Fin vs. Agentforce: Salesforce Customer Service Agents

As AI agents mature, one question increasingly shapes customer service technology decisions: where should the intelligence live?

Some teams want an agent that arrives already trained, plugs into the helpdesk they run today, and starts resolving conversations within days. Others want AI woven directly into their CRM's data model, flows, permissions, and governance framework, tailored object by object and approved step by step.

For years the industry treated these as competing philosophies. They are not. They are two deployment models aimed at the same outcome, and as of September 2026 they sit inside the same portfolio.

Salesforce completed its acquisition of Fin on September 10, 2026, in a transaction valued at approximately $3.6 billion. Fin now operates as part of Salesforce AI Labs, continuing to serve its customer base of more than 30,000 businesses, continuing to advance its own proprietary models, and sitting alongside Agentforce's deeply customizable enterprise platform.

The #1 customer agent meets the #1 CRM. The future of customer service is autonomous, intelligent, and built on trust. Fin brings proven agent technology and an extraordinary AI team to Salesforce, making Agentforce even more powerful for customer service. Together, we're giving companies of every size the ability to deploy trusted AI agents from day one—accelerating time to value and delivering measurable outcomes at scale.

Marc Benioff, Chair and CEO, Salesforce

"I don't think there's a technology quite like Fin in the customer space. The big strategic benefit of our decision to join Salesforce is magnitudes more businesses and their customers that we can bring it to."

Eoghan McCabe, CEO and Co-Founder, Fin

This guide goes deep on both products: the architecture of each, a full feature inventory, what verified reviewers on G2 and Gartner Peer Insights actually say, how the pricing models compare, and how to decide which is the right starting point for your team.

TL;DR:

Fin is the fastest path to measurable autonomous resolution.

• It arrives pre-trained, runs on top of whatever helpdesk you already have, and is owned day to day by the support team rather than the platform team.
• It holds a 4.5 out of 5 rating across 3,911 verified G2 reviews, making it the most-reviewed dedicated AI customer support agent on the market.
• It resolves 76% of conversations on average, and it charges $0.99 per resolved outcome.

Agentforce is the deepest path to governed enterprise automation.

• It reasons natively over Salesforce objects, Flows, permissions, and audit trails, and extends far beyond support into sales, marketing, commerce, and employee-facing workflows.
• G2 named it the #1 Agentic AI Product in its 2026 Best Software Awards.

- If your priority is resolving customer conversations at high volume, fast, with cost tied to outcomes, start with Fin.

- If your priority is orchestrating deeply customized, tightly governed processes inside Salesforce, start with Agentforce.

Many organizations will run both, and that is now a supported architecture rather than a compromise.

Part I: Fin in Depth

Fin is a purpose-built AI customer service agent that autonomously resolves complex issues end to end. It does not simply answer questions. It takes action: processing refunds, updating accounts, changing subscriptions, handling transaction disputes, and running multi-step technical troubleshooting.

Fin runs inside Intercom's helpdesk or alongside an existing one, including Salesforce Service Cloud, HubSpot, and Freshdesk, with no migration required.

The Fin AI Engine

The patented Fin AI Engine is the architecture that separates Fin from agents that wrap a thin prompt layer around a general-purpose model. Most AI agents force a tradeoff between resolution rate and hallucination risk. The Fin AI Engine is designed to break that tradeoff by handling each stage of resolution with a dedicated, purpose-trained component.

Phase 1: Refine the query. Before anything reaches a language model, the incoming message is optimized for comprehension. The engine checks for safety and relevance, improves query clarity, checks whether a deterministic workflow should handle the request instead, and checks for a configured custom answer.

Phase 2: Retrieve relevant content. The proprietary Fin Retrieval model interprets the intent behind the question, then searches every enabled knowledge source: help center articles, uploaded documents, resolved conversation history, structured snippets, and connected third-party systems. Retrieval matches on semantics and meaning rather than keyword overlap.

Phase 3: Rerank for precision. The proprietary Fin Reranker model scores each retrieved candidate for relevance, accuracy, and contextual usefulness. It evaluates resolution fit, downranks outdated or low-confidence sources, and selects the final content set for generation.

Phase 4: Generate the response. Fin Apex 1.0 produces the answer. It applies your configured policies, grounds every output in your knowledge base rather than general training data, and decides honestly when a question needs a human instead.

Phase 5: Validate accuracy. Before the customer sees anything, the engine runs final checks against response accuracy and safety standards.

Always on: engine optimization. Integrated tooling continuously calibrates answer generation, efficiency, precision, and coverage, with AI analytics, reporting, and recommendations surfaced to the support team.

Always on: trust and security. Fin implements protections against the full range of LLM threats, including those identified in the OWASP LLM Top 10, alongside regional hosting options, international compliance standards, and contractual restrictions on third-party model providers.

The Fin Model Suite

Rather than routing everything through one frontier model, Fin runs a suite of models, each purpose-trained for a discrete stage of customer service resolution. This is the technical heart of Fin's performance advantage.

Fin Apex 1.0. The flagship generative model and the component that writes the final answer for every conversation. It is grounded in your knowledge base rather than general data, answers directly or escalates honestly, follows configured guidelines consistently, is trained to minimize hallucination at the source, and is post-trained on Fin's own production data. In production it delivers a 2.8% higher resolution rate, responses 0.6 seconds faster to first token, and 65% fewer hallucinations than Sonnet 4.6.

Fin Apex Flash. A latency-optimized version of Apex for time-sensitive interactions, with the same grounding, policy-following, and escalation behavior.

Fin Retrieval. Scans all available knowledge sources, understands intent, matches on semantics, and selects the top candidates.

Fin Reranker. Scores retrieved content for relevance and resolution fit, downranks stale sources, and outputs the final selection.

Fin Issue Summarizer. A fine-tuned 14B model that detects whether a conversation contains an addressable issue and extracts a clear, actionable summary from multi-turn exchanges. It handles greetings, feedback, and noise gracefully, at a 12.5% cost reduction versus frontier LLMs.

Fin Feedback Parser. A multi-task ModernBERT architecture that classifies feedback sentiment, detects follow-up questions, and identifies conversation endings, matching LLM accuracy at a fraction of the cost.

Fin Language Detector. Built on XLM RoBERTa, it identifies the customer's language across 45 supported languages, handling typos, very short messages, and script mismatches such as Romanized Hindi, and enabling intelligent fallback logic.

Fin Escalation Router. A multi-task ModernBERT model that decides whether to continue, offer escalation, or escalate outright. It cites the specific matching business guideline, provides reasoning for the decision, runs 0.5 seconds faster than LLM-based routing, and does so with over 98% accuracy.

The suite is built by a 50-person AI team led by Chief AI Officer Fergal Reid, drawing on more than a decade of customer service software experience.

Channels

Fin meets customers wherever they already are, with consistent behavior and configuration across every surface:

• Live chat and in-product messenger
• Email
• Phone and voice
• SMS
• WhatsApp
• Slack
• Social channels
• Tickets and cases in your existing helpdesk

Actions, Procedures, and Workflows

Fin combines generative reasoning with deterministic rules, which is what allows it to be trusted with real transactions rather than just answers.

• Procedures let you give Fin detailed, step-by-step instructions in plain language, which it follows reliably and repeatedly.
• Actions and data connectors let Fin read from and write to external systems through REST APIs, so it can look up an order, issue a refund, change a plan, or update a record.
• Deterministic workflows handle cases where you want guaranteed behavior rather than model judgment, with the engine routing to them automatically at the query refinement stage.
• Custom answers let you hard-pin exact responses for sensitive or high-stakes questions.
• Tone and length controls keep Fin aligned with your brand voice.
• Escalation guidelines define exactly when Fin should hand off, and to whom, with full conversation context preserved.

The Fin Flywheel: Train, Test, Deploy, Analyze

One of Fin's defining design decisions is that the support team owns the agent, not the engineering backlog. The entire lifecycle lives in a single no-code workspace.

• Train. Point Fin at your content sources, including web pages with scheduled refetches, so documentation maintenance doubles as agent maintenance.
• Test. Evaluate changes safely before they reach customers. Fin's Evals and Releases capability, announced in August 2026, lets teams assess performance before, during, and after go-live.
• Deploy. Roll out with usage caps and staged releases.
• Analyze. Pre-built and fully custom reporting, resizable dashboard layouts, AI-surfaced recommendations, and conversation-level visibility into what Fin resolved and why.

Helpdesk and Ecosystem Integrations

• Intercom for teams that want the full Customer Service Suite
• Salesforce Service Cloud, natively
• HubSpot, natively
• Freshdesk, natively
• Other helpdesks via the Fin API platform
• Business systems through out-of-the-box and custom data connectors

Trust, Security, and Reliability

• 99.97% uptime across millions of production conversations
• SOC 2, ISO 42001, and HIPAA compliance
• Regional hosting in the EU and Australia
• Zero data retention agreements with third-party model providers, who are contractually barred from training on your customer data
• Automatic exclusion from model training for customers with a BAA, customers on EU or AU regional hosting, and any customer previously granted an opt-out
• Protections mapped to the OWASP LLM Top 10

Documented Performance

• 76% average resolution rate across all customers, improving 1% each month
• 85% or higher resolution rates at many individual companies
• 4.5 out of 5 on G2 across 3,911 verified reviews, the most-reviewed AI customer support agent in its category
• 45+ languages supported
• 12,000+ customers running Fin, including Anthropic, Kalshi, and Vanta, within a total base of more than 30,000 businesses
• Fin Million Dollar Guarantee backing the performance claim commercially
• Strong results in independent head-to-head evaluations against other dedicated AI support agents, including Forethought and Decagon

Part II: Agentforce in Depth

Agentforce is Salesforce's enterprise agentic AI platform for building, deploying, managing, and governing AI agents that act autonomously for customers, employees, and partners across digital and voice channels.

Where Fin is a finished agent you configure, Agentforce is a platform you build on. That distinction explains most of its strengths.

The Atlas Reasoning Engine

Atlas is the reasoning core of Agentforce. It decomposes an incoming prompt into smaller tasks, evaluates progress at each step, and proposes and revises a plan until the complete answer or action is achieved. Agentforce pairs this with a hybrid reasoning architecture that combines deterministic workflow execution with LLM reasoning, so predictable steps stay predictable while natural language nuance is still handled.

Agent Builder

Agent Builder is the low-code environment where Agentforce agents are defined. Teams create a job to be done, define subagents, write natural language instructions inside each subagent, and assemble a library of actions the agent can choose from. Builders can observe the agent's plan of action and test responses directly in the tool. For teams that need to go further, the same agent can be extended with pro-code.

Grounding and Context

• Salesforce CRM data as the native grounding source, with no integration work required
• Data 360 and Data Cloud for unified real-time customer profiles, external data, analytics, and AI governance
• Intelligent Context processing that extracts and structures information from unstructured and multimodal sources so agents can act on documents, not just records
• Prompt Builder for grounded, reusable prompt templates

Actions and Integration

• Flows for automation across any connected system
• MuleSoft API connectors for reaching systems outside Salesforce
• Apex and JavaScript for custom business logic
• Metadata mapping so agents inherit the semantics of your existing objects
• Cross-cloud actions spanning Service, Sales, Marketing, and Commerce

The Out-of-the-Box Agent Library

Agentforce ships with pre-built agents that can be customized rather than built from zero:

• Service Agent for customer support across a wide range of issues without preprogrammed scenarios
• Sales Development Representative engaging prospects 24/7, handling objections, and booking meetings
• Sales Coach running personalized role-play sessions grounded in real deal data
• Merchandiser assisting ecommerce teams with site setup, promotions, and product content
• Buyer Agent supporting B2B purchasing, orders, and tracking
• Personal Shopper acting as a concierge on ecommerce sites and messaging apps
• Campaign Optimizer automating the marketing campaign lifecycle
• Agentforce for employees, formerly Einstein Copilot, supporting internal teams in the flow of work

Lifecycle Management and Supervision

• Batch testing of agent behavior at scale
• Performance monitoring and observability
• Tools to refine agent behavior over time
• Human supervision and handoff controls
• Full agent lifecycle coverage from creation through scaling

Trust, Governance, and Compliance

This is Agentforce's strongest suit, and it is a genuine differentiator for regulated enterprises.

• Configurable guardrails to reduce hallucinations, bias, and off-topic responses
• Grounding, access controls, auditability, and privacy safeguards inherited from the Salesforce platform
• The same permissioning model as the rest of your Salesforce estate, so an agent cannot see what a user could not
• Enterprise-grade reliability and compliance built on the broader Salesforce platform
• Complete audit trails for every agent action, inside the system of record

Scope Beyond Support

Agentforce is not a customer service product. It is an automation layer for the whole enterprise, covering service, sales, IT, operations, HR, marketing, commerce, and industry-specific workflows. For organizations whose ambition is an agentic enterprise rather than an automated support queue, that breadth matters.

Salesforce itself has processed more than one million support requests with Agentforce, which is a meaningful reference deployment at genuine enterprise scale.

Recognition

• #1 Agentic AI Product in G2's 2026 Best Software Awards, determined by verified customer reviews and market presence data rather than analyst opinion or paid placement
• 4.3 out of 5 on G2 across 1,226 verified Agentforce reviews in the AI Agent Builders category
• Agentforce Service, formerly Service Cloud, carries 7,358 G2 reviews and 417 Gartner Peer Insights reviews
• Agentforce Sales, formerly Sales Cloud, holds 4.4 out of 5 across 25,585 G2 reviews
• G2 Leader placements across Enterprise, Mid-Market, and Small Business segments, plus Momentum Leader and Best Relationship awards, and regional leadership in EMEA, Europe, APAC, and India

Part III: Feature-by-Feature Comparison

Part IV: What Reviewers Actually Say

Fin on G2: 4.5 out of 5 - 3,911 reviews

Fin sits in the AI Customer Support Agents category and is the most-reviewed product in it. G2 aggregates review feedback into themes, with the number of reviews behind each one.

What users like most:

That distribution is worth pausing on. The top two themes, resolution power and ease of use, are the two things buyers most often have to trade off against each other in this category. Fin's review base reports both at high volume.

Pierre G., a Customer Support Manager at a mid-market software company, rated Fin 5.0 and highlighted deployment flexibility and analytics depth:

"We can put the Fin messenger on any surface we own, and we have a ton of analytics, plus the ability to build our own custom reports."

He also noted that the native workflow builder is an area he would like to see developed further, and that he currently supplements it with external automation tooling.

Karan S., an events platform administrator, also rated Fin 5.0 after six years of use:

"I've been using Intercom since 2020, and it's the best chatbot provider in the market. It really redefines what a chatbot can do."

His team uses Fin for AI registrant matchmaking and attendee lookup through REST API and custom data connectors, and reports that Fin "has brought our cases down significantly."

Muhammed A., a technical project manager, rated Fin 4.5 and described the before and after plainly: his team previously

"spent significant time answering repetitive customer questions and routing routine requests," and Fin "automated a large portion of these interactions by providing accurate, context-aware responses using our knowledge base while escalating more complex cases when needed."

What reviewers want improved. In the interest of giving you a real picture rather than a curated one, the most-cited criticisms are missing features around procedure visibility and conversation simulation (135 reviews), AI limitations with unusual formats (117 reviews), a learning curve during setup and ongoing tuning (102 reviews), and pricing concerns at higher volumes (92 reviews). Several of these are being addressed directly: the Evals and Releases launch in August 2026 targets the simulation and testing gap specifically.

Fin on Gartner Peer Insights

Fin AI Agent carries 23 in-depth reviews verified by Gartner. The volume is lower than G2's because Gartner's verification process is more demanding and the product listing is newer, so treat it as a supplementary signal rather than a primary one.

Agentforce on G2: 4.3 out of 5 - 1,237 reviews

Agentforce is reviewed in the AI Agent Builders category, which is the right comparison set for a platform rather than a finished agent.

Its headline achievement is significant: G2 named Agentforce the #1 Agentic AI Product in the 2026 Best Software Awards, drawn from more than 500,000 reviews and market presence data, with no analyst input or paid placement. G2 cited performance, ease of use, and measurable business impact.

Representative verified reviews:

Ashish B., an operations associate, rated Agentforce 5.0 and pinpointed the native advantage: it automates

"repetitive tasks while staying fully within the Salesforce environment," helping teams "streamline day-to-day workflows without forcing users to constantly switch between different tools."

His improvement request is the one that recurs most across Agentforce reviews:

"Building effective agents can take some customization and a solid understanding of the workflow."

A reviewer named Tarun captured the Data Cloud pairing well: "The combined capabilities of Agentforce and Data Cloud have significantly improved the quality and efficiency of customer support. Having a unified real-time customer profile embedded directly in the agent workspace allows for more informed and context-rich interactions."

Clark, another G2 reviewer, described the build experience:

Agentforce "lets me build smart, responsive agents in minutes, automate complex tasks, and deliver consistent, high-quality support."

Krishan focused on the voice capability:

"The voice agent delivers fast, accurate, and natural customer interactions, reducing handling time and improving overall service quality."

The consistent theme in critical reviews is setup complexity and a steep learning curve, particularly for teams new to Salesforce configuration. This is not really a defect so much as the cost of the platform's depth. A tool that lets you model any workflow against any custom object requires you to decide how. That is the trade Agentforce makes deliberately.

Wider Recognition

Agentforce Service, formerly Service Cloud, carries 7,358 G2 reviews and 417 Gartner Peer Insights reviews. Agentforce Sales, formerly Sales Cloud, holds 4.4 out of 5 across 25,585 G2 reviews. Salesforce products collectively account for more than 97,000 G2 reviews, which is a scale of validated enterprise adoption almost nothing else in software can match.

Reading the Review Data

Three patterns are worth drawing out.

First, Fin's rating is higher and its review base in the agent category is larger. 4.5 across 3,911 reviews versus 4.3 across 1,226. For a buyer evaluating a customer service agent specifically, Fin has both the stronger score and the deeper evidence base.

Second, the criticism profiles are near-mirror images. Agentforce reviewers most often ask for simpler setup. Fin reviewers most often ask for deeper workflow customization. That is exactly what you would predict from a finished agent versus a build platform, and it is the clearest signal in the data about which tool suits which situation.

Third, both products are category leaders by independent measure. Fin is the highest-rated and most-reviewed dedicated AI support agent. Agentforce is G2's #1 Agentic AI Product for 2026. Buyers are not choosing between a good option and a bad one.

Part IV: Pricing

Fin Pricing

Fin charges for outcomes, not activity. You are billed when Fin successfully delivers value.

Outcomes include resolutions, Procedure handoffs, lead qualifications, and disqualifications. You are not charged for abandoned conversations, failed attempts, customers who ask for a human, or escalations where Fin did not reach an outcome. Workflow complexity does not change the price. A conversation that takes twelve steps and four API calls costs the same as one that takes two.

Option 1: Fin with any helpdesk

Works with Salesforce Service Cloud, HubSpot, Freshdesk, and others.

• $0.99 USD per outcome
• Minimum commitments apply
• Free 14-day trial

Includes setup in under an hour, coverage across tickets, cases, email, live chat, WhatsApp, SMS, voice and more, customizable tone and answer length, actions against external systems, agent handoff directly into your preferred inbox, and eligibility for the Fin Million Dollar Guarantee.

Option 2: Fin with Intercom's helpdesk

• $0.99 USD per outcome, plus
• $29 per helpdesk seat per month

Includes every Fin AI Agent capability plus Intercom's full Customer Service Suite: configurable inbox and ticketing, email, chat, phone and SMS, workflow automations, pre-built reporting, public Help Center and Knowledge Hub, and the Proactive Outbound Suite.

Add-on: Copilot

• $35 USD per user per month

An AI assistant inside the agent inbox providing instant advice and answers, accelerated onboarding, trusted answers drawn from your content sources, and AI translation for global teams.

Agentforce Pricing

Agentforce offers three models, which suit genuinely different usage profiles. Every Salesforce customer can also start with Agentforce at no cost through Salesforce Foundations, which is a real advantage for teams that want to prototype before committing.

Flex Credits. $500 per 100,000 credits. Most standard actions consume roughly 20 credits, or about $0.10 per action. Available for customer-facing agents, employee-facing agents, and Agentforce Voice. This model rewards efficient workflow design, because cost scales with the number of actions a journey actually triggers. Tightly modeled workflows are inexpensive; sprawling multi-system journeys cost more.

Conversation pricing. $2 per conversation for customer-facing agents, with pre-purchased volume commitments. You pay per conversation regardless of how many actions run behind it, which simplifies forecasting for workloads with deep per-conversation automation.

Per-user licensing. Available for deployments where seat-based economics fit better than consumption.

Modeling total cost. As with any platform deployment, the complete picture depends on several variables worth working through up front: how many actions a typical workflow generates, whether Data Cloud forms part of your grounding and analytics foundation, which third-party channels you deploy, whether you need separately billed sandbox environments, and how much administration the configuration requires on an ongoing basis. Organizations already standardized on Salesforce frequently have much of that foundation in place already, which improves the economics considerably.

How the Two Models Differ in Practice

Neither approach is universally cheaper. They make cost predictable along different axes.

Action-based pricing tracks the work performed, which is efficient when workflows are well understood and tightly designed, and less predictable when conversation complexity varies widely. Outcome-based pricing tracks the value delivered, which is predictable regardless of complexity, and means an unusually difficult conversation carries no cost penalty.

For support organizations with high volume and variable complexity, Fin's flat $0.99 per outcome is usually the easier number to forecast and defend in a business case. For organizations running tightly scoped, deeply integrated enterprise journeys, Flex Credits can be very efficient.

Part VI: Which Should You Choose?

Start with Fin if:

You want measurable resolution quickly. Fin publishes a clear benchmark, 76% average resolution with documented monthly improvement, and backs it with the Million Dollar Guarantee. Initial setup takes under an hour and full deployment is measured in days.

You are not on Salesforce, or not only on Salesforce. Fin runs natively with Intercom, Salesforce, HubSpot, and Freshdesk, and connects to others via API. No migration, no data model project.

Your support team should own the agent. Training, testing, deployment caps, and analytics all live in one no-code workspace designed for CX practitioners rather than platform engineers. No engineering queue between a policy change and production.

You need cost predictability. $0.99 per resolved outcome, independent of how many steps or API calls a conversation required.

You need broad channel and language coverage out of the box. 45+ languages with a dedicated detection model, across chat, email, SMS, WhatsApp, voice, Slack, social, and tickets.

Hallucination risk is a board-level concern. The validation phase, the grounding architecture, and Apex 1.0's model-level training combine to produce 65% fewer hallucinations than Sonnet 4.6, with escalation decisions made by a dedicated router at over 98% accuracy.

You want the most-validated dedicated support agent available. 4.5 out of 5 across 3,911 verified G2 reviews.

Start with Agentforce if:

Your resolution logic is inseparable from Salesforce. If resolving a case means orchestrating custom objects, Flows, and approval chains built over years, Agentforce reasons over them natively rather than through an integration layer.

Governance is a hard requirement, not a preference. Agentforce inherits Salesforce permissioning, auditability, and compliance wholesale. In regulated environments where every AI action must sit inside an existing control framework, that inheritance is often the deciding factor.

You need automation beyond support. Service, sales, marketing, commerce, HR, IT, and operations in one agentic layer, with cross-cloud workflows that a support-focused agent is not designed to cover.

You need employee-facing and operational agents too. The out-of-the-box library spans SDR, Sales Coach, Merchandiser, Buyer Agent, Personal Shopper, Campaign Optimizer, and internal copilots.

You have platform capacity and want maximum control. Teams with Salesforce architecture expertise can tune agent behavior to a specificity that a pre-trained agent intentionally abstracts away.

You want to prototype before you pay. Salesforce Foundations lets any Salesforce customer start with Agentforce at no cost.

Run both if:

You want fast frontline coverage and deep enterprise orchestration, which describes most large support organizations.

A common pattern in the combined portfolio:

1. Fin handles the front line. It resolves the high volume of inbound questions and routine actions across every channel, quickly, at a predictable cost per outcome, and without a platform project to get started.
2. Agentforce handles the depth. Cases requiring orchestration across custom Salesforce objects, multi-cloud processes, or tightly governed approval chains route into Agentforce Flows.
3. Both write back to the same CRM record. Customer context, case history, and outcomes stay unified in Salesforce.

The result is coverage at both ends of the spectrum: rapid deployment on systems you already run, and deeply tailored enterprise-scale transformation. That combination is precisely what the acquisition was built to deliver.

Frequently Asked Questions

1. What is the difference between Fin and Agentforce?

Fin is a pre-trained AI customer service agent that resolves customer issues end to end across any helpdesk or channel, optimized for fast deployment and measurable resolution. Agentforce is Salesforce's agentic AI platform for building and governing agents natively within Salesforce using CRM objects, Flows, and metadata, optimized for depth of customization and enterprise governance. Both are now part of Salesforce's customer service AI portfolio and address different ends of the same market.

2. Now that Salesforce has acquired Fin, is Fin going away?

No. Fin operates as part of Salesforce AI Labs, continuing to serve existing customers, advance its agent capabilities, and build its own models. Eoghan McCabe remains CEO and Des Traynor continues to lead R&D. The acquisition closed on September 10, 2026.

3. Is Fin only available if you use Intercom?

No. Fin runs inside Intercom's helpdesk or integrates with existing platforms including Salesforce Service Cloud, HubSpot, and Freshdesk, with other helpdesks reachable via the API platform. In those environments Fin deploys without a helpdesk migration and works directly inside existing tools and workflows.

4. How do Fin and Agentforce compare on independent review sites?

Fin holds 4.5 out of 5 across 3,911 verified G2 reviews in the AI Customer Support Agents category, making it the most-reviewed product in that category. Agentforce holds 4.3 out of 5 across 1,226 verified G2 reviews in the AI Agent Builders category and was named the #1 Agentic AI Product in G2's 2026 Best Software Awards. Fin reviewers most often praise resolution quality and ease of use, and most often request deeper workflow customization. Agentforce reviewers most often praise native Salesforce integration and breadth, and most often request simpler setup.

5. How do Fin and Agentforce perform?

Fin publishes and operates against clear benchmarks: 76% average resolution rate with documented monthly improvement, over 85% at many individual companies, 99.97% uptime, and 65% fewer hallucinations than Sonnet 4.6 from its Apex 1.0 model. Agentforce outcomes depend more heavily on configuration, data quality, and how workflows are modeled, which is the natural trade-off for a platform built for deep customization. Salesforce has itself processed more than a million support requests on Agentforce. Teams evaluating either should benchmark against their own conversation mix.

6. How does pricing differ?

Fin charges $0.99 per resolved outcome regardless of complexity or number of actions. Agentforce charges roughly $0.10 per action via Flex Credits, $2 per conversation, or per user, with a free starting point through Salesforce Foundations. Outcome pricing is more predictable when conversation complexity varies; action pricing can be more efficient when workflows are tightly designed and volumes are well understood.

7. Can we run both?

Yes, and many organizations will. Fin on the front line across all channels, with complex and deeply governed cases orchestrated through Agentforce Flows, and both writing back to the same Salesforce records.

8. Which is faster to deploy?

Fin, substantially. Initial setup takes under an hour and full deployment is typically measured in days, because Fin arrives pre-trained and reads your existing help content. Agentforce deployment is a configuration project whose length depends on how much you are modeling.

9. Does Fin work with Salesforce Service Cloud specifically?

Yes, natively. Fin deploys into Salesforce environments without requiring a helpdesk migration, works within existing cases and workflows, and hands off to human agents in the inbox they already use.

10. What languages does Fin support?

45 and counting, backed by a dedicated language detection model built on XLM RoBERTa that handles typos, very short messages, and script mismatches such as Romanized Hindi, with intelligent fallback logic.