Salesforce Agentforce vs Computer: two architectures for enterprise AI agents

TL;DR

• Salesforce Agentforce and Computer, by DevRev, both build enterprise AI agents that reason and act. The real difference isn't features – it's where the agent's context lives.
• Agentforce is CRM-anchored: agents reason over Salesforce data and Data Cloud, with the Atlas Reasoning Engine planning actions, and reach outside Salesforce through MuleSoft connectors and MCP partners.
• Computer is system-neutral: agents run on shared memory that spans your tools through 2-way sync, so context and write-back are native across systems rather than routed back to one cloud.
• If Salesforce is your center of gravity, Agentforce fits naturally. If your work – and your data – lives across many systems with no single hub, evaluate on where the memory and the actions actually sit.

What is Agentforce?

Agentforce is Salesforce's enterprise AI agent platform for building agentic AI – "the AI agent platform that delivers 24/7 autonomous support at enterprise scale," in Salesforce's words.

You compose agents in Agent Builder; they answer questions, resolve cases, manage orders, and escalate to humans, using the Atlas Reasoning Engine to break a request into steps and act on trusted business data across Sales Cloud, Service Cloud, and Data Cloud. As of September 2026 it reaches beyond Salesforce through MuleSoft connectors, custom Apex, and MCP partners.

That definition matters because the common assumption about Agentforce – that it only works inside Salesforce – misses its MuleSoft and MCP connectivity, and a fair comparison shouldn't rest on it.

Agentforce does connect outward. The honest question is architectural: where does the agent's context and action model *live* by default, and what does that mean for a business whose work spans many systems?

The architectural fork: CRM-anchored vs system-neutral

Both platforms build agents that reason and act. They start from different centers of gravity, and that starting point shapes everything downstream.

Agentforce is CRM-anchored. Its natural home is the Salesforce cloud – Customer 360, Data Cloud / Data 360, and the Atlas Reasoning Engine. Agents are strongest where the data already lives in Salesforce, and they reach other systems through MuleSoft API connectors and MCP server support from AgentExchange partners.

If Salesforce is your system of record and most of your work happens there, that gravity is an advantage.

Computer, by DevRev, is system-neutral. Its center of gravity is shared memory – a live, permission-aware knowledge graph built across the systems your teams already use, kept current by AirSync, a 2-way sync engine that reads and writes back to Salesforce, Zendesk, Jira, Slack, and dozens more.

The agent's context isn't anchored to one cloud; it spans them, and it can take a governed action in one system and write the result back to another as a single step.

Neither is "better" in the abstract. The right one depends on whether your enterprise has a single dominant hub or a genuinely distributed stack.

Six dimensions that separate the two

Read this down each column, not across. The question isn't which platform has more features – it's which architecture matches how your data and work are actually arranged.

The pricing row is there because it's often the deciding factor at scale, and both models are public. Model your real action volume against whichever structure you're weighing – per-conversation, per-action credits, and per-user licensing behave very differently as usage grows.

Which one fits your team

Three situations, three answers:

Salesforce is your center of gravity. If most of your customer data, workflows, and teams already live in Salesforce, Agentforce meets them where they are, and its CRM-native reasoning is a real advantage – prebuilt agent types like an AI SDR slot straight into existing Salesforce workflows.

The question to pressure-test is what happens at the edges – the tickets, product signals, and engineering context that live outside the CRM.

Your stack is genuinely distributed. If your work spans support, product, sales, and engineering across different systems – and no single one is the hub – evaluate on where the agent's memory and actions sit.

Computer is built for this: shared memory means the agent already holds context across systems, and 2-way sync means it can act in one and update another without routing everything through a single cloud.

You're standardizing for the next few years. If this is a platform decision, evaluate the architecture, not the demo – including how each platform handles the unglamorous production work of deploying, testing, and governing agents at scale. The full platform comparison across ten enterprise AI agent tools lays out the evaluation criteria; this page is the Agentforce-specific view of the same architectural question.

What this looks like in production

A feature list reads clean. Production tests the architecture.

Take a customer who contacts support about a billing problem, where resolving it means reading the account in the CRM, checking a related engineering issue, and updating a record in a third system. A CRM-anchored agent is strongest on the first step and routes the rest back through connectors.

A system-neutral agent already holds all three in shared memory and can act across them in one governed sequence – then write each result back to its source.

That's the architectural split in practice. It's also the kind of thing that only surfaces in a rigorous evaluation: the fintech BILL ran a competitive process across more than 15 AI providers before committing, precisely because the deciding factors – cross-system context and governed write-back – are the ones a demo hides and only a real proof of concept exposes.

Frequently asked questions

What is the difference between Salesforce Agentforce and Computer?

Both build enterprise AI agents that reason and act. The difference is architectural: Agentforce is CRM-anchored – its agents are strongest on Salesforce data and reach other systems through connectors – while Computer is system-neutral, running agents on shared memory that spans many systems with native 2-way write-back to each.

Is Agentforce only for Salesforce customers?

No. As of 2026, Agentforce reaches beyond Salesforce through MuleSoft API connectors and MCP server support from AgentExchange partners. Its natural strength is still where data lives in Salesforce; the evaluation question is how much of your work happens outside the CRM and how that context reaches the agent.

How much does Agentforce cost?

Salesforce publishes several models: $2 per conversation, Flex Credits at $500 per 100,000 (about $0.10 per action, since one action uses 20 credits), and Agentforce 1 Editions from $550 per user per month, with a lower-cost per-user add-on around $125. Enterprise Edition customers can also start with a free allotment of Flex Credits through Salesforce Foundations. Which model is cheapest depends on your action volume, so model it against your real usage before deciding.

What's the best Agentforce alternative for a multi-system stack?

If your data and work span many systems with no single hub, evaluate a system-neutral platform where the agent's memory and actions aren't anchored to one cloud. Computer, by DevRev, is built this way – shared memory across systems with governed, reversible write-back to each. Compare on architecture, not feature count. For a broader look beyond the agent layer, our guide to Salesforce alternatives covers the wider platform decision.

Does Computer replace Salesforce?

No. Computer connects to Salesforce through 2-way sync and works alongside it, reading and writing back so the CRM stays current. It's an intelligence-and-action layer across your systems, not a CRM replacement – which is why teams often run it alongside Salesforce rather than instead of it.

How to choose

Don't choose Agentforce or Computer from a feature grid – choose from where your data and work actually live. If Salesforce is the hub, Agentforce meets your teams there. If your stack is distributed and the value is in connecting it, evaluate on where the memory and the actions sit. To see how a system-neutral agent builds, governs, and acts across systems, explore Agent Studio – or bring a cross-system workflow to a walkthrough and watch it run end to end.