Agentic AI Examples

Quick answer

Agentic AI examples are AI systems that take a goal, plan the steps, use tools such as APIs, databases and apps, act on their own, and check the result, like a support agent that reads a ticket, checks the order system, issues a refund within policy and closes the ticket. You already see agentic AI in customer support, software development, IT operations, banking, healthcare paperwork, retail, HR, marketing, supply chain and personal productivity. In every good example, the agent handles the repetitive multi-step work and a human approves the risky or high-value decisions.

Key takeaways

1. What makes something an agentic AI example?

Short answer: An AI system counts as agentic when it is given a goal, works out the steps itself, uses tools to act in real systems, and checks and adjusts its own work with limited human supervision.

Google Cloud describes agentic AI as AI that sets goals, plans and executes tasks with minimal human intervention, in a "continuous cycle of perception, planning, action, and reflection" (Google Cloud, checked October 2026). It also gives a simple contrast: generative AI writes marketing material, while agentic AI deploys it, tracks the results and adjusts the campaign.

Before you read the examples, here are five terms you will see again and again:

A quick test you can apply to any "AI agent" product:

Gartner warns about "agent washing", where vendors rebrand chatbots and assistants as agents. In the same release it estimated that only about 130 of the thousands of agentic AI vendors are real (Gartner, June 2025). Use the test above and you will not be fooled. For the full definition and how agents work inside, read our guide on what agentic AI is.

2. Customer support examples

Short answer: Support agents read a customer's message, look up the account and order data, take an allowed action such as a refund or reschedule, reply, and hand complex or angry cases to a human.

Customer support is the most common starting point because the volume is high, the policies are written down, and every step happens in software.

Example 1: Ticket resolution agent

What it does: A customer writes, "My order arrived damaged." The agent reads the message, identifies the order, checks the delivery record and the returns policy, offers a replacement or refund, updates the ticket and sends a reply. It does not stop at a polite answer. It finishes the job.

Tools it uses: the helpdesk system, the order database, the policy knowledge base through RAG, and the email or chat channel.

Human role: a support lead sets the refund limits. Anything above the limit, any legal threat, and any customer who asks for a person goes to a human with a summary already written.

Example 2: Multi-language order and booking agent

What it does: For an Indian e-commerce, travel or telecom company, the agent handles the same request in English, Hindi, Telugu or Tamil. It can change a delivery slot, reschedule a booking or update a plan, then confirm the change by SMS or WhatsApp.

Tools it uses: booking or order APIs, a messaging API, a translation step inside the LLM, and the customer profile.

Human role: humans review conversations the agent was unsure about and update the policy documents the agent reads.

Business teams often build agents like these on enterprise platforms such as Salesforce Agentforce or Microsoft Copilot Studio, a no-code agent builder. Our agentic AI tools guide compares these platforms.

3. Software development examples

Short answer: Coding agents take a task such as "fix this bug" or "add tests", read the codebase, plan the change, edit files, run tests and open a pull request for a developer to review.

Software development is where agentic AI has moved fastest, because code can be tested automatically. That gives the agent a clear way to check its own work.

Example 3: Coding agent that opens pull requests

What it does: You assign a task, for example an issue that says "the login page crashes when the email field is empty". The agent researches the repository, plans the fix, edits the files, runs the tests and linters, and opens a pull request.

Real products: GitHub's documentation says Copilot cloud agent can "research a repository, plan changes, and implement them in the background", works in an ephemeral cloud development environment, and can open a pull request for each task (GitHub Docs, October 2026). Other coding agents follow the same loop: read, plan, edit, test, then hand over for review.

Human role: the developer reviews every pull request. GitHub's own page says session logs do not replace your own review and testing.

Example 4: Test-writing and code review agent

What it does: On every new pull request, the agent reads the changed files, writes missing unit tests, runs them, flags likely bugs or security issues and leaves review comments.

Tools it uses: the Git host API, the CI pipeline (continuous integration, the system that builds and tests code automatically), a test runner and a code search tool.

Human role: a senior developer decides which comments to accept. The agent never merges code into the main branch on its own.

This is also one of the easiest use cases to measure, because you can count pull requests accepted, tests added and bugs fixed.

4. IT operations examples

Short answer: IT operations agents watch alerts, find the likely cause of an incident, run approved fixes from a runbook, and handle routine service desk requests such as access and software installs.

Example 5: Incident triage agent

What it does: At 2 a.m., an alert says a payment service is slow. The agent reads the alert, pulls recent logs and metrics, checks what changed in the last deployment, and writes a short incident summary with the most likely cause. If the runbook says "restart the service when memory crosses X", it can do that step.

Tools it uses: monitoring and logging tools, the deployment history, the runbook (a written list of fix steps) through RAG, and a chat tool to post updates.

Human role: the on-call engineer approves any action beyond the safe, pre-approved list, such as a rollback or a database change.

Example 6: Service desk request agent

What it does: An employee asks, "I need access to the sales dashboard." The agent checks the employee's role, finds the right access group, raises the request, gets manager approval, grants access and closes the ticket.

Tools it uses: the IT service management system, the identity and access system, and email or chat.

Human role: managers approve access, and security teams set rules for what the agent can never grant, such as admin rights.

5. Finance and banking examples

Short answer: Finance agents read invoices, match them to purchase orders, chase missing details and prepare payments, while banking agents gather and check customer documents and prepare fraud or compliance cases for human review.

Banking and finance have strict rules, so the agent prepares and checks while people decide.

Example 7: Accounts payable (invoice) agent

What it does: Invoices arrive as PDFs and emails in many formats. The agent reads each invoice, extracts the vendor, amount, GST details and due date, matches it to the purchase order and goods receipt, and flags mismatches. If something is missing, it emails the vendor to ask.

Tools it uses: document reading (OCR plus the LLM), the ERP system, email, and the company's approval rules.

Human role: the finance team approves payments. The agent never releases money on its own.

Example 8: KYC and fraud alert investigation agent

What it does: When a bank flags a suspicious transaction, the agent collects the customer's history, recent transactions, device and location data and past alerts, then writes a case summary with the reasons for concern. For onboarding, a similar agent checks that KYC (know your customer) documents are complete and consistent.

Tools it uses: the core banking system, the fraud monitoring system, document checks and a case management tool.

Human role: a compliance officer makes the final call on blocking an account or filing a report. OpenAI's agent guide recommends human-in-the-loop for high-risk actions like these (OpenAI, checked October 2026).

6. Healthcare examples

Short answer: Healthcare agents handle paperwork and coordination, such as insurance claims, scheduling and follow-up reminders. Diagnosis and treatment decisions stay with doctors.

Example 9: Insurance claims and pre-authorisation agent

What it does: For a hospital billing team, the agent gathers the discharge summary, bills and policy details, fills the insurer's claim form, checks for missing documents and tracks the claim status. When the insurer asks a query, it drafts the reply with the right documents attached.

Tools it uses: the hospital information system, document reading, the insurer portal or API, and email.

Human role: the billing executive reviews every submission, and doctors confirm any medical detail.

Example 10: Appointment and patient follow-up agent

What it does: The agent books appointments based on doctor availability, sends reminders, reschedules when the patient asks, and after a visit sends medicine and follow-up reminders that the doctor has already prescribed.

Tools it uses: the scheduling system, SMS or WhatsApp messaging and the patient record (read-only for most fields).

Human role: clinic staff handle anything clinical. The agent is told to send any symptom question straight to a nurse or doctor.

7. Retail and e-commerce examples

Short answer: Retail agents help shoppers find and compare products, and help sellers keep catalogues, prices and stock up to date.

Example 11: Shopping assistant agent

What it does: A shopper says, "I need a laptop under ₹60,000 for coding and light gaming, delivered before Friday." The agent asks one or two questions, searches the catalogue, compares specifications, checks stock and delivery dates for the shopper's pin code, and builds a shortlist with reasons.

Tools it uses: product search, inventory and delivery APIs, reviews data through RAG, and the cart.

Human role: the shopper decides and pays. The merchandising team controls which products and offers the agent can recommend.

Example 12: Catalogue and pricing agent

What it does: For a seller with thousands of products, the agent writes product titles and descriptions, fixes missing attributes, watches stock levels and suggests price changes within limits the team has set.

Tools it uses: the product information system, marketplace seller APIs, sales data and a pricing rules file.

Human role: a category manager approves price changes outside the normal range and reviews new listings in sample batches.

8. HR examples

Short answer: HR agents screen applications against a job description, schedule interviews and guide new joiners through onboarding, while recruiters and managers make every hiring decision.

Example 13: Resume screening and interview scheduling agent

What it does: For each opening, the agent reads resumes, compares them with the must-have skills, writes a short fit summary for each candidate, and books interviews in the panel's free calendar slots.

Tools it uses: the applicant tracking system, calendar APIs and email.

Human role: recruiters review the shortlist and make all decisions. Teams should also check the agent for bias, for example by testing whether college names or gaps change its scores.

Example 14: Employee onboarding agent

What it does: When a new joiner accepts an offer, the agent collects documents, raises laptop and access requests, enrols them in mandatory training, and answers policy questions like "how many leaves do I get?" from the HR handbook.

Tools it uses: the HR system, the IT service desk, the learning platform and the policy handbook through RAG.

Human role: HR business partners handle sensitive questions about salary, performance or personal matters.

9. Marketing and sales examples

Short answer: Marketing agents plan, launch, measure and adjust campaigns, and sales agents research prospects and draft personalised outreach for a salesperson to send.

Example 15: Campaign agent

What it does: This is the example Google Cloud uses to explain the difference between generative and agentic AI. Generative AI writes the ad copy. An agentic system goes further: it launches the campaign, tracks clicks and conversions, and shifts budget or changes the message when results drop (Google Cloud, checked October 2026).

Tools it uses: ad platform APIs, analytics, the email marketing tool and a content generator.

Human role: the marketing manager sets the budget cap, brand rules and goals, and approves new creative before it goes live.

Example 16: Sales research and outreach agent

What it does: Given a list of target companies, the agent researches each one from its website and public news, finds the right contact role, writes a short account brief and drafts a personalised first email.

Tools it uses: web search, the CRM (customer relationship management system), and email.

Human role: the salesperson edits and sends the email. Sending messages automatically to strangers can break anti-spam rules and damage the brand, so most teams keep this step human.

10. Supply chain examples

Short answer: Supply chain agents watch orders and shipments, spot delays early, find alternatives, and prepare reorders so planners can act before stock runs out.

Example 17: Supplier delay and procurement agent

What it does: The agent tracks open purchase orders. When a supplier's shipment slips, it checks which customer orders are affected, looks for alternative suppliers already approved in the system, requests quotes and prepares a recommendation for the buyer.

Tools it uses: the ERP, the logistics tracking system, supplier email and the approved vendor list.

Human role: the procurement manager chooses the supplier and signs off on the purchase order.

Example 18: Demand and replenishment agent

What it does: Every morning the agent reads yesterday's sales, upcoming festival dates and current stock in each warehouse, updates the forecast, and drafts transfer and reorder plans.

Tools it uses: sales data, a forecasting model, warehouse systems and a calendar of events.

Human role: planners review and approve the plans, especially before big sale days when mistakes are costly.

11. Personal productivity examples

Short answer: Personal agents manage your inbox and calendar, run research for you, and automate repetitive tasks across the apps you already use.

Example 19: Inbox and calendar agent

What it does: The agent sorts your email, drafts replies in your style, finds a meeting slot that works for everyone, sends the invite after you approve it, and prepares a one-page brief before each meeting.

Tools it uses: email and calendar APIs, your documents, and a notes app.

Human role: you approve anything sent in your name.

Example 20: Research and workflow automation agent

What it does: You ask, "Find five Python libraries for PDF parsing, compare them and save the summary to my Notion page." The agent searches, reads documentation, builds a comparison and saves it. Or it runs on a trigger, such as "every time a new lead fills the form, research the company and post a summary to Slack".

Real products: low-code tools such as Zapier Agents and n8n, a fair-code workflow automation tool with AI agents and a self-hosted Community Edition, let you build this kind of agent by connecting apps visually.

Human role: you choose which apps the agent can touch and check its research before you rely on it.

12. Agentic AI use cases by industry (summary table)

Short answer: The table below lists the main agentic AI use cases in each industry, the tools the agent typically uses and the decision a human keeps.

Gartner expects this list to grow quickly. It predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, and at least 15% of day-to-day work decisions will be made autonomously, up from 0% in 2024 (Gartner, June 2025).

13. What all 20 examples have in common

Short answer: Every example combines an LLM with tools, memory and instructions, follows one of a few known design patterns, and keeps humans at the risky decision points.

Look back at the 20 examples and you will see the same parts each time:

OpenAI's practical guide names the first three (model, tools, instructions) as the core components of any agent, and suggests building agents where you face complex decisions, rules that are hard to maintain, or lots of unstructured data such as emails and PDFs (OpenAI, checked October 2026).

Anthropic's engineering guide describes the common patterns behind these systems (Anthropic, December 2024):

Anthropic also separates workflows, where code follows a set path, from agents, where the LLM directs its own process and tool use. Its advice is to start with the simplest design that works. Many of the examples above are really workflows with one or two agentic steps, and that is fine.

When several agents with different roles work together, such as a planner, a researcher and a writer, you have a multi-agent system. Our agentic AI frameworks guide explains how tools like LangGraph, CrewAI, Microsoft Agent Framework and the OpenAI Agents SDK build these systems.

14. How to pick your first agentic AI use case

Short answer: Choose a task that is frequent, multi-step, rule-based, low-risk to get wrong, and easy to measure, then start with one agent, a few tools and a human approval step.

This section is for two readers: a team choosing its first business use case, and a learner choosing a first portfolio project. The checklist is the same.

A simple scoring checklist

Score each idea from 1 to 5 on these questions:

Ideas that score high on all seven are your best first use cases. Ticket triage, invoice matching, service desk requests, resume summaries and research agents usually score well. Autonomous payments, medical advice and automatic hiring decisions score low on risk and should wait.

Start small, then widen

OpenAI's guide recommends starting with a single agent and moving to multiple agents only when one agent struggles with too many tools or too complex instructions. It also lists guardrails worth adding from day one: relevance and safety classifiers, a PII (personal data) filter, moderation, tool safeguards, rules-based checks and output validation (OpenAI, checked October 2026).

Why so many projects fail

Gartner predicts 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 (Gartner, June 2025). In plain words: teams pick a use case that is too vague, too expensive to run, or too risky to trust. The checklist above protects you from all three.

If you are a learner, pick one example from this guide and build a small version of it. Our list of AI agent projects gives you more ideas with step-by-step scope.

15. Where agents still need humans

Short answer: Humans stay in charge of money, health, hiring, security, legal matters and anything that cannot be undone, and they review the agent's work regularly.

Across the 20 examples, the human role follows the same rules:

LLMs can still produce confident but wrong answers, which is called hallucination. Tools reduce this by giving the agent real data, and human checkpoints catch what slips through. This is why the most successful examples are designed as a partnership: the agent does the repetitive work, and people make the decisions that matter.

The skills behind all of this, such as tool calling, RAG, orchestration, evaluation and guardrails, are now among the most useful things a developer can learn.

16. How Brolly Academy helps

Short answer: Brolly Academy's 3-month Agentic AI Course in Hyderabad teaches you to build the kinds of agents in this guide, with six capstone projects that map closely to the examples above.

Reading examples is a start. Building them is what gets you hired. The Agentic AI Course in Hyderabad at Brolly Academy runs for 3 months: foundations in month 1, tools and workflows in month 2, and projects and deployment in month 3, plus career preparation.

Here is how the course capstone projects line up with this guide:

What the course page lists:

The next batch starts on Monday, 2 November 2026, and new batches start every week. You can book a free demo class first.

Frequently asked questions