10 Agentic AI Examples & Use Cases In 2025

Agentic AI use cases in 2025 are powering real outcomes across marketing, sales, and revenue teams every single day.

From launching entire outbound sequences to managing deal follow-ups without hand-holding, agentic AI isn’t just saving time - it’s making moves.

Unlike traditional automation, agentic AI doesn’t wait for instructions. It acts. It reasons. It adapts in real time.

These are self-directed agents that take high-level goals (like “increase demo conversions” or “revive stale leads”) and execute multi-step workflows to make them happen with minimal human input.

And while some teams are still meddling with manual hacks, the most forward-thinking companies are already deploying AI agents to do what used to take entire departments.

In this article, we’ll explore 10 powerful agentic AI examples and use cases and show how modern revenue teams are using them to scale smarter.

Let’s dive in!

What are the different types of AI agents?

AI agents aren’t one-size-fits-all - they exist on a spectrum of complexity and autonomy.

Understanding this landscape is crucial to unlock the full power of agentic AI use cases in your go-to-market motion.

Let’s walk through the main types of AI agents, starting with the basics and ending with the truly game-changing.

1. Reactive agents (aka reflex agents)

This is the most basic type of AI agent.

They don’t remember past actions or adapt; they simply react to stimuli based on predefined rules.

Example: A chatbot that always replies “Let me check that for you” when someone asks about pricing.

Useful for: Straightforward and repetitive responses.

Limitation: No context, no memory, no learning.

2. Rule-based agents (logic-driven automations)

These follow structured “if-this-then-that” logic trees.

Slightly more intelligent than reactive agents, they can handle branching flows and decision trees.

Example: An email sequence that routes leads to different reps based on job title and region.

Useful for: Predictable processes like routing, lead scoring, or form-based workflows.

Limitation: Can’t adapt beyond what’s been manually programmed.

3. Learning agents

These agents take it a step further by incorporating machine learning to improve over time.

They use feedback loops to optimize outcomes, like identifying which messages or timing get the best response rates.

Example: An AI model that adjusts email subject lines based on open rates across segments.

Useful for: Content optimization, personalization at scale, and pattern recognition.

Limitation: Still requires humans to define goals, train models, and oversee results.

4. Goal-based agents

These agents can make decisions by evaluating actions against a specific desired outcome.

They simulate different possibilities and pick the one that best moves the needle toward the goal.

Example: An agent that selects the best follow-up channel (email vs. LinkedIn) based on lead engagement patterns.

Useful for: Scenarios with multiple valid options where trade-offs must be considered.

Limitation: Not yet fully autonomous, still operates under a defined goal framework.

5. Agentic AI agents (the future - and the now)

Agentic agents are fully autonomous systems that go beyond execution.

They plan, act, monitor, and adjust in real time, often across multiple tools and steps.

They take a high-level objective like “book more meetings from warm leads” and independently decide how to get it done, whether that means launching a LinkedIn DM sequence, escalating an inbound hand-raiser, or tweaking the nurture flow.

Example: Warmly’s AI SDR that notices a prospect viewed your calendar twice but didn’t book, automatically sends a personalized reminder via email, then follows up with a LinkedIn DM 24 hours later if they still haven’t engaged.

Useful for: End-to-end workflow orchestration, outbound sequencing, lead revival, post-demo follow-ups, and more.

Superpower: They take initiative. They adjust. They self-correct. And they act as true teammates, not just tools.

10 agentic AI examples in 2025 per use case

Agentic AI is no longer just for early adopters.

From logistics to recruiting, support to sales, agents are now working behind the scenes by planning, adapting, and executing multi-step workflows without constant human oversight.

Here’s how different industries are putting agentic AI to work in the real world.

1. Sales prospecting and lead activation

Industry: B2B Sales / GTM / SaaS

Agentic AI is redefining how sales teams handle prospecting in 2025.

The most forward-thinking companies aren’t just automating tasks but deploying fully autonomous AI SDRs that act like always-on teammates.

Instead of handing sales reps a static list of leads and hoping they find time to follow up, agentic SDRs proactively engage, qualify, and activate prospects across channels with minimal human involvement.

These agents can monitor signals (like site visits, job changes, and social activity), personalize outreach based on intent data, and orchestrate multi-touch follow-up across email, LinkedIn, and live chat.

When the time’s right, they escalate to a rep or book the meeting.

Some of the key benefits include:

24/7 coverage and responsiveness.
Infinite outbound capacity.
Human-grade personalization at scale.
Integrated multichannel sequencing.
Direct meeting booking with no rep involvement.

Warmly’s SDR agents are the perfect example of AI agents built from the ground up to scale sales development without ballooning headcount.

Here’s how they work:

AI-powered outbound - Warmly’s agent autonomously handles outbound prospecting across a limitless number of leads by researching, prioritizing, and personalizing outreach so your human reps can focus on high-value conversations.
Automated lead nurturing - AI SDR follows up with smart sequences across email and LinkedIn, re-engaging leads who’ve gone quiet, keeping every deal moving through the funnel without the team lifting a finger.
Conversational AI for booking - Warmly also deploys an AI chatbot that lives on your website 24/7, engaging visitors in real time with dynamic, context-aware conversation. It adapts to each visitor’s intent and helps book meetings directly, before competitors get the chance.

2. Marketing campaign orchestration

Industry: Marketing / B2B SaaS / GTM Ops

Today, marketers are finally off the hamster wheel of building campaigns from scratch, exporting CSVs, uploading audiences, and manually stitching together ads, emails, and retargeting flows.

Agentic AI has changed the game by orchestrating multi-channel campaigns end-to-end.

These agents can do anything from identifying the ideal audience using dynamic data and launching personalized campaigns across platforms to tracking real-time signals and automatically shifting budget or creative based on performance.

They don’t just launch campaigns. They manage them like a strategist would: constantly optimizing for intent, timing, and ROI.

Warmly, for example, brings this agentic orchestration to life with two powerful agents designed to supercharge marketing execution and scale without headcount:

Marketing Ops agent handles campaign planning, segmentation, routing, and follow-up across your stack. Here’s how it works:

AI-powered ICP identification - Warmly uses AI to uncover the true behavioral, contextual, and demographic traits of your best customers, then continuously finds new leads that match your ideal profile.
Real-time data & signal monitoring - The agent pulls in signals from 10+ data providers, such as website activity, firmographic changes, job shifts, and more, to ensure every campaign reaches the right audience at the right time.
Lead routing & notifications - When a lead reaches a threshold of buying intent, the agent sends the right Slack alert, routes to the right rep, and triggers the right action (campaign, outreach, or offer) in real time.

2. Warmly’s Demand Gen agent targets leads showing warm signals on and off your site and syncs those segments directly into your ad platforms. Here’s what it does:

Signal-based ad targeting - Warmly uses dozens of real-time signals to build high-conversion lead segments and push them straight to Meta, LinkedIn, and Google for precise targeting.
Automatic follow-up based on buyer readiness - Warmly’s agent sends each lead into the proper flow, whether a targeted ad, an email sequence, a chatbot offer, or a human outreach, based on how close they are to conversion.
Warm offers - Each lead is presented with a personalized offer tailored to their behavior, past interactions, customer journey stage, etc.

3. Software development automation

Industry: Software / Engineering / DevOps

Agentic AI is rapidly transforming software development from a manual, linear process into a fluid, self-directed workflow.

These agents don’t simply autocomplete functions. They understand the problem, outline a multi-step solution, write the code, debug errors, and even submit pull requests.

They're goal-driven, not task-bound.

Take Devin by Cognition, for example.

Devin is widely considered the world’s first fully agentic AI software engineer.

It takes high-level engineering tasks, like "build a web app with user authentication", and executes them independently like this:

It breaks down requirements into subtasks.
Writes clean, modular code across front- and back-end.
Sets up dev environments, runs tests, and debugs on the fly.
Collaborates via GitHub to track progress.
Learns from feedback and iterates

This goes far beyond Copilot-style code suggestions.

Devin truly acts like a junior developer capable of owning and shipping scoped tasks end-to-end.

As a result, developer teams get:

Dramatic reduction in dev time for repetitive or well-scoped tasks.
More time for senior engineers to focus on architecture and innovation.
Faster prototyping and iteration cycles.
Enhanced productivity across levels.

4. Claims processing in insurance

Industry: Insurance / InsurTech / Risk management

Claims processing has long been one of the most resource-intensive and delay-prone areas in insurance.

Traditionally, it has involved human adjusters reviewing lengthy documentation, assessing eligibility, and manually coordinating payouts, which was a slow, costly process prone to errors and inconsistencies.

But in 2025, agentic AI is stepping in to handle this complexity at scale.

These agents don’t just extract data from claim forms.

They understand policy rules, assess damage using structured and unstructured data (including images and scanned PDFs), and autonomously manage the entire claims lifecycle from intake to payout.

As such, AI agents can tackle a wide range of tasks, from reading and extracting information from structured claims forms, emails, and third-party data sources to assessing evidence and detecting fraud red flags.

As a result, companies implementing AI agents in claims processing experience:

Faster claims resolution - Many straightforward cases are processed within minutes, not days.
Lower operational costs - Reduces the need for large back-office claims processing teams.
Consistency & accuracy - Eliminates human oversight errors and bias in evaluations.
Fraud detection - AI can cross-reference data across claims to flag anomalies.
Improved CX - Customers receive faster updates and less back-and-forth.

5. Retail and e-commerce optimization

Industry: Retail / E-commerce / DTC / Marketplaces

In today’s ultra-competitive digital retail landscape, success depends on making fast, personalized, and data-informed decisions across thousands (or millions) of SKUs, users, and touchpoints.

That’s where agentic AI steps in.

Most common use cases in retail include:

Personalized product recommendations - AI agents analyze individual shopper behavior, purchase history, session activity, and psychographic traits to dynamically display products most likely to convert, adapting in real-time.
Dynamic pricing optimization - Agents monitor competitor pricing, demand trends, and stock levels, and autonomously adjust product prices to maximize margin or market share.
Cart abandonment recovery - When a user leaves with items in their cart, an AI agent determines the best recovery strategy: a reminder email, a retargeted ad, a real-time SMS with a discount, or a chatbot nudge when they revisit the site.

The outcome?

Higher conversion rates, better inventory and ad budget allocation, optimized pricing, and less manual grind.

6. Healthcare administration

Industry: Healthcare / HealthTech / Clinics / Hospitals

Healthcare has long struggled with heavy administrative overhead, such as appointment scheduling, insurance claims, regulatory compliance, and patient communication.

These tasks are essential but time-consuming, often diverting attention away from patient care.

Agentic AI can help healthcare organizations streamline operations by handling these tasks autonomously.

Where traditional RPA or workflow automation stops short, agentic AI takes initiative, coordinating across multiple systems, monitoring signals, and driving outcomes without waiting for human input.

Some of the real-world applications of agentic AI in healthcare include:

Appointment scheduling and management - AI agents autonomously book, confirm, and reschedule appointments based on clinician availability, patient preferences, appointment urgency, and location.
Compliance and documentation support - AI agents generate and archive required regulatory documentation, including audit trails and HIPAA compliance logs, reducing manual oversight and risk.
Patient intake and digital onboarding - From collecting pre-visit forms to syncing data with the electronic health record (EHR), agentic AI streamlines intake, reduces front-desk workload, and improves patient experience.

By taking over time-consuming administrative tasks, agentic AI significantly reduces the operational burden on providers and staff, allowing clinical teams to dedicate more of their time and energy to patient care, rather than paperwork.

7. Customer support automation

Industry: SaaS / Ecommerce / Telecom / Financial Services

Customer support is one of the most natural fits for agentic AI.

Why?

Because support interactions are often time-sensitive, multi-step, and highly repetitive, yet require enough reasoning and personalization that traditional automation often falls short.

Here are some of the tasks AI customer support agents can tackle:

Contextual issue resolution - Agentic support agents understand the full context of a customer’s request, search across internal knowledge bases, past tickets, and third-party systems, then deliver tailored solutions, often before the customer even finishes explaining the problem.
Self-improving response libraries - Agentic systems track which replies lead to successful resolutions and learn from every interaction, adapting their recommendations and behavior across support tiers and customer segments.
Human-aware escalation - When a case exceeds their confidence level, agentic support agents don’t just pass the ticket upstream; they summarize the issue, attach all relevant context, and recommend the best course of action.

This way, with agentic AI handling routine and complex support tasks, response and resolution times drop significantly, even for nuanced issues.

This leads to more consistent, accurate service across every channel while lowering operational costs and freeing human agents to focus on higher-impact conversations requiring strategic thinking or empathy.

8. Human resources assistance

Industry: HR / Talent Acquisition / People Ops

HR teams today are expected to deliver seamless candidate experiences, manage complex onboarding processes, and support employee growth, often with limited staff and fragmented tools.

That’s where agentic AI saves the day, helping HR leaders scale these efforts with speed and precision.

Unlike legacy HR automation tools that rely on rigid workflows, agentic HR agents make autonomous decisions, adapt to each candidate or employee, and collaborate across departments, acting more like a strategic operations partner than a script executor.

This means they can independently handle a wide range of operations, such as:

Candidate screening and shortlisting - AI agents autonomously review resumes, cross-reference with job descriptions, assess candidate fit based on prior experience, certifications, or even soft skills (via writing samples or public data), and create ranked shortlists for hiring managers.
Interview scheduling and coordination - Once shortlisted, the agent reaches out to candidates, aligns interview times with panel availability, sends calendar invites, and follows up with reminders, minimizing recruiter back-and-forth.
Employee onboarding - After a hire is made, the agent guides the new employee through IT setup, policy training, document submission, and first-week scheduling, ensuring nothing is missed and the process is personalized by role and location.

By doing all this, agentic AI improves time-to-hire, reduces administrative HR workload, and enhances employee and candidate experiences through consistent, proactive communication.

As a result, HR teams can shift from reactive task handling to strategic people enablement.

9. Supply chain management

Industry: Logistics / Manufacturing / Retail Operations

Supply chains have always been complex, but agility and visibility are no longer optional in 2025 with ongoing global disruptions, labor shortages, and rising customer expectations.

Agentic AI is stepping in to autonomously manage supply chain workflows, respond to real-time data, and ensure resilience at scale.

These agents don’t just alert managers about problems - they solve them by analyzing delays, rebalancing inventory, optimizing delivery routes, and rerouting logistics operations on the fly.

They can take care of the following operations:

Demand forecasting - Agents analyze historical sales, seasonal trends, market signals, and external data (weather or news) to project future demand, then adjust procurement plans accordingly.
Dynamic logistics coordination - When shipping delays or route disruptions occur, the agent identifies alternative carriers, reroutes shipments, and updates ETAs across systems, reducing delays and avoiding manual intervention.
Supplier risk monitoring - By scanning supplier behavior, financial signals, and regional news, agents detect early warning signs of disruption, allowing businesses to diversify vendors or secure alternative sources before problems escalate.

The result?

Agentic AI reduces operational risk, shortens lead times, and increases supply chain responsiveness, freeing teams from reactive firefighting.

10. Academic research assistance

Industry: Higher Education / Scientific Research / Think Tanks / R&D

Academic research has always been a time-intensive process, including things like sifting through hundreds of papers, conducting literature reviews, compiling data, drafting manuscripts, and applying for funding.

Today, agentic AI is being deployed as a powerful co-pilot across every phase of the research lifecycle.

These agents don’t just summarize articles or generate text. They reason, plan, and take action on behalf of researchers.

For example, they can be used for:

Automated literature reviews - Agents scan thousands of peer-reviewed articles across platforms like PubMed, JSTOR, and Google Scholar, identifying recurring themes, highlighting conflicting findings, and summarizing the current state of knowledge, cutting down what once took weeks to a few hours.
Research gap identification - Beyond summarizing, these agents detect where gaps in evidence exist, suggesting original research questions or hypotheses based on what hasn’t been explored or sufficiently addressed.
Data analysis and visualization - Agentic tools ingest raw research data, run statistical models, check assumptions, and create relevant graphs or visualizations, automating a large portion of quantitative analysis.

Why is this so important for the academic community?

Well, agentic AI dramatically reduces the time needed for literature synthesis, improves the quality of insights by surfacing non-obvious patterns, and supports researchers in producing more rigorous, complete, and timely work.

It’s not a shortcut to thinking - the humans behind it still need to do the heavy lifting - but a partner in accelerating it.

Three real-life examples of agentic AI in action

We’ve explored how agentic AI is being applied across industries.

But what does this look like in practice?

In this section, we’ll dive into a few real-world examples of companies already using agentic AI to solve complex problems, automate multi-step workflows, and generate real business results.

These aren’t experiments - they’re live deployments showing just how powerful autonomous agents can be.

1. Connecteam scales outreach with an 11x’s AI SDR without hiring a single rep

As Connecteam expanded into new verticals like healthcare, retail, and construction, their sales team faced a growing challenge:

How do you scale personalized outreach without adding more SDRs?

Traditional outbound channels, such as email and SMS, weren’t cutting it.

Engagement was low, reactivating old leads was difficult, and their team was stretched thin managing 120,000+ monthly calls while still booking 20 meetings per week.

To break this cycle, Connecteam partnered with 11x to deploy Julian, an AI-powered SDR built to operate like a human phone rep - but at a scale no team could match.

Instead of spinning up generic automations, 11x embedded themselves in Connecteam’s GTM strategy.

They trained Julian on vertical-specific messaging, aligned the agent with live product updates, and designed workflows around real operational gaps, especially re-engaging closed-lost and low-intent leads their human reps couldn’t prioritize.

Julian didn’t just boost capacity - he completely transformed how Connecteam engaged leads.

From personalized, intent-driven follow-ups to real-time meeting confirmations, Julian helped cut no-show rates by 73%, reactivated thousands of dormant leads, and doubled call coverage, all without expanding the sales team.

The results include:

$450,000+ saved annually in SDR salaries.
120,000+ monthly phone calls handled autonomously.
73% drop in meeting no-shows.
$30K increase in monthly revenue per SDR.
20+ qualified meetings booked weekly—with a 40% conversion rate.

Warmly is proud to partner with 11x to bring this kind of agentic AI firepower into our own platform, helping sales teams run 24/7 outreach, re-engage pipeline, and scale SDR capacity without adding headcount.

2. Equinix uses an AI copilot to eliminate IT queues at global scale

As the world’s largest interconnection and colocation platform, Equinix runs a truly global operation with thousands of employees relying on fast, accurate IT support to stay productive.

But with a lean IT team of 400 supporting a distributed workforce across the US, UK, and Asia, the service desk was under constant strain.

The challenge? Scaling support without scaling headcount.

Tickets often sat unresolved due to misrouting, especially with hundreds of possible IT assignment groups and a help desk team located halfway across the world from most end users.

Response delays, resolution backlogs, and time zone mismatches all aggravated the problem.

Equinix needed more than a generic chatbot.

They needed an intelligent triage system that could understand requests, reason through them, and route them correctly the first time.

Enter Moveworks and E-Bot.

In 2019, Equinix launched E-Bot, a fully autonomous AI agent capable of resolving thousands of common IT issues end-to-end, right inside Microsoft Teams.

More importantly, it acted as a real-time triage co-pilot.

With 96% routing accuracy, E-Bot instantly assigned unresolved issues to the correct subject matter expert, matching or exceeding the performance of human agents, and doing it in under 30 seconds (versus the 5-hour average for L1 help desk queues).

This shift freed Equinix’s IT agents from tedious ticket triage and allowed them to focus on complex, high-impact work, all while dramatically improving service delivery speed and employee satisfaction.

The results:

96% routing accuracy to the correct IT expert group.
82% of tickets are now routed autonomously by E-Bot.
30-second average triage time (vs. 5 hours manually).
~33% reduction in ticket lifecycle time.
Millions in savings through reduced manual workload and improved resolution velocity.

3. Dutch insurer automates 91% of motor claims with Beam’s AI agent

For insurers, speed, accuracy, and customer satisfaction in claims processing aren’t just operational goals - they’re core to competitiveness.

But for one major Dutch insurance provider, processing high volumes of motor claims manually had become a bottleneck.

Each claim required adjusters to analyze coverage, liability, and documentation before a payout could be approved, significantly slowing resolution times, consuming costly human hours, and frustrating customers.

And with rising claim volumes and no easy way to scale personnel, the company needed a smarter solution.

The solution: AI-powered claims decisioning from Beam.

To tackle the problem, the insurer partnered with Beam to deploy a custom-built AI agent directly into their claims management workflow.

This wasn’t a bolt-on automation - it was a deeply integrated, decision-capable agent that mirrored the process a human claims adjuster would follow.

The agent handled three critical steps autonomously:

Intake and classification - The agent analyzed incoming claims to identify which were eligible for automation, based on structured criteria.
Automated assessment - It applied business rules and liability logic to determine whether the claim should be approved, denied, or escalated.
Decision and action - The agent executed low-risk approvals, rejected invalid claims, or forwarded complex cases to human adjusters, ensuring quality control without creating backlog.

The results:

91% of eligible motor claims were processed automatically.
46% reduction in average claim processing time.
9% improvement in Net Promoter Score (NPS), driven by faster resolutions and consistent outcomes.

Next steps: Implementing agentic AI in your workflows

As the real-world examples in this article show, agentic AI isn’t a future trend - it’s already transforming how modern teams operate.

From streamlining claims processing and eliminating IT ticket queues to driving pipeline and booking meetings at scale, AI agents are delivering measurable impact across every corner of the business.

The shift is clear: the most effective teams are no longer just automating tasks - they're deploying AI agents that think, adapt, and act autonomously to drive outcomes.

If you're also looking to scale your GTM without adding headcount, Warmly’s agentic AI platform is built for exactly that.

Our AI SDRs, marketing ops agents, AI Copilots, and demand gen workflows help you:

Prospect 24/7 based on real-time signals.
Re-engage leads and warm up pipeline automatically.
Orchestrate multichannel campaigns without manual lift.
Book more meetings, generate more pipeline, and move faster without burning out your team.

Ready to scale smarter? Book a demo with Warmly and see agentic AI in action.

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