Real-world Use Cases For Agentic Ai

While genAI chatbots are mostly deployed arsenic assistants aliases copilots, agentic AI allows companies to automate full business processes and fundamentally toggle shape operations. Here's really they're doing it.

Remember those elemental days of yore, erstwhile generative AI meant sending a mobility to an AI exemplary and getting an reply successful return? You mightiness adhd successful a vector database to supply immoderate discourse for nan mobility and immoderate guardrails for information and security. That sounded difficult astatine nan time, but successful retrospect it was a locomotion successful nan park.

Today, nan trending exertion is agentic AI systems. Instead of a chatbot, a vector database, and a guardrail, you now person an endless action of datasets, ample and mini models of each kinds moving successful each imaginable locations, and alternatively of a elemental prompt-response relationship pinch a quality connected 1 extremity and an LLM connected nan other, there’s an service of agents connected by a analyzable — and dynamically evolving — logical workflow. Or probabilistic workflow, arsenic nan lawsuit whitethorn be.

There are caller protocols connecting information and agents, caller protocols connecting agents to different agents, and orchestration frameworks to concatenation it each together.

With each this complexity, you mightiness deliberation that companies would beryllium slow to adopt agentic AI. You’d beryllium very wrong.

In a Cloudera survey of 1,500 endeavor IT leaders successful 14 countries released successful mid-April, 57% of respondents opportunity they’ve already implemented AI agents, and 96% opportunity that they scheme to grow their usage of AI agents successful nan adjacent 12 months.

[ Agentic AI’s effect connected nan enterprise: ongoing coverage ]

Other surveys show akin results.

According to a SnapLogic survey of complete 1,000 IT decision-makers successful nan US, UK, Germany, and Australia released successful February, 50% are utilizing AI agents. In addition, 92% of respondents are assured that AI agents will present meaningful business outcomes successful nan adjacent 12 to 18 months, and 79% are readying to put complete $1 cardinal successful AI agents complete nan adjacent year.

According to Gartner, agentic AI is nan apical strategical inclination of 2025. By 2029, 80% of communal customer services issues will beryllium resolved autonomously, without quality intervention. The patient besides predicts that 33% of endeavor package applications will see agentic AI by 2028, and 15% of each day-to-day activity decisions will beryllium made autonomously.

“It’s surely not conscionable trading hype,” says Gartner expert Sid Nag. “It is thing that’s going to beryllium of very precocious value for automating galore tasks successful galore environments.”

What is an AI agent, really?

There is simply a spot of “agent washing” happening successful trading departments correct now. Just as, complete nan past 3 years, companies person added nan “AI” explanation to each application, truthful now everything pinch a chatbot anyplace adjacent it is being branded an agent.

But successful general, nan measurement that exertion leaders differentiate an AI supplier from a chatbot is that nan supplier tin return autonomous action.

No longer constricted to answering questions, AI agents tin transportation retired tasks connected our behalf — sometimes highly analyzable tasks that require extended interactions pinch different agents and systems.

Here’s really enterprises are putting AI agents to usage today.

Software engineering pinch agentic AI

Software improvement was 1 of nan breakout usage cases for generative AI — and is besides a apical usage lawsuit for agentic systems.

A GitHub survey of 2,000 developers successful nan Brazil, Germany, India, and nan US recovered that 97% were utilizing AI coding devices by mid-2024. And according to a HackerRank survey of much than 13,000 developers crossed 102 countries released successful March, AI now generates, connected average, 29% of each code.

There’s a wealthiness of nationalist codification bases connected which models tin beryllium trained. And larger companies typically person their ain codification repositories, pinch elaborate alteration logs, bug fixes, and different accusation that tin beryllium utilized to train aliases fine-tune an AI strategy connected a company’s soul coding methods.

As AI exemplary discourse windows get larger, these devices tin look done much and much codification astatine erstwhile to place problems aliases propose fixes. And nan usefulness of AI coding devices is only expanding arsenic developers adopt agentic AI. According to Gartner, AI agents alteration developers to afloat automate and offload much tasks, transforming really package improvement is done — a alteration that will unit 80% of nan engineering workforce to upskill by 2027.

Today, location are respective very celebrated agentic AI systems and coding assistants built correct into integrated improvement environments, arsenic good arsenic respective startups trying to break into nan marketplace pinch an AI attraction retired of nan gate.

The astir celebrated agentic coding platforms coming see Devin from Cognition Labs, Cursor, and Windsurf. There’s besides a free, open-source option, Cline.

OpenAI is expected to merchandise its ain agentic package technologist level soon, A-SWE, which stands for agentic package engineer.

Established players are getting into nan crippled arsenic well. GitHub Copilot announced an agentic mode successful February. Amazon announced an enhanced CLI agent for its Q Developer level successful March. VS Code rolled retired an agentic mode successful April. Google besides has an agentic AI improvement platform, Firebase Studio, that nan institution announced successful April.

Agentic AI codification improvement platforms are a important beforehand complete chatbot-based codification assistants. With a chatbot, a developer asks a mobility and gets a codification snippet. But an agentic AI level tin scheme an full project, constitute nan components, create tests and cheque that nan codification works, and iterate until it meets each nan task objectives.

At cybersecurity patient Abnormal AI, betwixt half and three-quarters of nan company’s 350 engineers are presently utilizing these tools, says Dan Shiebler, nan company’s caput of instrumentality learning.

“We’re making very important investments successful making our engineers much effective,” he says. The institution is presently utilizing Cursor and is experimenting pinch different platforms. “And location are a number of things built internally.”

Not each usage lawsuit requires a afloat agentic system, he notes. For example, nan institution uses ChatGPT and reasoning models for architecture and design. “I’m consistently impressed by these models,” Shiebler says.

For package development, however, utilizing ChatGPT aliases Claude and cutting-and-pasting nan codification is an inefficient option, he says.

“The adjacent measurement up is nan Cursor type of interface, wherever you person a container wherever you show it what to do, and nan supplier responding to you has discourse of nan codification and tin make changes based connected nan instructions you springiness it, and you tin reappraisal it.”

But nan latest improvement is wherever nan coding strategy tin make an full exertion without a quality rubbing nan codification astatine all. It tin usage APIs and proviso infrastructure — and location are respective areas wherever Abnormal is already utilizing specified tools.

“Bolt, v0, and Lovable are 3 devices successful this category,” Shiebler says. “I personally for illustration Lovable, but we’ve seen a batch of occurrence pinch v0 for interface design, wherever it’s taken nan spot of Figma successful a batch of personification workflows.”

Any institution that’s superior astir processing exertion needs to beryllium utilizing agentic AI package improvement tools, says Kevin Merlini, VP of AI astatine Thomson Reuters. “If they’re not, I don’t cognize why they’re not doing that,” he says.

Thomson Reuters’ package engineers usage various AI-powered coding tools. “We person a multi-model attack truthful we’re not locked in,” he says. “And, broadly, we person a multi-vendor approach.”

Being elastic allows companies to beryllium capable to thrust nan activity of innovations that’s happening now, he says. “Everyone should beryllium employing multi-prong strategies, exploring products, and trying to understand it themselves.”

AI agents for investigation and archive analysis

Thomson Reuters isn’t conscionable using agentic AI internally for things for illustration package improvement and research. It’s besides building agents into its customer-facing offerings.

Specifically, nan institution has created nan CoCounsel genAI adjunct for legal, tax, audit, and accounting professionals. More than 240,000 customers now usage CoCounsel, pinch nan top usage related to ineligible investigation and archive study skills.

“Agentic exertion is supercharging nan measurement we tin present worth for customers,” says Merlini. “I look astatine it arsenic a caller class of software.” It goes acold beyond what tin beryllium accomplished pinch a elemental chatbot interface, he says.

“With a basal chatbot utilizing RAG and 1 files of files, you’re getting a punctual and giving an answer,” he says. “There’s not excessively overmuch autonomy. But what if you person dozens of different repositories? How does it cognize which repositories to access? What if you person aggregate devices and capabilities, taking actions successful immoderate systems, pulling information from an API?”

Even a straightforward task for illustration investigation tin use from an supplier approach, he says. “It seems elemental connected nan surface,” he says. “But what if personification has a mobility that requires aggregate steps, and nan reply isn’t conscionable successful 1 source?”

AI is successful a feedback loop correct now, he says. “All these building blocks are coming together, giving nan strategy much capabilities and much devices that it tin use,” he says. “It’s opening up much usage cases. And it’s decidedly nan guidance we’re going.”

Agentic AI for customer service

Customer support chatbots tin reply elemental questions. AI agents tin tackle much analyzable challenges — and tin moreover enactment to lick problems.

There’s a batch of consequence here. It’s bad capable if a chatbot gives a customer incorrect information aliases promises a discount that nan institution can’t deliver. But what if nan AI tin enactment autonomously, tin spot aliases cancel orders aliases tin springiness discounts and refunds?

That’s why, for its first deployment, Bosch Power Tools is utilizing agentic AI to assistance quality agents, not switch them — and is keeping humans successful nan loop arsenic a information precaution.

“The users will beryllium our agents,” says Victor Nguyen, nan company’s task lead for genAI successful business operations. End customers won’t beryllium exposed to nan caller agentic AI systems directly. “We’ll person autonomous AI agents supporting our quality agents.”

Bosch is utilizing Cognigy.AI arsenic its AI platform, which supports integration pinch aggregate back-end AI models. “At nan infinitesimal we’re utilizing [OpenAI’s] GPT 4.0 and [Google’s] Gemini,” says Nguyen. “We’re rather flexible.”

 It’s besides integrated pinch nan company’s CRM strategy and ticketing system. “We person besides integrated it pinch a translator service, truthful we tin construe email matter aliases archive attachments,” Nguyen says.

The strategy is presently successful nan 2nd aviator phase, he says, and will beryllium utilized by unrecorded quality agents for existent cases starting successful May. In June, it will beryllium deployed to nan first customer work center, retired of 23 astatine nan company.

The eventual extremity is to person nan level beryllium wide utilized crossed nan company, he says. “Bosch is specified a immense company; Power Tools is conscionable 1 division,” he says. “We will subordinate forces pinch different Bosch groups to make it a scalable solution. We’re intimately collaborating pinch our cardinal IT to make judge this is globally scalable.”

The biggest challenge, he says, isn’t nan agentic exertion but nan deficiency of company-wide standardized processes.

“In Germany, say, location mightiness beryllium a different process for changing an bid than if personification successful Latin America was doing it,” he says. “And location are different systems being used. We reviewed screens and made judge we standardized them arsenic overmuch arsenic possible, though location will ever beryllium immoderate country-specific stuff.”

Nguyen recommends that companies looking to rotation retired agentic AI for customer work commencement standardizing information and systems arsenic soon arsenic possible.

“Most group deliberation that AI is nan solution, that AI will hole everything,” he says. “That’s not nan case. The first homework to do is to get nan bully data, bully value data, and make judge it’s maintained. It’s not conscionable a one-time task to upload nan information somewhere.”

AI agents for archive processing

Enterprises person been utilizing chatbots to process documents for years. Generative AI is bully at, say, summarizing, aliases pulling retired circumstantial information.

But pinch agentic AI, an full document-focused workflow tin beryllium automated.

Marketing patient Route Three Digital precocious built an AI supplier for a customer utilizing Google’s Vertex level and Gemini genAI models to automate a process that utilized to return 7 days arsenic nan client’s users collected documents and accusation to create a proposal.

“We wrote nan codification and scripted it to seizure each nan pivotal accusation into 1 maestro document, past usage Gemini to cleanable up nan matter and make it much readable,” says Sharmilla Singh, nan company’s main trading and operations officer.

It’s still not wholly foolproof, she says, and location is still a quality progressive to reappraisal nan last archive and tailor it arsenic needed. But erstwhile nan instrumentality launched past year, nan customer saw a multi-day workflow reduced to a fewer hours.

The adjacent step, she says, is to person an AI supplier that does everything. “The extremity is to region nan human,” she says.

Marketing is simply a comparatively low-risk usage lawsuit for agentic systems, Singh says. “It’s not going to return down your company.”

Other usage cases for AI successful trading see hunt motor trading and online advertising. “If you don’t enactment connected apical of caller methodologies, you could suffer marketplace share,” she says.

Agent democratization

Google’s Vertex AI is conscionable 1 of galore AI supplier building platforms that’s trying to make it easier to build and deploy AI agents. In April, Google besides announced that its Agentspace platform, first launched successful December, now has a no-code supplier designer and pre-built agents for tasks for illustration heavy investigation and thought generation.

Google has besides launched an agent marketplace and opened it up to partners. As of this writing, location are 138 agents offered connected nan platform, from companies for illustration Deloitte, VMware, Amdocs, Palo Alto, Wipro, and Dun & Bradstreet.

But Google is conscionable starting to drawback up to nan 800-pound gorilla that is Microsoft’s Copilot Studio. It has already been utilized by much than 160,000 organizations to build agents, said Charles Lamanna, Microsoft’s Corporate VP of Business and Industry Copilot, successful a March update. More than 400,000 civilization AI agents person been created successful nan erstwhile 4th alone, he added.

Other companies offering AI agents see AWS, pinch its Bedrock Agents, arsenic good arsenic Salesforce, ServiceNow, Workday, and SAP.

What’s more, AI exemplary makers are opening to cook agentic capabilities into their halfway products. OpenAI, for example, just announced 2 caller reasoning models pinch agentic AI functionality and instrumentality usage built correct in. In nan future, businesses whitethorn not moreover request third-party agents aliases agentic platforms.

But agentic AI exertion is still truthful caller that “it’s a small excessively early to get immoderate existent feedback from enterprises” astir their experiences pinch it, says Gartner’s Nag. “I don’t deliberation it’s fresh for premier clip yet, aliases moreover if it’s fresh for premier time, it’s not thing that group are adopting wholesale.”

And there’s still a batch of patient skepticism astir nan technology, he says. “I deliberation that will beryllium mitigated complete clip and you’ll spot it go much pervasive successful various functions — IT operations, sourcing, procurement, finance, and a full bunch of different things.”

“It’s not location yet,” he adds. “But it’s becoming a small spot much real.”