Table of Content
What are examples of AI agents in business?
An AI agent is software that takes a goal, decides the steps and acts in your tools. Real examples include Klarna’s customer service assistant, Salesforce’s Help Agent, Lyft’s support agent, GitHub’s Copilot coding agent, ChatGPT agent and Microsoft’s payables and sales order agents in Business Central. Each one does a task from start to finish, not just a single answer.
The short version
- An agent acts, a chatbot answers. An agent plans steps, uses tools and finishes a task, such as opening a pull request or matching an invoice.
- Support is the proven starting point. Klarna, Salesforce and Lyft all report large shares of support work handled by an AI agent.
- Company numbers are self-reported. Treat the figures below as direction, and note how each company defines “resolved”.
- Finance and operations agents are arriving. Business Central ships expense, sales order and payables agents.
- Developers got agents first. GitHub’s coding agent takes an assigned issue and returns a draft pull request for review.
- Everything below was checked on October 13, 2026. Vendors change features and prices often, so confirm before you buy.
What counts as an AI agent
An AI agent is software that works toward a goal. You give it an outcome, it chooses the steps, calls tools such as your CRM, email or code repository, and checks its own progress. This is also what people mean by agentic AI.
Three things separate an agent from the tools around it:
- A chatbot answers questions. It reads a message and replies. Many modern chatbots are built on the same models, but they stop at the reply.
- Automation follows fixed rules. “When a form is filled, create a record” is automation. It never decides anything.
- An agent plans and acts. It reads the situation, picks a path, uses tools and hands off to a person when it is unsure.
The line is blurry in practice, and most products mix all three. For a deeper comparison, read our guide to the best AI agents for business.
12 AI agent examples from real companies and products
The examples below are documented by the companies or vendors themselves. Where a number is a company claim, we say so.
1. Klarna: a customer service agent at scale
Klarna’s AI assistant, built with OpenAI, launched globally in early 2024. In its February 27, 2024 announcement, Klarna said the assistant had handled 2.3 million conversations in its first month, two-thirds of its customer service chats, and did the work of 700 full-time agents. The company also reported that resolution time fell from 11 minutes to under 2 minutes and repeat inquiries dropped 25%. It covered 23 markets in more than 35 languages.
What it teaches: support works because requests repeat. Caution: the 2.3 million figure is a first-month claim, not a steady monthly volume.
2. Salesforce Help Agent: the vendor runs its own agent
Salesforce runs an Agentforce agent on its own help site. Salesforce coverage in 2026 reported that the Help Agent had handled 4.3 million inquiries and resolved 70% of them. Earlier Salesforce figures were higher, for example more than 84% of questions in the first months, but they used different definitions of “resolved”. The company states that only a small share of conversations are handed to human engineers.
What it teaches: ask any vendor how it defines resolution and over what period. Reopen and repeat-contact rates matter as much as the headline number.
3. Lyft: Claude for driver and rider support
Lyft uses Anthropic’s Claude, through Amazon Bedrock, in its customer care. Lyft reports an 87% reduction in average resolution time. TechCrunch notes that an issue counts as resolved when the customer answers yes to “Did we resolve your issue?”, so the metric comes from customer replies, not an outside audit.
What it teaches: a clear escalation path matters. The agent handles the common cases and people handle the rest.
4. Morgan Stanley: an internal knowledge agent for advisors
Morgan Stanley built the AI @ Morgan Stanley Assistant on OpenAI’s GPT-4 so that wealth management advisors can search the firm’s own research and documents. OpenAI’s case study reports that more than 98% of advisor teams actively use it. The study credits an evaluation framework that tests each AI use case before launch.
What it teaches: internal agents often beat customer-facing ones as a first project, because the risk is lower and the users are motivated.
5. GitHub Copilot coding agent: issues in, pull requests out
GitHub’s docs describe how you assign an issue to Copilot like a human developer. Copilot works in a contained environment, creates a draft pull request and requests your review. If you leave feedback, it revises the pull request. It is available on Copilot Pro, Pro+, Business and Enterprise plans.
What it teaches: the safest agents produce something a person approves. A draft pull request is a good model for any agent that changes real systems.
6. ChatGPT agent: a general agent with its own computer
OpenAI launched ChatGPT agent on July 17, 2025. It uses its own virtual computer to browse sites, filter results, run code and produce editable slides and spreadsheets, and it asks you to log in securely when needed. Access started with paid plans.
What it teaches: general agents suit research and one-off tasks. For repeated business processes, a configured agent with fixed tools is usually more reliable.
7. Zapier Agents: agents connected to your apps
Zapier Agents are AI teammates that work across the apps Zapier connects; Zapier claims more than 9,000. Its templates include meeting prep, enterprise lead qualification, Zendesk ticket responses, support email drafts, candidate ranking, Slack thread summaries and expense categorization.
What it teaches: the value is in the integrations. An agent that cannot reach your tools cannot finish the job.
8. Lindy: an AI teammate for routine admin
Lindy’s own page lists tasks such as reconciling Stripe charges against a ledger, updating CRM records, triaging support escalations and running scheduled daily briefs. Team plans start at $29.99 a month for 3,000 credits.
What it teaches: small teams use agents for back-office chores before customer-facing work.
9. Intercom Fin: an agent you pay for by outcome
Intercom prices its Fin AI Agent at $0.99 per outcome. An outcome is counted when a customer confirms the issue is resolved, stops asking for help after Fin responds, or Fin completes a workflow, and each conversation is charged once.
What it teaches: outcome-based pricing ties cost to results. Compare it with per-seat and per-conversation models before you commit.
10. Shopify Sidekick: an agent inside a store admin
Shopify’s developer documentation describes Sidekick as the AI assistant built into Shopify, which merchants use to get work done in the admin. Merchants describe a task in plain language, and Sidekick uses the store’s own data to answer questions, draft content and set things up for the merchant to review.
What it teaches: an agent embedded in the system of record needs no integration work. See our guide to AI tools for Shopify.
11. Business Central: finance and sales agents inside ERP
Microsoft Dynamics 365 Business Central lists an Expense Agent for reports and approvals, a Sales Order Agent that turns emails into orders and quotes, and a Payables Agent for invoice capture, matching and routing. You can also build custom agents with Copilot Studio. Our guide to AI agents in ERP covers what to expect.
What it teaches: the biggest agent wins are often in paperwork-heavy processes that already live in your ERP.
12. Copilot Studio: build your own business agent
Microsoft’s Copilot Studio is a no-code platform for building conversational and autonomous agents. Microsoft’s example use cases include balance sheet reconciliation, recruitment screening, upsell recommendations, IT support and contract review. Agents can be published to websites, internal channels or Microsoft Copilot.
What it teaches: once the basics work, a platform lets you build agents for processes no vendor ships.
The 12 examples side by side
| Example | Function | Reported result | Who verifies it |
|---|---|---|---|
| Klarna | Customer service | 2.3M chats in month one | Company |
| Salesforce Help Agent | Support | 70% of 4.3M inquiries resolved | Company |
| Lyft | Support | 87% faster resolution | Company |
| Morgan Stanley | Internal knowledge | 98% of advisor teams use it | OpenAI case study |
| GitHub Copilot agent | Software tasks | Draft pull request per issue | Product docs |
| ChatGPT agent | Research, tasks | Slides, sheets, browsing | Product docs |
| Zapier Agents | Cross-app work | 9,000+ apps claimed | Vendor |
| Lindy | Admin tasks | From $29.99 a month | Vendor |
| Intercom Fin | Support | $0.99 per outcome | Vendor |
| Shopify Sidekick | Store admin | Plain-language store tasks | Vendor |
| Business Central agents | Finance, sales orders | Three named agents | Vendor |
| Copilot Studio | Custom agents | No-code builder | Vendor |
AI agent use cases by department
The examples fall into repeating patterns. These are the AI agent use cases that show up across industries, with the example from above that proves each one.
| Department | Agent use case | Seen in |
|---|---|---|
| Customer support | Resolve common questions, hand off the rest | Klarna, Salesforce, Lyft, Fin |
| Sales | Qualify leads, prepare calls, update the CRM | Zapier Agents, Lindy |
| Finance | Capture and match invoices, process expenses | Business Central |
| Operations | Turn emails into orders and quotes | Business Central |
| Engineering | Fix issues and open pull requests | GitHub Copilot agent |
| HR | Screen and rank candidates | Zapier, Copilot Studio |
| Knowledge work | Answer questions from internal documents | Morgan Stanley |
| E-commerce | Manage catalog, discounts and reports | Shopify Sidekick |
AI agent ideas you can start with this quarter
Start where requests repeat, rules are clear and a mistake is cheap to fix. These agent ideas fit that test.
- A support triage agent. It tags and answers the top ten questions, and routes the rest with context attached.
- A meeting prep agent. It gathers the CRM history and recent emails before each sales call.
- A lead qualification agent. It scores form submissions against your criteria and books the good ones.
- An invoice matching agent. It reads invoices, matches them to purchase orders and flags exceptions.
- An internal policy agent. It answers “how do we handle refunds?” from your own documents, with sources.
- A reporting agent. It builds the weekly sales or ad-spend summary and flags anomalies.
For a step-by-step build, follow our guide on how to build an AI agent for your business.
What separates agents that work from agents that fail
The examples above share five habits. Teams that skip them are the ones in the news for the wrong reasons.
- A narrow first job. One process, clear success criteria and a named owner.
- Tested before launch. Morgan Stanley’s case study credits its evaluation framework for the result.
- Human approval for risky actions. GitHub’s agent returns a draft for review. Sidekick sets things up for the merchant to review.
- A path to a person. Every support agent above hands off hard cases.
- Accountability stays with you. In 2024 a Canadian tribunal held Air Canada responsible for wrong refund advice its chatbot gave a passenger, rejecting the idea that the chatbot was a separate entity. Your agent speaks for your business.
Buy, configure or build
- Buy when your need matches a product feature, such as a support agent inside your helpdesk.
- Configure a platform such as Zapier Agents or Copilot Studio when the process is common but your tools are specific.
- Build custom when the agent must read your own systems, follow strict rules or work across several tools. Our agentic AI development services cover that, and our AI agent development company page explains how a project runs from scope to launch. Multi-agent setups need coordination, which we cover in our AI agent orchestration guide.
Frequently Asked Questions
What are examples of AI agents?
Examples include Klarna’s customer service assistant, Salesforce’s Help Agent, Lyft’s support agent, GitHub’s Copilot coding agent, ChatGPT agent, Zapier Agents and Business Central’s payables and sales order agents. Each pursues a goal and uses tools, rather than only answering questions.
What is the difference between an AI agent and a chatbot?
A chatbot replies to messages. An AI agent also plans steps and acts in other systems, such as updating a CRM record, matching an invoice or opening a pull request. Many products blend both, so judge them by what they can do, not by their label.
What are agentic AI examples in business?
Agentic AI examples include support agents that resolve tickets, finance agents that process invoices and expenses, coding agents that fix issues, and research agents that browse and compile reports. Business Central, Salesforce and GitHub all ship named agents of these types.
What are the best AI agent use cases for a small business?
Customer support triage, meeting preparation, lead qualification, invoice matching and weekly reporting. They share three traits: repeated requests, clear rules and low cost when a draft is wrong. Start with one and measure it for a month.
How much does an AI agent cost?
It depends on the model. Lindy’s team plan starts at $29.99 a month for 3,000 credits, Intercom Fin charges $0.99 per outcome, and Copilot Studio sells credit packs of 25,000 for $200 a month. Custom agents are quoted by scope. Always get a written estimate.
Can AI agents replace employees?
They take over repeatable tasks, which changes jobs more than it removes them. Klarna described its assistant as doing the work of 700 full-time agents, which shows the scale of the task share. Plan a handoff path for hard cases, not a blanket replacement.
Are AI agents safe to use with customer data?
They can be, with controls. Use business plans with clear data terms, limit the tools an agent can reach, require approval for risky actions and log what it does. You remain responsible for what your agent tells customers.
How do I build an AI agent for my business?
Pick one process, list its steps and exceptions, choose a platform or a custom build, connect the tools it needs, test it on real cases and launch with human review. Expand only after the first agent hits its target.
How do I know if an agent is working?
Track the measure tied to its job, such as resolution rate, handling time or hours saved, along with reopen and escalation rates. A good resolution rate with a high reopen rate means the agent is closing tickets without fixing problems.
Bottom Line
The strongest AI agent examples share a pattern: one clear job, access to the right tools, tested behavior and a path to a person. Support, finance, engineering and internal knowledge are where the evidence is today.
Pick one process that repeats, define what success looks like and start small. When you want an agent that works with your own systems, our agentic AI development services team can scope it with you.
