What Are Use Cases for Agentic AI? Real Examples by Business Function

Agentic AI is software that is handed a goal, works out the steps, and acts across real tools until the job is done, checking its own work along the way. That makes it different from a chatbot that answers one prompt or an RPA bot that replays a fixed script. Real examples of agentic AI in production include Klarna’s AI assistant resolving refunds and payment questions (2.3 million conversations in its first month), Salesforce Agentforce closing support cases (over a million conversations), GitHub Copilot’s coding agent and Devin turning issues into tested pull requests, Uber’s agents generating and validating tests (around 21,000 developer hours saved), and Ramp’s agents reviewing expenses against policy. Each plans its own steps and acts in real systems, with people approving risky actions.

Below, the examples are grouped by business function, with what each does on its own, which systems it touches, what it delivered, and where people still sign off.

Key takeaways

• An example only counts as agentic if it pursues a goal, plans its own steps, uses tools, and adapts to what it finds. A chat window or a fixed macro does not qualify.
• The most mature agentic AI use cases today are customer service, IT and software development, where tasks are frequent, measurable and easy to check.
• Named deployments at Klarna, Salesforce, Uber, LinkedIn and others show measurable outcomes, but every one of them keeps a human approval step somewhere.
• Gartner expects over 40% of agentic AI projects to be cancelled by the end of 2027, mostly due to cost, unclear value and weak risk controls. Small, well-scoped pilots win.
• The fastest pilot for most teams is an AI employee that works inside the chat tools they already use, rather than a custom build on a framework.

What makes an example agentic (and what does not)

Gartner calls the rebranding of old tools “agent washing” and estimated in 2025 that only around 130 of the thousands of vendors claiming agentic capabilities actually offer them. Use four tests.

Autonomy. The system decides the next step itself instead of waiting for a prompt at every move.

Goal orientation. It works toward an outcome (“resolve this refund request”, “get this test suite green”), not toward a single reply.

Environment interaction. It reads the state of real systems (a CRM, a ticket queue, a repository) and reacts when it changes.

Tool use. It takes actions through APIs and apps: it updates records, sends messages, opens pull requests, books shipments.

Here is how agentic AI compares with the technologies it is often confused with:

Rule of thumb: generative AI writes the email, RPA clicks the button it was told to, and agentic AI decides whether the email is needed, sends it after approval and logs it in the CRM.

The best agentic AI examples at a glance

The table summarizes the deployments covered in this guide. Autonomy levels: High = acts end to end and escalates exceptions; Medium = acts, but a human approves key steps; Low = proposes, human executes.

If you want to see what the first row feels like in practice, you can try Viktor free in your Slack workspace with up to $100 in credits and no credit card.

Best agentic AI example for customer service and IT operations

Customer service has the longest track record because the work is a clear loop: intake, triage, context, act, close. IT service desks follow the same loop for access requests and incident triage.

The clearest public numbers come from Klarna. In February 2024 the company said its AI assistant handled 2.3 million conversations in its first month, about two thirds of all customer service chats, doing work equivalent to roughly 700 full-time staff and cutting average resolution time from 11 minutes to under 2. It handles refunds, returns, payment issues and cancellations by acting in Klarna’s own systems, not just by answering questions. Klarna later made sure customers can always reach a person: the strongest deployments keep a human route open.

Salesforce Agentforce

Overview. Agentforce, launched in 2024, is Salesforce’s platform for AI agents built on Salesforce data.

What runs autonomously. The service agent reads a case, pulls customer history and knowledge articles, takes an allowed action (update an order, issue a credit within limits) and closes the case or routes it to a person with a summary.

Systems. Service Cloud, the Salesforce CRM, Data Cloud, knowledge bases and connected channels such as web chat.

Measured outcome. Salesforce runs Agentforce on its own help site and has reported more than a million support conversations handled there, with most resolved without a human.

Where humans stay in the loop. Topics and actions are whitelisted by admins, and anything outside the defined scope is escalated with full context.

Best for. Companies whose customer data and service process already live in Salesforce.

Best agentic AI example for supply chain, finance and risk

Structured data and clear rules make these functions a good fit for autonomous monitoring, while the money involved keeps approval steps strict.

Supply chain orchestration. Agentic systems watch inventory, supplier lead times and demand signals, then propose or place reorders, reroute shipments when a carrier is delayed, and alert planners when a forecast moves outside a range. The model is “agent proposes, planner approves”: small, reversible decisions run alone, large purchase orders need sign-off.

Fraud detection. Banks have scored transactions with machine learning for years. The agentic layer adds investigation: the agent gathers related transactions, checks account history, writes up the case and blocks the payment within policy limits or queues it for an analyst.

Expense oversight. Ramp launched AI agents for finance teams in 2025 that read a company’s expense policy as written and apply it to every transaction, approving clean expenses, asking employees for missing receipts and flagging exceptions to the controller.

Guardrails that matter here: spending caps per action, dual approval above a threshold, read-only access by default, and a log of every decision the agent made and why.

Best agentic AI example for software development

Coding has moved fastest because the environment gives instant feedback: code passes tests or it does not, so a coding agent can plan, act, check and self-correct in a tight loop.

GitHub Copilot coding agent. Introduced in 2025, it lets a developer assign a GitHub issue to Copilot. It works in GitHub Actions, writes the change, runs the tests, opens a draft pull request and responds to review comments. A person still merges. Best for teams on GitHub with well-defined backlog items.

Devin by Cognition. Devin works in its own sandbox with a shell, editor and browser. It takes a task in plain language, plans the steps, writes and runs code, and reports back with a pull request. Cognition highlights large migration and refactoring jobs. Best for teams with a long tail of well-scoped tickets.

Cursor. Cursor’s agent mode and background agents edit many files, run terminal commands and iterate until tests pass, inside the editor. Best for engineers who want an agent beside them while they work.

The pattern across all three: the agent owns the branch, the human owns the merge.

Best agentic AI example for named company deployments

Named deployments with measured outcomes are rarer than demos. These are among the most cited agentic AI examples in real life.

Uber. Uber’s developer platform team has described internal agents built on LangGraph that generate unit tests and validate code changes against best practices. Uber engineers reported that the tooling saved thousands of developer hours, with one figure shared publicly at around 21,000 hours. The agent works inside Uber’s own code platform; developers accept or reject its suggestions.

eBay. eBay has described an internal agentic platform, which it calls Mercury, that powers personalised recommendations and shopping experiences by chaining retrieval, reasoning and actions across its catalogue. The gain eBay emphasises is speed: new agent features ship on shared infrastructure.

LinkedIn. LinkedIn’s Hiring Assistant takes a recruiter’s notes or job description, builds the search, sources and ranks candidates, and drafts outreach, so recruiters spend their time on conversations instead of searching. It is one of the first agentic products aimed at a whole job function.

Together with Klarna and Salesforce above, these deployments share a narrow, high-volume task, clean access to the systems involved, a measurable baseline, and a clear point where a person decides.

Types of agentic workflows behind these examples

Most agentic workflow examples combine a handful of design patterns. Andrew Ng popularised four of them; two more are now standard.

RAG is a building block rather than a workflow: almost every production agent retrieves context before it acts.

Other agentic AI examples worth watching

• HR operations. Answering policy questions, running onboarding checklists across HR, IT and payroll, chasing missing paperwork.
• Healthcare administration. Prior authorisation paperwork and draft clinical notes, always with a clinician signing off.
• Research and analysis. Deep research tools that read dozens of sources and assemble a cited report.
• Security triage. Investigating alerts and closing false positives so analysts focus on real incidents.
• Sales operations. Researching accounts, updating the CRM after calls and drafting follow-ups for a rep to approve.

Costs, risks and guardrails before you deploy

Gartner predicted in June 2025 that over 40% of agentic AI projects will be cancelled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls. The good news is that the causes are predictable.

Why pilots get cancelled.

• The use case was chosen for novelty, not volume.
• Nobody measured the baseline, so nobody can prove value.
• Security would not approve the access the agent needed.
• Cost per task was never estimated.

Common failure modes.

• Runaway loops. An agent retries a failing step forever and burns budget. Set step limits, timeouts and spend caps per run.
• Confident wrong actions. The agent acts on bad data. Require retrieval from the system of record and validation before writes.
• Scope creep. An agent given broad permissions does things nobody asked for. Grant least privilege and expand only after review.

Where to place human approval. Put a person in the loop wherever an action is hard to reverse or visible to customers: money movement, customer emails, code merges, data deletion. Let the agent act alone on reversible, internal steps such as drafting, researching and updating internal notes.

Sizing cost and latency per pilot. Estimate tasks per month, average steps per task and model cost per step, then compare with the current cost of doing the work by hand. A task that takes a person 20 minutes and an agent 3 minutes is a good fit; a task that has to finish in under a second is usually better left to traditional automation. For a packaged option, check Viktor’s pricing: the Team plan is $100 a month for 40,000 shared credits, with no per-seat charge.

Which agentic AI example should you pilot first

Pick the pilot that matches your readiness, not the most impressive demo.

Viktor is the shortest path for most operations teams and founders. He is an AI employee who lives in Slack and Microsoft Teams, connects to 9,800+ tools (most through one-click OAuth), and takes a request in plain language the way a new hire would. He plans the work, pulls data from your CRM, ad accounts, docs and billing, and delivers finished output: a report, a CRM cleanup, a scheduled automation, a small internal app. Sensitive actions such as sending customer emails, moving money or pushing code wait for your approval on an approval card, and every action is logged. If you are still deciding whether this category fits, start with what an AI employee is and how he differs from a chatbot.

Frameworks suit teams with engineers: LangChain gives components, LangGraph adds stateful agent graphs, CrewAI runs role-based agent teams, and MCP connects agents to tools and data.

A practical sequence: pick one high-volume task, measure today’s baseline, run the pilot for four weeks with approval on every external action, then relax approvals only for tasks that were handled correctly every time. You can start that pilot with Viktor today and see results in your own channels within the first week.

FAQ

What is the difference between agentic AI, generative AI and RPA?

Generative AI creates content from a prompt. RPA follows a fixed script. Agentic AI pursues a goal: it plans steps, uses tools, checks results and adapts. Many agentic systems use generative AI for reasoning and call RPA bots as tools.

What can AI agents do on their own?

They resolve routine support requests, triage IT tickets, write and test code, review expenses, update CRMs, monitor inventory and flag fraud. Most pause for approval before irreversible or customer-facing actions.

What are the main types of agentic workflows?

The reflection pattern, tool use pattern, planning pattern, multi-agent pattern and human-in-the-loop pattern, usually combined with retrieval-augmented generation (RAG) so the agent acts on current data.

Which companies use agentic AI in production, and with what results?

Klarna (2.3 million conversations in the first month), Salesforce (over a million conversations on its help site) and Uber (thousands of developer hours saved) have published results. LinkedIn, eBay and Ramp run agentic features in hiring, shopping and finance.

How do you measure the ROI of agentic AI?

Measure the baseline first (volume, time, cost and error rate per task), then compare with the agent’s cost per task including human review time. Count only fully completed tasks.

What are the risks of human-in-the-loop agentic AI?

Approval fatigue: if people approve without reading, the safeguard disappears. Keep approvals for high-impact actions, show the reasoning behind each request, and audit a sample weekly.

Why do so many agentic AI pilots get cancelled?

Gartner points to escalating costs, unclear business value and weak risk controls. Narrow, measurable, high-volume pilots with clear approval rules survive.

Which frameworks are used to build AI agents?

LangChain and LangGraph are the most widely used, with CrewAI, Microsoft AutoGen and the OpenAI Agents SDK as common alternatives. MCP has become the standard way to connect agents to tools. Teams that do not want to build anything can put Viktor to work in Slack or Teams instead.