Your support team spends hours each day answering the same questions. Your sales reps are buried in follow-ups and data entry instead of closing deals. Your marketing campaigns go out to broad audiences because there is not enough time to personalize at scale. And somewhere in the middle of all this, your customers are waiting longer than they should.
These inefficiencies add up to lost revenue, higher operating costs, and customers who move on.
The problem is not that your teams are not working hard enough. It is that a large portion of what they do every day is repetitive, manual, and time-consuming work that does not require a person to do it.
Salesforce Agentforce is built to fix this. As Salesforce’s enterprise AI agents platform, it uses Salesforce agentic AI to deploy agents across customer service, sales, marketing, and operations. AI agent automation Salesforce makes it possible to handle high-volume, routine work so your people can focus on what actually moves the business forward.
Since its launch in October 2024, Agentforce has gone through five major releases and crossed 18,500 customer deals. Companies using it are reporting faster response times, lower cost per interaction, and measurable increases in pipeline conversion, without adding headcount.
This guide covers how the platform works, what is new in Agentforce 360, real results from companies already using it, and which use cases are worth prioritizing first.
What is Salesforce Agentforce?
Agentforce is Salesforce’s AI agent platform. It lets businesses build, deploy, and manage AI agents in Salesforce that can handle real work: answering customer questions, qualifying leads, scheduling meetings, and processing returns, without a person needing to start or oversee each interaction.
Salesforce describes it as always-on digital labor that works for your customers, employees, and business processes around the clock. Agents can be deployed across customer service, sales, marketing, commerce, and internal operations.
Before we dive into Salesforce Agentforce, let’s understand what AI agents are.
A Salesforce AI agent is a software system that can understand a request in plain language, reason about what needs to happen, take action, and assess the result, all on its own. It does not follow a rigid script or wait to be told each next step. It figures out the right course of action based on the context it has access to, then carries it out.
In Salesforce, AI agents have access to your CRM data, your business processes, and your connected systems. That is what allows them to do things like look up a customer’s order history, determine whether a refund qualifies under your return policy, process the return, and send a confirmation, all within a single interaction, without a human touching any step.
Two types of agents are available.
- Autonomous agents: These agents complete tasks from start to finish on their own. You define the goal, connect the agent to the right data and tools, and it handles the rest.
- Assistive agents: These agents work alongside your team. They surface relevant information, suggest next steps, and draft responses, but a human makes the final call.
Both types can be deployed across customer service, sales, marketing, commerce, and internal operations.
How Agentforce Has Changed Since Launch
The Agentforce 360 platform is the version available today. According to Salesforce, it brings together humans, applications, AI agents, and data so that any company can safely deploy agents that work for their customers, suppliers, and employees around the clock.
Agentforce 1.0 (October 2024): Announced at Dreamforce 2024. The first version embedded autonomous AI agents directly into Salesforce CRM workflows. Agents could respond to customer inquiries, update records, trigger workflows, and assist sales and support teams using natural language. Data Cloud provided the contextual data. Primary use cases were customer service, lead qualification, and case resolution.
Agentforce 2 (December 2024): The focus shifted to making the platform enterprise-ready. Agent orchestration improved, integration with Salesforce Flows deepened, and support expanded across sales, service, and marketing functions. The key shift was from single-task assistants to agents capable of handling multi-step workflows.
Agentforce 2dx (March 2025): A developer-focused release. Salesforce introduced APIs and tools that allowed teams to extend and customize agents beyond the packaged defaults. Governance and observability capabilities were strengthened, and agents gained the ability to connect with external tools and workflows. The platform moved from packaged AI agents to customizable enterprise agents.
Agentforce 3 (June 2025): Agents moved deeper into core business processes, including prospecting, case resolution, and operations workflows. Multi-agent collaboration was introduced, allowing agents to hand off work to each other across departments. Salesforce also published the Agent-to-Agent (A2A) protocol, enabling Agentforce agents to work with agents on other platforms. Monitoring and control tools were improved across the board.
Agentforce 360 (October 2025): Announced at Dreamforce 2025, this was the biggest shift to date. Salesforce redefined the platform as a unified enterprise AI operating system connecting humans, agents, applications, and data. New capabilities included a hybrid reasoning engine, Agentforce Voice for phone-based interactions, Intelligent Context for grounding agents in live enterprise data, multi-modal support for documents and tables, and a redesigned low-code Agent Builder. Slack was added as a collaboration layer for workplace AI interactions.
Agentforce 360 Updates (January 2026) Following the Dreamforce launch, Salesforce rolled out general availability improvements, including faster agent deployment, a more capable Agentforce Builder, and stronger reasoning controls for enterprise workflows. The platform has moved from experimental AI features to core business infrastructure.
Agentforce Help Agent and pay-per-resolution (June 2026): Salesforce launched a prepackaged Help Agent with guided setup and pay-per-resolution pricing, generally available from July 2026. Salesforce reports that on help.salesforce.com, Agentforce has handled 4.3 million inquiries and resolved 70% of them.
Headless 360 expansion (August 2026): Salesforce extended Headless 360 so external agents can take action in Salesforce through MCP servers and APIs, which set up the wider agent access announced a month later.
Job-ready agents and long-horizon runtime (September 2026): Agentforce 360 remains the current platform generation, and Salesforce has not given this release a new version number. It added a portfolio of job-ready agents (Casey, Paige, Carter, Hunter, Marshall, Piper, and Fin), a long-horizon runtime for goals that span days and weeks, Agent Optimizer, AI Skills in Coworker, Multi-Agent Orchestration at general availability, and Koa, Salesforce’s first CRM reasoning model for Agentforce.
Agentforce 360: What the Current Platform Looks Like
Agentforce 360 is the version available today. According to Salesforce, it brings together humans, applications, AI agents, and data so that any company can safely deploy agents that work for their customers, suppliers, and employees around the clock.
The platform is organized around four pillars.
Agentforce 360 Platform is the core infrastructure. This includes the Atlas Reasoning Engine, Agentforce Builder, Agent Script, the Trust Layer, and the Observability dashboard. Everything needed to build, run, and monitor agents’ lives here.
Data 360 is powered by Salesforce Data Cloud. This gives agents access to unified, real-time data from across the business, including CRM records, purchase history, support tickets, website activity, and third-party sources. Agents need accurate data to make good decisions, and this is where that data comes from.
Customer 360 Apps are the pre-built agent experiences for sales, service, marketing, commerce, and industry-specific clouds. These are deployed to customer-facing channels.
Agentforce Ecosystem covers third-party integrations, partner-built agents, and AgentExchange, which is Salesforce’s trusted marketplace for ready-to-use agent actions and templates. If you need agents that connect to systems outside of Salesforce, this is where you find and configure those connections.
How Salesforce Agentforce Works
Understanding how Agentforce works helps you decide where to deploy it first and what needs to be ready before you do.
Data, reasoning, and actions
Salesforce describes three things every agent needs to get work done: data, reasoning, and actions.
- Data gives the agent context. Agentforce agents draw on Salesforce CRM records, knowledge articles, and Data 360 (formerly Salesforce Data Cloud), which unifies data from inside and outside Salesforce, including sources accessed through Zero Copy without moving the data.
- Reasoning decides what to do. The Atlas Reasoning Engine breaks a request into smaller tasks, evaluates progress at each step, and proposes a plan until the answer or action is complete. Hybrid reasoning lets you fix certain steps so they always run the same way while leaving other steps flexible.
- Actions carry out the work. Actions are the specific things an agent is allowed to do, such as running a Flow, calling an Apex class or MuleSoft API, using a prompt template, or connecting to an external tool through MCP.
If any of the three is weak, the agent underperforms. Poor data produces wrong answers, loose reasoning instructions produce unpredictable behavior, and missing actions mean the agent can explain a problem but not fix it.
What happens in a single agent interaction
Step 1 – Request intake: A customer sends a message through web chat, WhatsApp, Messenger, email, SMS, or phone. An employee asks a question in Slack or Salesforce Lightning. A background trigger fires because a deal has gone quiet or a renewal is due. Agentforce handles all three.
Step 2 – Intent recognition: The Atlas Reasoning Engine reads the input, identifies the intent, and selects the right subagent (the defined job the agent should perform). A message saying “I never received my order” is recognized as a delivery issue, not a general inquiry, and routed to the order management subagent.
Step 3 – Data retrieval: The agent pulls live customer records, including order history, support tickets, and account status, from CRM and Data 360. Every response is grounded in real, current data rather than generic assumptions, and the Einstein Trust Layer masks sensitive fields before anything reaches a language model.
Step 4 – Action execution: The agent sends a reply, updates a record, processes a refund, books a meeting, or triggers a workflow. Complex situations may involve several actions in sequence, or a handoff to another specialized agent through Multi-Agent Orchestration.
Step 5 – Logging and optimization: Every action, response, and escalation is recorded in Agentforce Observability so you can track performance and find weak spots. Agent Optimizer analyzes these session traces and recommends specific changes.
Salesforce Agentforce ROI Calculator
Salesforce Agentforce vs Traditional Chatbots
A lot of businesses have already invested in chatbots for customer service or sales support. When they first look at Agentforce, the natural question is: how is this different from what we already have?
The answer is that they are different in almost every meaningful way. A traditional chatbot is built around scripts and decision trees. It can only go where you have already mapped a path. Agentforce agents reason through situations and take action. Here is how they compare across the areas that matter most.
| Aspects | Traditional Chatbots | Salesforce Agentforce |
| How it handles requests | Follows pre-written scripts and decision trees. Can only respond to what it was specifically programmed to handle. | Reads the request, understands the intent, and decides what action to take based on context and available data. |
| What happens with unexpected questions | Falls back to a generic error message or routes to a human because it cannot recognize the input. | Interprets the request even if the wording is new, and either handles it or routes to a human with full context included. |
| Data access | Works only with static data fed into it at setup. Does not pull live customer records during a conversation. | Pulls real-time data from Salesforce CRM, Data Cloud, and connected systems during every interaction. |
| What it can actually do | Answers questions and collects information. Cannot update records, process transactions, or trigger workflows. | Completes tasks end-to-end: processes returns, updates records, books meetings, triggers workflows, and more. |
| Personalization | Generic responses based on the channel or product category. Cannot tailor responses to the individual customer. | Every response is grounded in that specific customer's history, preferences, and current account status. |
| Learning over time | Does not improve without manual updates to the script. Performance depends entirely on how well it was programmed. | Logs every interaction through Agentforce Observability. Performance data is used to identify gaps and refine behavior. |
| How it handles complex situations | Escalates to a human with little or no context transferred. The customer often has to start over. | Passes to a human with the full conversation transcript and relevant CRM data already attached. |
| Setup and maintenance | Requires ongoing manual scripting for every new scenario. Adding new capabilities means writing new decision trees. | Built through Agentforce Builder using natural language and low-code tools. New topics and actions can be added without rewriting the whole system. |
| Cross-channel coverage | Typically limited to one or two channels, usually web chat or a messaging app. | Operates across chat, email, SMS, phone via Agentforce Voice, Slack, and Salesforce Lightning. |
| Compliance and auditability | Limited logging. Hard to audit what the bot said or why it responded in a particular way. | Full audit trail through Agentforce Observability. Every action, response, and escalation is logged and reviewable. |
| Multi-agent coordination | Not possible. Each bot is a standalone system. | Multiple agents can collaborate, hand off work to each other, and complete complex multi-step processes across departments. |
Types of Agentforce agents
Agentforce gives you four starting points. Choosing the right one has more effect on time to value than almost any other early decision.
Job-ready agents
Announced at Dreamforce 2026, job-ready agents come with job-specific subagents, prebuilt actions, and Salesforce data context already in place. Each can be renamed and adapted to how your business works.
| Agent | Job | Availability (per Salesforce, September 2026) |
| Casey | Help agent for customer service across voice, SMS, WhatsApp, and web chat, with prebuilt support for FAQs, returns, account management, and human escalation. | Generally available |
| Paige | IT and HR service agent for employee requests across Slack, portals, and existing tools. | Generally available |
| Carter | Shopper agent for product discovery, comparison, questions, and in-chat checkout. | Generally available |
| Hunter | Outbound sales agent that works a pipeline from research to outreach over weeks and months. | Pilot; GA planned November 2026 |
| Marshall | Supply chain agent that runs multi-step back-office processes with deterministic execution and an audit record. | Generally available |
| Piper | Inbound pipeline agent that qualifies and converts website and inbox leads. | Generally available |
| Fin | Customer agent for complex customer experience workflows across every channel. | Generally available |
Salesforce has published early results. Engine’s help agent fully resolves 50% of chat inquiries. Perk’s outbound sales agent builds 60% of its sales pipeline. Autism Queensland’s employee service agent resolves 70% of administrative requests. Hibbett’s shopper agent handles 90% of core shopper journeys and went live in six weeks.
Prebuilt agent templates in Agentforce Builder
If no job-ready agent fits, Agentforce Builder includes a library of templates to start from: Service Agent, Employee Agent, Campaign Agent, Sales Development Rep, Sales Coach, Guided Shopping (B2B and D2C), Guided Shopping (B2C), and Setup Agent. Builder also includes out-of-the-box subagents for common jobs such as order management, reservation management, and general FAQs. Each template can be extended with your own data, guardrails, and business logic.
Agentforce Coworker
Agentforce Coworker is an assistive agent that sits inside the Salesforce Lightning interface. It reasons across accounts, activity, and history, answers natural-language questions, and takes action within each user’s existing permissions. It also calls the specialized agents you have already deployed, so its usefulness grows as you add agents. Salesforce reports that 100,000 users activated Coworker within its first 35 days, and that Fulton Bank moved from zero to more than 20 Coworker use cases in production within weeks, supporting about 3,000 users.
Custom agents
For processes unique to your business, you can build a custom agent in Agentforce Builder from scratch, defining your own subagents, actions, instructions, and Agent Script logic. Custom agents take longer to design and test, but they suit regulated workflows, industry-specific processes, and multi-system orchestration that no template covers.
What is new in Agentforce after Dreamforce 2026
Salesforce summarized its Dreamforce 2026 Agentforce release as five new ways to put agents to work, alongside a new reasoning model. Availability dates below are Salesforce’s own, and Salesforce notes that unreleased features may change, so confirm status before planning a rollout around them.
Purpose-built agents
The job-ready agents described above are the first item. Their purpose is to move teams past a blank canvas: instead of assembling subagents, actions, and data connections from scratch, teams start from a proven pattern and spend their effort on adapting it.
Long-horizon runtime
Most agents complete a task within one conversation. The long-horizon runtime lets an agent pursue a goal across days or weeks. It keeps context between sessions (memory), keeps plans running and adjusts them as conditions change (durable execution), and adapts its behavior based on each user’s feedback. Built-in checkpoints keep people informed and in control, so autonomy does not become a black box. Hunter is the first agent built on it, and Salesforce says customers will be able to build their own long-horizon agents over time.
Agent Optimizer
Agent Optimizer (generally available October 2026) is a conversational agent inside Agentforce Builder that helps teams build and improve other agents. It can create a starting agent from your existing case and conversation data, test each change, handle the underlying Agent Script, explain an agent’s behavior in plain language, and analyze session traces to recommend specific fixes.
AI Skills in Coworker
AI Skills (pilot now, GA planned October 2026) let an employee teach Agentforce Coworker a task once and reuse it across the workforce. Salesforce says these skills can appear in Slack, Microsoft Teams, desktop, mobile, ChatGPT, and Claude, so a team does not rebuild the same capability for each tool.
Multi-Agent Orchestration
Multi-Agent Orchestration (generally available) sends work to the right agent, carries context between steps, and coordinates handoffs across systems and departments. The customer or employee sees one connected journey while each specialized agent handles the part it was built for. This lets teams build several small, focused agents instead of one oversized agent that is hard to test.
Koa, the CRM reasoning model for Agentforce
Koa is Salesforce’s first reasoning model built specifically for Agentforce CRM work, developed with NVIDIA by post-training Nemotron 3 Super on synthetic scenarios across more than 14 industries. No customer data was used in training, and Salesforce runs Koa inside its own trust boundary. On Salesforce’s CRM benchmark (tasks such as updating an opportunity, routing a case, or scheduling a follow-up), Koa matches or exceeds leading models with three times fewer errors. Koa is in pilot with select customers, with general availability expected in winter 2026 in U.S. regions. It sits alongside the frontier models already available in Agentforce, so teams can choose a model per use case on price and performance.
Key Salesforce Agentforce capabilities
The capabilities below form the technical foundation of Agentforce. Each plays a specific role in how agents are built, how they reason, how they act, and how they are monitored.
1. Atlas Reasoning Engine
Atlas Reasoning Engine is the core intelligence layer behind every Agentforce agent. It breaks an initial request into smaller tasks, evaluates progress at each step, and proposes a plan until the answer or action is complete. Accuracy improves when subagents have clear scopes, instructions, and rules.
Hybrid reasoning lets you fix structured steps while keeping flexible reasoning for open-ended situations. Atlas supports a choice of foundation models, including OpenAI, Anthropic Claude, and Google Gemini, and Salesforce’s own Koa model is in pilot.
Key features:
- Multi-step planning, so agents can break a goal into smaller actions and execute them in sequence
- Model choice per use case, based on price and performance
- Configurable for any industry use case without rebuilding from scratch
2. Agentforce Builder
Agentforce Builder is the low-code and pro-code workspace for creating, configuring, testing, and deploying agents. It is located in Agentforce Studio in the App Launcher. Building is text-first on the Canvas view, which makes agents easy to copy, reuse, and move across teams or environments. Agentforce Vibes, an AI coding partner, reads your org’s metadata to generate Lightning Web Components or Apex code that fits your org.
Key features:
- Build through chat with Agent Optimizer, on Canvas in natural language, or in the Agent Script view
- Prebuilt templates and subagents for common jobs
- Simulator with full reasoning traces and built-in batch testing
3. Agent Script
Agent Script is Salesforce’s open-source language for agent behavior. It combines AI reasoning with deterministic rules so defined steps run the same way every time, regardless of how a conversation unfolds. This matters most in regulated industries where interactions must be auditable, predictable, and consistent.
Key features:
- Conditions, loops, transitions, and variables for precise control
- Three ways to write it: assistant chat, Canvas blocks, or script view
- Supports auditability for compliance-sensitive workflows
4. Agentforce Voice
Agentforce Voice brings AI agents into phone-based customer interactions and is a common route for replacing traditional interactive voice response (IVR) menus. Voice agents handle inbound calls, resolve routine inquiries, and transfer complex calls to a human agent with the full conversation context attached. Every word is transcribed live in Salesforce, so human agents can see the conversation when they need to step in.
Key features:
- Handles inbound calls and resolves routine inquiries without human involvement
- Transfers calls to human agents with full conversation context included
- Integrates with Amazon Connect, Five9, Genesys, NiCE, and Vonage
- Connected to Salesforce CRM data for personalized, context-aware responses
5. Intelligent Context
Intelligent Context makes sure agents work from your actual business data rather than generic knowledge. It extracts, structures, and makes available information from unstructured sources, including PDFs, tables, images, and flowcharts, so agents give accurate, business-specific answers. Low-code configuration means teams can set up data pipelines in hours rather than days.
Key features:
- Low-code pipeline setup that can be completed in hours
- Grounds agent responses in your live business data
- Reduces the risk of generic or inaccurate answers
6. Prompt Builder
Prompt Builder is the tool for writing and managing prompt templates that agents can use as actions. Prompts can pull in live CRM data, including a customer’s name, order history, and account tier, so every interaction reflects the customer’s actual context. Prompts can be tested before going live, and everything runs within the Einstein Trust Layer.
Key features:
- Pulls live CRM data directly into prompts
- Test and review prompts before deploying to production
- Reusable prompt templates across multiple agents and use cases
7. Model Builder
Model Builder lets data science teams build and deploy custom predictive models inside Salesforce without separate infrastructure. Models connect to CRM and Data 360 data and feed predictions into agent workflows or standard Salesforce processes.
Key features:
- Supports churn scoring, fraud detection, demand forecasting, and propensity-to-buy models
- Feeds model outputs into agent workflows and Salesforce automation
- No separate data science infrastructure required
8. Agentforce guardrails
Agentforce guardrails combine your own safeguards with Salesforce-managed protections. They keep agents within their core instructions, block off-topic or inappropriate conversations, and reduce the risk of hallucinated, biased, or incorrect responses. Guardrails are on by default and configurable by administrators. (See the security section below for the Einstein Trust Layer.)
Key features:
- User-defined and Salesforce-managed protections working together
- Blocks off-topic and inappropriate requests
- Configurable per agent and subagent
9. Agentforce Observability
Agentforce Observability monitors, analyzes, and helps improve agent performance in near real time. It covers total sessions, deflection rate, escalation rate and reasons, response latency, unhandled requests, and customer satisfaction, with session-level data to debug individual interactions. Conversation logs are stored in Data 360 for quality review or compliance purposes.
Key features:
- Traces agent reasoning, task completion, and operational impact across apps and systems
- Session-level detail for debugging individual conversations
- Provides the audit trail regulated industries need to demonstrate human oversight
10. Agent Optimizer
Agent Optimizer (generally available October 2026) is a conversational agent built into Agentforce Builder that works across the agent lifecycle. It can create agents from existing case and conversation data, refine actions, test changes, explain agent behavior in plain language, and analyze session traces to recommend what to improve.
Key features:
- Recommends improvements from real session traces rather than assumptions
- Tests each change it makes
- Works on agents, subagents, and individual actions
11. Multi-Agent Orchestration
Multi-Agent Orchestration (generally available) routes work across specialized agents so they operate as one coordinated team when a job crosses roles, systems, or stages of the customer journey. A service agent can hand a renewal question to a sales agent, or a qualification agent can pass a lead to a routing agent, without losing context.
Key features:
- Routes work between agents based on the task
- Preserves context across handoffs
- Lets teams build small, focused agents instead of one oversized agent
12. Long-horizon runtime
The long-horizon runtime lets agents pursue goals over days and weeks with memory, durable execution, and human checkpoints. It currently powers Hunter, with custom long-horizon agents planned.
Key features:
- Context persists across sessions
- Plans adjust as conditions change
- People stay in control through built-in checkpoints
13. AI Skills in Coworker
AI Skills let employees teach Agentforce Coworker a task once and reuse it across Slack, Microsoft Teams, desktop, mobile, ChatGPT, and Claude (pilot now, GA planned October 2026).
Key features:
- Record a proven way of working once
- Reuse it across tools without rebuilding
- Keeps results consistent wherever work starts
14. Agentforce Mobile SDK
The Agentforce Mobile SDK lets development teams embed AI agents directly into mobile applications using native iOS and Android SDKs. This extends agent capabilities to field teams, mobile customers, and any workflow that runs outside the browser.
Key features:
- Native iOS and Android SDKs for direct app integration
- Extends agent capabilities to field teams and mobile customer experiences
- Connects mobile interactions to the same CRM data and trust protections
15. Agentforce MCP support
Agentforce MCP support uses Model Context Protocol, an open standard, to connect Agentforce agents to external tools and resources through verified MCP servers from AgentExchange partners. Salesforce’s Headless Toolkit also exposes Salesforce capabilities through MCP servers and APIs so registered external agents can act inside Salesforce under governed permissions.
Key features:
- Connects agents to external systems and models using open standards
- Verified partner MCP servers reduce custom integration work
- Supports multi-vendor agent setups without building each connection from scratch
16. AgentExchange
AgentExchange is the marketplace for pre-built agents, actions, MCP servers, plugins, and templates built by Salesforce partners and the developer community. At Dreamforce 2026, Salesforce listed integrations from companies including Anthropic, AWS, Google, Microsoft, DocuSign, and Rippling.
Key features:
- Pre-built agents and connectors across many industries and use cases
- Shorter build time by starting from partner-built templates
- Publish custom agents for reuse across organizations
Agentforce AI use cases across Industries
The agentforce ai use cases that deliver the fastest ROI depend on where your biggest operational costs and revenue gaps sit. CTOs and digital transformation leaders need to know where agents deliver impact without adding complexity. eCommerce managers and business unit heads need automation that works at scale without breaking the customer experience. Here is where Agentforce fits.
Retail and E-Commerce
Retail agents handle product recommendations, order status, returns, and post-purchase support. Inventory agents monitor stock levels and trigger reorder workflows automatically. Associates in physical stores get product and customer information surfaced during in-store conversations.
Companies using Agentforce in retail include Saks, Williams-Sonoma, Pandora, and SharkNinja.
Healthcare
Healthcare organizations can use agents to handle appointment scheduling, intake questions, patient record updates, and post-visit follow-ups . Predictive agents analyze data patterns to surface early warning signals for care teams, without replacing clinical judgment. All of this runs within Salesforce Health Cloud’s HIPAA-compliant setup.
Financial Services
Banks use agents for account inquiries, transaction verification, and fraud alerts. Insurance companies use them for claims intake, document collection, and case routing. Compliance agents monitor interactions for regulatory issues before they escalate. Investment agents analyze customer financial profiles and surface relevant options across savings, investment, and credit products.
Manufacturing
Manufacturers use agents to track orders, handle supply chain exceptions, and communicate with suppliers. Predictive maintenance agents analyze equipment data and schedule service before something breaks. Companies like FedEx and Dell have deployed Agentforce for logistics and supply chain coordination.
Telecommunications
Telecom agents handle billing questions, troubleshoot connectivity issues, and recommend plan upgrades based on actual usage data. Proactive agents reach out before contract renewals with relevant options, reducing churn before it happens.
Travel and Hospitality
Travel agents handle booking changes, loyalty inquiries, and disruption support, including rebooking passengers automatically when flights are canceled. Finnair and Heathrow Airport both use Agentforce for passenger services at scale.
Education
In education, agents answer prospective students’ questions at any hour, register students for campus visits, and guide applicants through the process. For enrolled students, agents handle registration questions, financial aid inquiries, and basic administrative requests.
Real Estate
Real estate AI agents qualify buyer leads, schedule property viewings, answer listing questions, and send personalized property alerts when new listings match a buyer’s stated preferences.
Ranosys can help set up Agentforce for you
Ranosys has spent 18+ years building and deploying Salesforce solutions for enterprises across retail, eCommerce, financial services, healthcare, manufacturing, and more. As a 3X award-winning Salesforce Summit Partner, we have the certifications, the cross-industry experience, and the hands-on Agentforce deployment track record to get this right for your business from day one.
We have helped global brands across retail, eCommerce, financial services, healthcare, and manufacturing deploy Salesforce solutions that scale. We bring that same experience to Agentforce, identifying where agents will deliver the fastest impact in your environment and deploying them without disrupting what is already live.
How we help you deploy Agentforce:
- Assess your current Salesforce setup and identify the one or two use cases most likely to show measurable ROI within months
- Evaluate data readiness before any build begins so agents have access to accurate, structured information from day one
- Handle end-to-end configuration, including Flows, APIs, third-party integrations, and Trust Layer governance
- Set up Observability dashboards so you have full visibility into agent performance from the moment they go live
- Stay involved after launch to optimize based on real interaction data, not assumptions
Most Agentforce deployments do not fail because of the technology. They fail because the first use case was too broad, the underlying data was not ready, or the team did not have a clear measure of success before going live. We make sure none of that happens.
Not sure if Agentforce makes financial sense for your business yet? Use Salesforce’s Agentforce ROI Calculator to get a directional estimate before committing to anything.
If you are ready to take it further or have an existing deployment that is not performing as expected, we can give you an honest assessment and a clear path forward.
