15 Agentic AI Examples and Use Cases for Businesses 

The best way to understand agentic AI is to see what it actually does in the real world. From a hospital that cut sepsis deaths by 27% to a tax agency that reduced a 10-day process to 30 minutes, these are not projections or case studies from a whitepaper. These are real deployments, with real numbers, happening right now. Here are 15 real agentic AI examples, drawn from verified sources.

Real-World Examples for Agentic AI 

Explore how businesses are using agentic AI to accelerate growth and ease operations. These are real deployments, with real numbers, happening right now.

1. JPMorgan Chase: 450+ Use Cases Across an Entire Bank

Source: McKinsey Interview, October 2025 | AI News, December 2025

JPMorgan Chase is executing what may be the most ambitious agentic AI deployment in corporate history. With an $18 billion annual technology budget and over 450 AI use cases in production, the bank has built a proprietary platform called LLM Suite, used daily by more than 200,000 of its employees.

Agentic AI agents at JPMorgan now create investment banking presentations in 30 seconds (work that previously took junior analysts hours), draft M&A memos, automate trade settlement, detect fraud in real time, and equip call center agents with instant context-aware answers. Portfolio managers have cut research time by up to 83%. The bank estimates $1.5 to $2 billion in annual business value from its AI programs, and it holds the number one global AI maturity ranking among banks according to the Evident AI Index 2025.

CEO Jamie Dimon has said: "It affects everything, risk, fraud, marketing, idea generation, customer service. And this is the tip of the iceberg."

2. Rachio: Scaling Support for 1 Million Users with Crescendo

Source: Crescendo Case Study: Rachio

Rachio, a smart home irrigation company serving over one million customers, was struggling to maintain support quality as its user base grew. Its Head of Customer Support Operations described their previous AI accuracy as sitting at just 20%. After deploying Crescendo's agentic AI platform, Rachio reached 95% to 99% AI accuracy within weeks, achieved an overall CSAT of 95%, and now handles over one million annual tickets, all managed by a single customer service leader. The Crescendo system integrates agentic AI for routine resolution with seamless human escalation for edge cases.

3. LinkedIn: Automating the Hiring Workflow

Source: LinkedIn Engineering Blog

LinkedIn's Hiring Assistant is a multi-agent system designed to help corporate recruiters work faster. One "supervisory" agent orchestrates a set of specialized sub-agents, each responsible for a different piece of the recruitment process: writing job descriptions, sourcing candidates, drafting outreach messages, generating screening questions, and ranking applicants. The orchestrator breaks down a recruiter's request into tasks, assigns them to the right sub-agent, and assembles the results.

Each recruiter gets their own dedicated agent instance, meaning the system learns the preferences and patterns of the individual it works with over time. This is a real example of a multi-agent architecture being used in production at enterprise scale.

4. DoorDash: Voice Support for Delivery Workers

Source: AWS Case Study, 2024-2025

DoorDash uses Amazon Bedrock with Anthropic's Claude to power a voice AI agent that handles hundreds of thousands of support calls daily from Dashers (its delivery workers). The agent maintains conversational latency at or below 2.5 seconds, fast enough to feel like a real phone call, and has reduced escalations to human agents by several thousand per day. For a company running a gig economy at scale, with support needs that spike unpredictably, having an agent that can absorb volume without delay is operationally critical.

5. eBay: Smarter Product Recommendations

Source: eBay Engineering: Mercury Platform

eBay built an internal agentic AI platform called Mercury to power its product recommendation engine across a marketplace of two billion items. The platform uses retrieval-augmented generation (RAG) to combine the AI's output with real-time inventory data, so recommendations aren't just contextually smart, they're actually available to buy. A Listing Matching Engine converts the AI's text-based suggestions into live listings. The platform also includes internal models to detect and block prompt injection attacks, a sign of how seriously production-grade agentic systems need to address security.

6. John Deere: AI in the Field

Source: John Deere See and Spray Technology

John Deere's See and Spray technology uses computer vision and agentic workflows to distinguish crop plants from weeds in real time, applying herbicide only where needed. The result is a reported 70% reduction in chemical usage. This is one of the clearest examples of agentic AI working outside of software, acting autonomously in the physical world with real environmental and cost consequences.

7. Webconnex: Growing Sales Without Growing Support Costs

Source: Crescendo Case Study: Webconnex

Webconnex, an event management software company, faced a familiar problem: support demand spiked sharply around events, making it impossible to staff efficiently for peaks without overpaying during quiet periods. After moving from a traditional IVR system to Crescendo's agentic AI voice and chat assistants, Webconnex achieved 75% chat CSAT, resolved more than 50% of inquiries instantly, and grew ticket sales by 12,000 year-over-year without scaling its support headcount. CEO Eric Knopf noted that the solution could "adapt as quickly as our needs change."

8. EVPassport: 70% AI Resolution for EV Charging Support

Source: Crescendo Case Study: EVPassport

EVPassport, a fast-growing EV charging network, needed to deliver 24/7 customer support across a geographically distributed infrastructure. After deploying Crescendo's agentic AI platform, 70% of customer inquiries are now handled entirely by AI, with human agents responding in under 30 seconds for escalated cases. EVPassport's AI Lead summed it up: "We are the most reliable EV charging network in the United States and the most advanced in using AI to deliver support, and Crescendo is making that possible."

9. Klarna: Customer Service at Scale (and What They Learned)

Source: Klarna Press Release, 2024 | Case Analysis

In January 2024, Klarna deployed an AI assistant built on OpenAI across 23 markets and 35 languages. In its first month alone, the agent handled 2.3 million customer conversations, doing the equivalent work of 700 full-time employees. Average resolution time dropped from 11 minutes to under 2 minutes, repeat inquiries fell by 25%, and customer satisfaction scores matched those of human agents. Klarna projected $40 million in profit improvement for 2024.

But the story has an important second chapter. By mid-2025, Klarna's CEO admitted that the push to go AI-only had compromised the quality of customer experience for complex or emotional cases. The company reversed course, not by abandoning AI, but by adopting a hybrid model. AI handles routine, high-volume requests while human agents manage nuanced situations. Today the system supports the equivalent of 800 full-time employees' worth of work, more than when they tried to go fully automated.

The lesson: Agentic AI works best as a partner to humans, not a replacement for human judgment.

10. Duke University Hospital: Predicting Sepsis Before It Strikes

Source: Duke Institute for Health Innovation | Duke Today, 2024

Duke University Hospital deployed Sepsis Watch in November 2018, making it the first deep learning model integrated into routine clinical care in the United States. The system continuously monitors patient data across the entire hospital, scoring every adult patient hourly for their risk of developing sepsis, the number one cause of death in hospitals. It can predict the onset of sepsis a median of 5 hours before clinical presentation, giving care teams critical lead time to intervene.

The results are documented: after Sepsis Watch went live, sepsis-related deaths at Duke dropped by 27%, and the hospital doubled its compliance with the federally tracked sepsis treatment bundle. A 2025 multisite validation study published in NPJ Digital Medicine confirmed the model's strong performance (AUROC of 0.906 to 0.960) held up across four geographically distinct hospitals, demonstrating that agentic AI built for healthcare can travel beyond its original institution. The system is now active across all three Duke Health hospitals.

11. Amazon: 4,500 Developer-Years of Work Saved with Q Developer

Source: Amazon CEO Andy Jassy, Q2 2024 Earnings Call | Digiday coverage

Amazon used its own agentic AI coding tool, Amazon Q Developer, to migrate 30,000 internal software applications from Java 8 and Java 11 to Java 17, a task that typically takes developers up to two full days per application. By assigning the work to coding agents instead, Amazon saved over 4,500 developer-years of work and generated $260 million in annualized efficiency gains from improved performance and reduced infrastructure costs. Developers accepted 79% of the auto-generated code without any additional changes.

Amazon CEO Andy Jassy disclosed the results publicly on the Q2 2024 earnings call, describing the number as "crazy but real." The case is one of the clearest real-world proofs that agentic AI can take on large-scale, tedious engineering work that would otherwise consume enormous human time, at a quality level developers trust enough to ship without significant rework.

12. Carvana and EY: Using GitHub Copilot Coding Agent in Production

Source: GitHub Press Release, May 2025

When GitHub launched its Copilot coding agent in May 2025, two early enterprise adopters shared direct results. Carvana, the online used car marketplace, reported that the agent converts specifications to production code in minutes, increasing development velocity and freeing engineers to focus on higher-level creative work, according to Alex Devkar, Senior Vice President of Engineering and Analytics. EY, the professional services firm, described the agent as opening doors for developers to have their own agent-driven team working in parallel, allowing them to assign tasks that would typically pull them away from deeper, more complex work.

The Copilot coding agent runs asynchronously in the background: a developer assigns a GitHub issue to Copilot, the agent spins up a secure environment, writes code, runs tests, and opens a pull request for human review. All pull requests require human approval before any CI/CD workflows run, keeping engineers in control of what ships. GitHub reports the agent performs best on low-to-medium complexity tasks in well-tested codebases, which covers a significant portion of the day-to-day backlog at most engineering teams.

13. Meister: Clearing a 1,000-Ticket Multilingual Backlog

Source: Crescendo Case Study: Meister

MeisterTask, a project management SaaS company, came to Crescendo with a backlog of over 1,000 support tickets spanning multiple languages. Within two weeks of going live, the Crescendo AI platform was handling multilingual inquiries, clearing the backlog, and maintaining a 99.2% quality score. The Customer Success Team Lead described it as making "every day feel like the slow season for our internal support team." This case is notable because it shows agentic AI handling not just volume but multilingual complexity, a challenge that traditionally requires a large, geographically distributed support team.

14. Capital One: Chat Concierge for Auto Dealerships

Source: Fortune, December 2025

Capital One deployed an agentic AI tool called Chat Concierge specifically designed for its business customers in the auto dealership industry. The agent helps car buyers ask questions about vehicles, compare options, and schedule test drives or appointments with salespeople, targeting one of the highest-value moments in the purchase journey. According to Prem Natarajan, Capital One's head of enterprise AI, Chat Concierge has been embraced by dealers because it is 55% more successful at converting customer engagement than previous approaches.

Capital One intentionally started at "the low end of the risk spectrum" while targeting use cases with enough complexity to generate meaningful learning. After launch, the team reduced the agent's response latency fivefold by building their own proprietary multi-agent workflow, a sign of how much performance can improve once organizations move from off-the-shelf deployments to purpose-built architectures. The case illustrates a pattern seen across successful agentic deployments: start focused, measure carefully, and iterate fast.

15. U.S. Internal Revenue Service (IRS): Automating Tax Case Processing with Agentforce

Source: Axios, November 2025 | Diginomica, December 2025

The U.S. Internal Revenue Service deployed Salesforce's Agentforce platform across three divisions: the Office of Chief Counsel, Taxpayer Advocate Services, and the Office of Appeals. The deployment came at a challenging moment: the IRS had seen its workforce reduced by roughly 25%, shrinking from about 103,000 employees to 77,000, leaving remaining staff to manage the same volume of taxpayer cases with significantly fewer people.

The results in the Office of Chief Counsel are specific and documented. Agentforce automated up to 98% of previously manual activities in that division, reducing the time to fully open a tax court case from 10 days to 30 minutes. A separate IRS division reported saving an estimated 500,000 minutes annually after retiring legacy systems. Agents are used for tasks like case summarization, data search, and generating responses to taxpayer inquiries, with human IRS staff retaining final decision-making authority on all case outcomes.

Salesforce's public sector executive was clear that the agents are "not allowed to make final decisions, they're not allowed to disperse funds." This makes the IRS one of the most significant real-world examples of agentic AI deployed in a high-stakes government context, with genuine accountability guardrails built in by design.

Agentic AI Use-Cases for Businesses 

You don't need to be JPMorgan or eBay to benefit from agentic AI. The technology is increasingly accessible through platforms that handle the complexity for you. Here's where it makes the most practical difference across common business functions.

1. Customer Support

This is currently the most mature and proven use case. Agentic AI can handle a wide range of customer requests around the clock, without queues, in multiple languages, and with consistent quality. The key is pairing it with a clean escalation path so complex or emotionally sensitive cases always reach a human.

Common tasks AI agents handle:

• Order tracking, returns, and refund processing
• Account questions, password resets, and billing inquiries
• Product troubleshooting and FAQ resolution
• Routing and triaging tickets before a human agent steps in

Platforms like Crescendo are built specifically for this, handling integrations with your existing tools (CRM, order management, ticketing) and maintaining human oversight so the AI operates within boundaries you define. The Klarna case shows both the upside and the limits: AI can handle enormous volume efficiently, but hybrid models consistently outperform fully automated ones for customer satisfaction.

2. Sales and CRM

AI agents can work alongside your sales team to handle the time-consuming parts of the pipeline, freeing reps to focus on closing. They don't need reminders, they don't lose track of leads, and they work around the clock.

Common tasks AI agents handle:

• Monitoring CRM for leads going cold and triggering follow-ups
• Drafting personalized outreach emails based on account history
• Summarizing account context before a sales call
• Flagging upsell and cross-sell opportunities based on usage data
• Logging call notes and updating CRM records automatically

JPMorgan's wealth management agents helped advisors respond to client inquiries during market volatility with personalized, portfolio-specific messages, contributing to a 20% increase in gross sales. For smaller businesses, Salesforce Agentforce and HubSpot are both building agentic capabilities that connect directly to your CRM data without a large technical team.

3. HR and Recruiting

HR teams are stretched thin, and much of their work involves repetitive, high-volume tasks that follow predictable patterns. Agentic AI is well suited to this because recruiting and HR workflows are structured, document-heavy, and largely rule-based at the early stages.

Common tasks AI agents handle:

• Sourcing candidates and ranking applications against job criteria
• Drafting job descriptions and personalizing outreach messages
• Scheduling interviews and sending follow-up communications
• Answering employee questions about benefits, policies, and onboarding
• Processing expense claims and leave requests

As LinkedIn's Hiring Assistant demonstrates, agentic AI can handle significant portions of the recruiting workflow autonomously. Beyond recruiting, Equinix, using Moveworks, achieved 68% deflection of employee IT and HR requests, meaning more than two-thirds of internal questions were resolved without a ticket ever being created.

4. Finance and Compliance

Agentic AI is particularly powerful in finance because it can monitor data continuously rather than periodically, catching issues the moment they appear rather than in the next reporting cycle.

Common tasks AI agents handle:

• Automated invoice processing and three-way matching
• Expense report review and anomaly flagging
• Running Know Your Customer (KYC) and Anti-Money Laundering (AML) checks
• Real-time monitoring for regulatory compliance violations
• Generating financial summaries and audit trail documentation

Mastercard's Agent Pay framework uses AI agents to track transactions and bind credentials to their origin in real time, targeting a $750 million fraud problem. At the IRS, Agentforce automated up to 98% of manual activities in the Office of Chief Counsel, cutting the time to open a tax court case from 10 days to 30 minutes.

5. Marketing

Marketing teams deal with high content volumes, fast-moving campaign data, and the constant pressure to personalize at scale. Agentic AI can handle the research, drafting, and analysis layer so teams spend more time on strategy and creative decisions.

Common tasks AI agents handle:

• Competitive research and market trend monitoring
• Drafting blog posts, ad copy, email campaigns, and social content
• Personalizing email sequences based on user behavior
• Pulling weekly performance reports and flagging what changed and why
• A/B testing suggestions based on past campaign data
• Maintaining brand tone consistency across high-volume content

An agent can pull the latest data from your analytics platform every week, compare it to the prior period, and surface a plain-English summary for your team, without anyone manually running a report.

6. IT and Software Development

Coding agents are one of the most validated agentic AI use cases today, with documented results from some of the world's largest technology organizations. For most engineering teams, a significant portion of the weekly workload involves repetitive, well-defined tasks that are a natural fit for an autonomous agent.

Common tasks AI agents handle:

• Writing boilerplate code and implementing features from tickets
• Running code reviews and flagging security vulnerabilities
• Writing and extending unit tests
• Refactoring legacy code and upgrading dependencies
• Generating documentation and updating READMEs
• Monitoring production systems and summarizing incidents

Amazon saved the equivalent of 4,500 developer-years of work by using Q Developer agents to migrate 30,000 applications to a newer Java version. Carvana and EY both reported significant velocity gains after deploying GitHub Copilot's coding agent. For most teams, tools like GitHub Copilot, Cursor, and Claude Code can deliver a productivity multiplier without rebuilding your development workflow.

7. Supply Chain and Operations

Agentic AI can monitor, flag, and respond to supply chain signals in real time, something that traditional dashboards and periodic reporting cannot match. For operations teams managing suppliers, inventory, and logistics, this translates directly into fewer disruptions and faster responses.

Common tasks AI agents handle:

• Monitoring supplier performance and flagging delivery risks early
• Tracking inventory levels and triggering restocking orders
• Identifying shipment delays and recommending rerouting options
• Analyzing demand forecasts and adjusting procurement plans
• Coordinating across warehouses, carriers, and ERP systems

John Deere's See and Spray technology is an extreme example, using computer vision agents in the field to reduce herbicide use by 70%. But the same logic applies in logistics and warehousing: agents that observe, decide, and act faster than any human review cycle.

8. Healthcare Administration

Clinicians spend nearly half their working week on administrative tasks rather than patient care. Agentic AI is being deployed to close that gap, handling the documentation and coordination work so clinical staff can focus on what only they can do.

Common tasks AI agents handle:

• Drafting clinical notes and after-visit summaries from ambient recordings
• Processing prior authorization requests with insurers
• Scheduling follow-up appointments and sending patient reminders
• Monitoring patient vitals and flagging early warning signs to care teams
• Managing billing codes and insurance claim submissions

Duke University Hospital's Sepsis Watch demonstrates what agentic AI can achieve in a clinical setting: continuously monitoring every patient and predicting sepsis onset a median of 5 hours before clinical presentation, contributing to a 27% reduction in sepsis-related deaths.

9. Legal and Compliance

Legal teams in most organizations are understaffed relative to their workload, and much of that workload involves reading, summarizing, and cross-referencing documents. This is exactly the kind of high-volume, structured work that agentic AI handles well.

Common tasks AI agents handle:

• Reviewing contracts for non-standard clauses and missing provisions
• Summarizing lengthy regulatory filings and legal documents
• Monitoring regulatory changes and flagging impact on existing policies
• Drafting NDAs, service agreements, and standard legal templates
• Tracking compliance deadlines and sending internal alerts
• Conducting due diligence research across large document sets

Law firms and in-house legal teams are using AI agents to cut the time spent on document review from days to hours, allowing lawyers to focus on judgment-intensive work rather than reading through stacks of contracts.

10. Procurement

Procurement is a process-heavy function with predictable workflows, large volumes of supplier data, and clear approval chains. This makes it well suited for agentic AI, which can manage the routine stages of the procurement cycle while flagging exceptions for human review.

Common tasks AI agents handle:

• Researching and comparing suppliers based on price, quality, and lead time
• Drafting RFPs and purchase orders from templates
• Monitoring supplier contracts for renewal dates and SLA compliance
• Flagging maverick spending and policy violations in real time
• Tracking delivery milestones and chasing outstanding orders

By automating the routine stages of procurement, teams can redirect their attention to strategic supplier relationships and negotiation, the areas where human judgment genuinely adds value.

11. Customer Onboarding

First impressions matter, and onboarding is often where companies lose customers they spent money to acquire. Agentic AI can make onboarding faster, more consistent, and more personalized without requiring a dedicated team to manage each new account manually.

Common tasks AI agents handle:

• Guiding new users through product setup step by step
• Sending personalized onboarding sequences based on user behavior
• Answering product questions in real time without a support ticket
• Flagging accounts that are stuck or going dark during the onboarding window
• Triggering check-in calls or demos when a user hits a friction point

This is especially valuable for SaaS companies with high onboarding volumes, where a small difference in time-to-value translates directly into retention and expansion revenue.

12. Cybersecurity

Security teams are overwhelmed by alert volumes, and the cost of a slow response to a real threat is high. Agentic AI can monitor, triage, and respond to security events faster than any human team, while escalating only the alerts that genuinely need human attention.

Common tasks AI agents handle:

• Continuously monitoring network traffic and endpoint behavior for anomalies
• Triaging security alerts and filtering out false positives
• Automatically isolating compromised endpoints or accounts
• Running vulnerability scans and prioritizing remediation by risk level
• Generating incident reports and post-mortem documentation
• Monitoring for data exposure and insider threat signals

Traditional rule-based detection tools are reactive. Agentic security systems are proactive: they learn from previous incidents, recognize intent patterns, and can block threats before they escalate, all without waiting for a human to notice an alert in a queue.

13. Real Estate and Property Management

Real estate involves significant administrative overhead: inquiries, viewings, lease renewals, maintenance requests, and tenant communications. Agentic AI can handle much of this at scale, freeing agents and property managers to focus on relationships and negotiations.

Common tasks AI agents handle:

• Responding to property inquiries and qualifying leads automatically
• Scheduling property viewings and coordinating with agents
• Drafting and sending lease renewal communications
• Logging and routing maintenance requests to the right contractor
• Monitoring rent payment schedules and sending reminders
• Generating property performance reports for landlords

Capital One's Chat Concierge, designed for auto dealerships, illustrates the broader pattern: an AI agent handling the high-volume inquiry and scheduling work that previously required human staff, converting 55% more customer engagement in the process.

A Note on Getting Started

The companies seeing the best results aren't the ones that deployed AI everywhere at once. They started with one high-volume, well-defined problem, usually customer support or an internal workflow, measured the results carefully, and expanded from there.

A few principles that consistently show up in the successful deployments above:

• Start with a clear boundary. Define exactly what the agent is responsible for and what should always go to a human. Klarna's costly lesson was removing that boundary too aggressively.
• Choose the right model for the job. Claude tends to excel at nuanced customer interactions and long-horizon tasks. GPT-5 is strong for structured analytical work. Gemini handles multimodal tasks well. Open-source options like DeepSeek are worth considering if data privacy or cost is a constraint.
• Use platforms, not just models. Building an agent from scratch requires engineering resources. Platforms like Crescendo, Salesforce Agentforce, and Amazon Bedrock let you focus on the business problem rather than the infrastructure.
• Keep humans in the loop, at least at first. The most effective agentic systems today are hybrid ones. Human oversight isn't a limitation; it's what makes the system trustworthy enough to scale.
• Agentic AI is no longer a future technology. It's running in production at some of the world's largest companies, and it's increasingly accessible to businesses of every size. The question is no longer whether to use it; it's where to start.