a conversational assistant embedded in the flow of work. Deploy the assistant pattern first, then expand to agents with controlled actions.

Einstein Copilot → Agentforce Assistant → (now part of) Agentforce. The name changed, but the “assistant/copilot pattern” is still a distinct deployment approach.

Agentforce is the broader platform for building agents that combine data + reasoning + actions and can leverage workflows and APIs.

If you’re deciding “what to deploy first,” start with assistant-style productivity and tight governance, then graduate to action-capable agents and finally semi-autonomous automation.

Key takeaways

Most orgs should start with a copilot-style experience (human-triggered, read-heavy, low-risk actions) to validate grounding, permissions, and adoption before automating end-to-end.

The real dividing line isn’t branding it’s autonomy and action scope: “suggest” vs “do.”

Production-ready AI in Salesforce” requires governance: data access rules, action constraints, auditability, and safety controls (Salesforce positions this under the Einstein Trust Layer).

“Actions” are where value and risk live whether they’re standard actions like updating records or custom actions you build.

In Service Cloud, the fastest ROI typically comes from case understanding (summaries/overviews), triage, and routing before you attempt fully automated resolution.

If you’ve searched “Agentforce vs Einstein Copilot,” you’re probably trying to answer one of these practical questions:

1. Are these different products or the same thing now?

2. Should we start with a conversational assistant or jump straight to autonomous agents?

3. What’s the safest path to real automation inside Salesforce (especially Service Cloud)?

Let’s solve those without the hype.

Terminology update: Einstein Copilot didn’t “disappear” it got renamed

Salesforce’s own guidance now frames “Einstein Copilot” as the earlier name for what it calls Agentforce Assistant, and notes that this assistant has been upgraded into Agentforce.

So why do people still say “Einstein Copilot”?

• Because the assistant/copilot experience is still how many teams start: a user asks a question or requests a draft, and the AI helps inside the flow of work.

• Meanwhile, “Agentforce” increasingly refers to the agent platform—the system for building agents that can plan and take actions across data and systems.

In other words: the words changed, but the implementation decision still exists.

The simplest mental model

Copilot/assistant pattern

Best for: productivity, adoption, “help me do my job faster.”

• User initiates (chat, prompt, button)

• AI responds with information, summaries, drafts, recommended next steps

• Actions (if any) are limited and usually confirmation-based

This is how Salesforce described Einstein Copilot: a customizable conversational assistant inside Salesforce, connected to platform data and actions.

Agent platform pattern

Best for: automation, multi-step execution, cross-system workflows.

Salesforce describes Agentforce agents as needing:

• Data (what’s true right now),

• Reasoning (planning/evaluation),

• Actions (doing real work via workflows/automation/APIs).

That’s the heart of the difference.

In one line:
Copilot helps users. Agents help processes.
And the moment you let AI touch workflows and records, governance becomes the real project.

What’s actually different in architecture and operations

1) Autonomy and “who’s driving”

Assistant: user drives; AI assists.

Agent: the system can drive multi-step execution once assigned a goal within constraints you define.

2) Actions are first-class (and must be governed)

Agentforce’s developer guidance treats actions as explicit executable tasks an agent can perform.

Salesforce also documents a catalog of standard agent actions (for example: query records, summarize record, update record, answer with knowledge, extract fields/values).

This matters because “AI in Salesforce” becomes real only when it can reliably:

• Read the right context

• decide correctly

• and execute safely

3) Trust layer and guardrails aren’t optional

Salesforce positions the Einstein Trust Layer as protections such as grounding in CRM data, masking sensitive data, toxicity detection, audit trails/feedback, and zero data retention agreements with model providers.

Whether you deploy an assistant first or jump to agents, your rollout will succeed or fail based on:

• permission boundaries

• data exposure controls

• auditability

• and whether actions are constrained and testable

Use cases: where each approach wins (especially in Service Cloud)

Here are real patterns that show the difference.

Assistant-first wins when the work is “interpret and advise”

Examples:

• Summarize a case thread and highlight the customer’s actual ask

• Draft a response in the right tone (with citations to knowledge)

• Pull key record details quickly (“what’s the SLA, entitlement, last contact?”)

• Suggest next steps or escalation paths

These are high-value, low-risk, and great for adoption.

Agents win when the work is “interpret, decide, and execute”

Examples:

• Classify a case, extract structured fields, set priority, assign to the right queue

• Trigger a Salesforce Flow (with approvals) to request information or initiate remediation

• Create/update records based on verified user input (with logging/audit)

Salesforce explicitly frames Agentforce as connecting data + reasoning + actions and leveraging workflows/automation/APIs to complete tasks.

Comparison table: Agentforce vs Einstein Copilot (assistant pattern)

Decision tree: what should you deploy first?

Use this as a practical “choose your path” guide.

Step 1 What’s your immediate goal?

A) “Reduce handle time and help agents respond faster.”
Start with assistant pattern:

• case summaries

• knowledge-grounded answers

• draft responses

• recommended actions (but don’t auto-execute yet)

B) “Reduce backlog by automating triage/routing and data entry.”
Start with agent pattern, but only if you can commit to:

• tight action constraints

• explicit approvals/logging

• test harnesses and rollback strategies

Step 2 How risky is the action?

If the AI will…

• Read only (summaries, Q&A): lower risk

• Write drafts (emails/comments): medium risk

• Update records / trigger Flow / send messages: high risk → requires guardrails and auditability

Step 3 Do you have the prerequisites?

If you answer “no” to any of these, deploy assistant first:

• Are your permissions and sharing model well understood and tested for AI access?

• Can you constrain actions to a small, testable set?

• Do you have an audit trail and monitoring plan (what the AI saw, decided, and did)?
  Salesforce emphasizes trust and protections via the Einstein Trust Layer framework.

  
  

What most teams should deploy first: the “crawl → walk → run” rollout

Phase 0 Data readiness (fast, but non-negotiable)

Deliverables:

• Define “source of truth” objects (Case, Contact, Account, Entitlement, Knowledge)

• Identify sensitive fields and masking requirements

• Establish evaluation set: 50–200 real cases with expected outcomes (summary quality, routing decisions, etc.)

Phase 1 Assistant pattern (human-in-the-loop)

Goal: adoption + accuracy.

Typical capabilities:

• Case summaries and overviews

• Suggested next steps

• Draft responses grounded to record/knowledge context

  
  

Why this phase works:

• You can measure value quickly

• You’re not letting the AI “break production” by taking irreversible actions

Phase 2 Action-enabled assistant (safe execution with constraints)

Goal: start capturing automation ROI.

Example design:

• AI proposes an action (e.g., “Update priority to High” / “Route to Queue X” / “Trigger Flow Y”)

• Human confirms

• Action runs

• Log everything

This aligns with how Salesforce conceptualizes actions as discrete executable units.

Phase 3 Targeted agents (semi-autonomous)

Goal: controlled autonomy for a narrow workflow.

Pick one:

• Case triage + routing

• Intake classification + field extraction

• Knowledge deflection for a single category

Keep it narrow. Expand only after metrics stabilize.

Governance checklist (the part everyone underestimates)

If you remember one thing: “agentic” is an operational capability, not a demo feature.

Here’s the checklist that separates pilots from production:

Data and access

• Map AI-visible objects/fields to permission sets

• Ensure the AI experience respects org sharing and record access

• Mask or restrict sensitive fields where needed (SSNs, payment info, etc.)

Grounding and accuracy

• Require answers to cite internal sources (record fields, knowledge articles)

• Use “I don’t know” behaviors when evidence is missing

• Build regression tests against real historical cases

Action safety

• Maintain an allowlist of actions (start with 3–10 max)

• Require confirmations for high-impact actions (updates, escalations, outbound comms)

• Add rollback/review mechanisms

Salesforce’s own docs emphasize actions and also list standard actions like updating records and querying/summarizing.

Audit and monitoring

• Log: prompt/context → decision → action → outcome

• Monitor drift: routing accuracy, hallucination rate, handle time, deflection quality

• Establish a kill switch for automation when anomalies spike

Where ConvoPro fits (for Service Cloud teams who want outcomes, not experiments)

If your focus is Service Cloud execution, ConvoPro is positioned as an enterprise AI platform built for Salesforce delivered as one package with two tools: ConvoPro Automate and ConvoPro Studio.

ConvoPro Automate is designed for AI-powered automation inside Salesforce—case overviews, triage, and routing—without rebuilding your workflows.

ConvoPro Studio is positioned as a conversational Salesforce assistant with model choice and secure conversations (avoiding single-model lock-in).

If you’re thinking in “deploy first” terms:

• Start with case understanding and agent productivity (assistant pattern)

• Then move to workflow automation with controlled actions and governance

That’s also the philosophy behind ConvoPro’s “beyond LLM + RAG” framing: production value comes from the full system—data + workflows + guardrails—not just a chat box.

A practical deployment blueprint (what to do next)

Here’s a concrete plan you can run with your Admin + Service Ops + IT/Sec team.

1) Choose one workflow (not “AI everywhere”)

Good first picks:

• Case summarization + next-step suggestions

• Case triage + routing (with human confirmation)

2) Define success metrics upfront

Examples

• Avg handle time reduction

• First response time reduction

• Triage accuracy (agreement with expert label)

• Deflection rate (if using knowledge) + reopen rate

3) Limit actions aggressively

Start with:

• summarize record/case

• query record details

• suggest routing (not auto-route)
  Then expand to:

• update record fields

• trigger Flow (with approval)

4) Put governance in writing

A one-page “AI policy” for the workflow:

• what data the AI can see

• what actions it can take

• how it’s monitored

• who owns incidents

Salesforce’s Trust Layer framing is a useful reference model for the categories of controls you should demand.

Next step

If you’re evaluating how to operationalize agentic automation in Service Cloud—starting with case understanding, then moving to triage/routing and controlled actions—ConvoPro is designed specifically for that Salesforce-native path.

• Learn how the platform is packaged: ConvoPro Automate + ConvoPro Studio → /product

Use the evaluation framework before vendor demos → /blogs/salesforce-native-ai-automation-tools-2025-buyers-checklist

If you want a walkthrough tied to your queue + workflows → /connect

FAQ

Is Einstein Copilot still a thing?

Salesforce has referred to Einstein Copilot as Agentforce Assistant and notes it has been upgraded into Agentforce. People still use “Einstein Copilot” as shorthand for the assistant/copilot experience embedded in Salesforce.

What is the main difference between a copilot and an agent in Salesforce?

A copilot/assistant is typically user-driven (responds when asked). An agent is designed to combine data + reasoning + actions to complete tasks, often across workflows and systems.

Can Agentforce update Salesforce records?

Salesforce documents standard agent actions that include capabilities like updating records, alongside actions like querying records and summarizing records. Whether you should allow updates depends on your governance and confirmation requirements.

How do I prevent hallucinations in Salesforce AI?

Treat hallucination prevention as a system design problem: require grounding to CRM/knowledge sources, limit what the AI is allowed to infer, and enforce “no evidence → say so.” Salesforce’s Trust Layer materials emphasize grounding and related protections.

Do I need the Einstein Trust Layer if I’m “just doing summaries”?

You still need governance for data exposure and auditability. Salesforce frames the Einstein Trust Layer as protections around data safety, grounding, masking, and audit trail behaviors when interfacing with LLMs.

Should we deploy Agentforce before the assistant experience?

Most teams should start with the assistant pattern to validate access controls, grounding quality, and user adoption—then expand into action-enabled agents once they can constrain and monitor actions safely.

Can Agentforce work with Salesforce Flow and Apex?

Salesforce’s developer materials describe building and invoking Agentforce capabilities with both low-code and pro-code tools, including Flow and Apex patterns.

What’s the fastest Service Cloud use case to start with?

Start where ROI is easy to measure and risk is low: case summaries/overviews, then triage and routing with human confirmation before you move to autonomous resolution.