Agentic AI Examples 2026: 11 Real Companies, Real Results

Agentic AI refers to AI systems that perceive their environment, set goals, plan sequences of actions, use external tools, and adjust based on the results, without waiting for a human to prompt each step. This article covers 11 named examples from companies already running agentic AI in production, across software, finance, logistics, and heavy industrial operations. Each example is drawn from public announcements or documented deployments.

What Agentic AI Is

Agentic AI is AI that acts, not just answers. A standard language model responds to a prompt and stops. An agentic system takes a goal, breaks it into steps, calls tools (APIs, databases, sensors, communication channels), evaluates what came back, and decides what to do next. The pipeline is consistent across deployments: perceive (collect inputs from connected systems), reason (interpret what the data means), plan (sequence the right actions), act (call tools and APIs), and reflect (check results and adapt). The reasoning engine is usually a large language model (Claude, GPT, Gemini), but the LLM is one component inside the agent, not the agent itself. For a longer treatment, read what agentic AI actually is, or see how agentic AI differs from generative AI at the architectural level.

How Agentic AI Differs from Traditional Automation

The shift is not “smarter scripts.” It is a change in how decisions get made and where humans sit in the loop.

Dimension	Traditional automation	Agentic AI
Trigger	A fixed rule or schedule fires a script	A goal or condition activates a reasoning loop
Decision-making	Deterministic: same input always produces same output	Reasoning-based: evaluates context and chooses the next action
Tool use	One API or one system per workflow	Orchestrates multiple tools, APIs, and data sources in sequence
Memory	Stateless: each run starts fresh	Persistent: retains context across steps and sessions
Off-pattern situations	Fails or escalates every exception	Adapts, re-plans, and routes only genuine exceptions to humans
Human oversight	Required at every decision point	By exception: humans review outcomes, not each step

11 Real-World Agentic AI Examples in Production

These eleven examples are running in production today. The first nine come from public announcements by the operating company or its primary technology partner. The last two are deployments by Opsima across marine terminals and port operations, where the agentic pattern meets the dark-data layer that traditional industrial software cannot reach.

1. Klarna AI assistant (customer service)

Klarna’s AI assistant handles end-to-end customer service for the buy-now-pay-later platform, covering refunds, returns, payment disputes, and account changes across 23 markets in over 35 languages. In its first month after launch, the agent had 2.3 million conversations, which Klarna describes as two-thirds of its customer service chats, and the company says it is doing the equivalent work of 700 full-time agents. The system pulls account history, evaluates policy, executes the appropriate financial action, and closes the case without human routing on standard issues. That is the agentic pattern in one of its purest forms: perceive customer intent, plan the resolution path, call the billing and CRM APIs, write back the result. Per Klarna’s press release (February 2024).

2. GitHub Copilot coding agent (software development)

GitHub introduced its Copilot coding agent in May 2025 to act on assigned issues end-to-end inside the platform. In GitHub’s own words, “the agent starts its work when you assign a GitHub issue to Copilot or prompt it in VS Code,” then “spins up a secure and fully customizable development environment powered by GitHub Actions.” From there it clones the repo, writes code, runs tests, and opens a draft pull request for human review. The agent works inside the same branch protection and required-review gates as a human engineer, which is what makes the workflow safe enough to roll out across enterprise codebases. The pattern is classic agentic execution: the issue is the goal, the codebase and CI are the environment, and the agent plans, acts, observes test failures, and iterates until it has something worth a reviewer’s attention. Per the GitHub blog (May 2025).

3. Uber Genie (internal engineering support)

Uber’s internal LLM-powered Slack bot, Genie, sits on-call for engineering teams answering questions about security and privacy policy. In a 2025 engineering blog post, Uber’s team described moving Genie from a traditional retrieval pipeline to an enhanced agentic RAG architecture and quantified the result directly: “increasing the percentage of acceptable answers by a relative 27% and reducing incorrect advice by a relative 60% through our transition from a traditional RAG architecture to an enhanced agentic RAG approach.” The agent does its own multi-step research: it expands the query, retrieves across multiple sources, reflects on whether the response covers the question, and re-queries until the answer clears the threshold. For an internal compliance bot, where wrong advice is worse than no advice, that re-planning loop is the difference between a tool engineers trust and one they bypass. Per Uber Engineering (May 2025).

4. Shopify Sidekick (merchant operations)

Shopify’s Sidekick is an AI assistant embedded in the merchant admin that lets store owners run their business through natural language. Per Shopify’s engineering blog, Sidekick has “evolved from a simple tool-calling system into a sophisticated agentic platform” that handles analyzing customer segments, filling product forms, writing SEO descriptions, and navigating complex admin interfaces, the kind of operations that previously required clicking through several screens or writing custom queries. The agentic shift mattered because merchant requests are open-ended (“show me my best month this year and write a campaign brief targeting those buyers”) rather than single-action. The system has to plan across multiple Shopify APIs and decide what to surface back to the merchant, all within a session that may span dozens of related sub-tasks. Per the Shopify engineering blog (August 2025).

5. OpenTable on Salesforce Agentforce (hospitality)

OpenTable deployed a Salesforce Agentforce-based agent to handle diner and restaurant inquiries. In October 2025, Salesforce reported that “OpenTable resolved 70% of diner and restaurant inquiries autonomously.” George Pokorny, OpenTable’s SVP of Global Customer Success, framed it as a step up from chatbots: “Within weeks, our restaurant agent handled 70% of questions, a marked improvement over our previous chatbot.” The agent reads the inbound request, queries the booking and restaurant database for context, determines the policy-correct response (rebook, cancel, recommend), executes the change, and only escalates ambiguous or sentiment-flagged conversations to a human. The 70% resolution rate is the operational threshold most enterprises chase for first-line support automation, and OpenTable hit it inside weeks, not quarters. Per the Salesforce press release (October 2025).

6. JPMorganChase LLM Suite (financial services)

JPMorganChase rolled out LLM Suite, an internal platform that gives employees secure access to large language models for drafting, idea generation, and (in the next phase) AI agents that execute multi-step workflows against the bank’s internal data. The bank’s June 2025 blog describes the platform going “from zero to 200,000 onboarded users within eight months,” driven by employee demand rather than a top-down mandate. That adoption velocity is what makes the next phase, agents that act on internal systems rather than just answer questions, an enterprise-scale rollout rather than a pilot. The deployment pattern is governed from the start: the agents operate inside the bank’s security boundary, against approved data scopes, with audit trails on every API call. Per the JPMorganChase technology blog (June 2025).

7. BNY Eliza (enterprise AI platform)

BNY (the bank formerly known as BNY Mellon) operates Eliza, an internal AI platform that “supports over 125 live use cases, with 20,000 employees actively building agents.” In OpenAI’s December 2025 case study on the deployment, BNY’s Chief Data and AI Officer characterized the scale of the change directly: “We’re not building side projects. We’re changing how the bank works.” Eliza covers everything from legal review to client research to operations, with governance and observability built into the platform layer so that individual agents inherit security and audit defaults. This is the enterprise pattern most banks are converging on: a governed platform that lets domain teams build their own agents without each one repeating the security and compliance work. Per OpenAI’s BNY case study (December 2025).

8. DHL Supply Chain on HappyRobot (logistics)

DHL Supply Chain deployed HappyRobot’s voice and email AI agents across regions and use cases including appointment scheduling, driver follow-up calls, and high-priority warehouse coordination. Per DHL’s November 2025 press release, the agents “autonomously handle phone and email interactions, enabling faster, more consistent, and scalable communication” across the operation. This is the closest of the third-party examples to industrial field operations. The agents work in the unstructured, voice-first, asynchronous communication layer that traditional logistics software cannot reach, scheduling, confirming, and chasing the kinds of conversations that previously happened by phone and never reached the TMS. Read more about agentic workflow automation in industrial IT. Per the DHL Group press release (November 2025).

9. Siemens Fuse EDA AI Agent (industrial design automation)

Siemens introduced Fuse EDA AI Agent in March 2026 for the semiconductor, 3D IC, and PCB design industries. The system, in Siemens’ own words, is “a purpose-built domain-scoped autonomous AI agent that plans and orchestrates multi-tool and multi-agent complex semiconductor, 3D IC and printed circuit board (PCB) system workflows that span across design, verification and manufacturing sign-off.” That phrasing matters: it is the explicit architectural choice that distinguishes a generalist chatbot from an industrial-grade agent, multi-tool orchestration across a defined domain with domain-specific reasoning. For Opsima’s audience, this is the closest analogue to industrial-scope agentic AI in the third-party set: domain-bounded, integrated with the existing engineering tool stack, and built for environments where wrong autonomous actions are expensive. Per the Siemens newsroom (March 2026).

Those patterns from software, finance, and consumer tech show the same underlying mechanic in each case: an agent that perceives a signal, plans a response, and acts without waiting for a human to queue it. The same mechanic is now running in marine terminals and industrial operations, against a different and harder set of inputs.

10. HCT (container terminal, Israel)

HCT (Hadarom Container Terminal, TIL-owned) deployed an agentic layer focused on unstructured-data ingestion at the terminal. The core problem: a significant share of operational status, equipment state, labor readiness, job completion signals, arrived through channels that never touched the terminal’s system of record. The agent captures and parses that unstructured input, classifies it, and writes it into the right operational context. Terminal coordinators now work from a live operational picture instead of chasing confirmations through radio and messaging. In production at HCT’s Israeli container terminal. See how this fits into live operations and status capture.

11. PNCT (Port Newark Container Terminal)

At Port Newark Container Terminal, 1.65 million TEU per year flows across a fleet of 100-plus straddle carriers running 24/7. Opsima’s EquipmentOS agent monitors equipment telemetry continuously, correlates signals across the fleet, and flags degradation patterns before they become breakdowns. Maintenance teams receive specific, actionable alerts rather than raw sensor data. The result: plus 5% fleet availability and approximately 15% fewer breakdowns. On a fleet that size, 5% availability means roughly 5 additional units online each shift, which translates into millions in throughput capacity without buying new equipment. See how this connects to predictive maintenance and automated KPIs.

Why Industrial Field Operations Leads

Industrial field operations is the primary driver of agentic AI deployment because the structural conditions making agentic AI necessary are most acute there. Two forces converge: dark data and IT backlogs.

The Dark Data Problem

Every hour, your yard, ramp, dock, or plant floor generates operational events. A straddle carrier breaks down. A shift crew discovers a safety issue. A truck arrives early. Maintenance completes ahead of schedule. Most of those events are communicated via radio, WhatsApp, text message, email, or handwritten log. They never enter a structured system. That is dark data, and it is the primary reason agentic AI exists in industrial operations.

No structured data means no decisions based on facts. Your operation runs on partial information: which crew is available, which machine is healthy, which shipment is at risk. Every decision made on incomplete data carries unnecessary risk and cost. Agentic AI solves this. An agent listening to radio traffic captures the event as it happens. It structures, categorizes, and acts on the information, all within seconds. The cost of building that capability the old way (custom IT development, system integrators) was $500K+ and 6 to 12 months. Agentic AI deployed inside a governed platform ships it in days, not quarters.

Why IT Backlogs Drive Agentic Adoption

Every industrial organization has a backlog of integrations, reports, forms, and change requests averaging 6 to 24 months deep. Operations leaders have endless ideas: “Let’s capture shift handovers digitally,” “Let’s automate PM scheduling,” “Let’s build a safety dashboard.” IT responds: “We’ll get to it next year.”

Agentic AI changes that equation. A business user describes the problem in plain language. An AI agent designs the solution and writes the code. It tests everything, on top of existing systems (SAP, Maximo, Navis, AS400, Priority). No migration, no replacement. IT reviews, assesses the risk, and approves. Then it is live. The backlog does not disappear, but the velocity does. You can deploy several solutions in the time a traditional system integrator delivers one. Learn more on agentic workflow automation for industrial IT and agentic AI in ports and terminals.

The Governance Gap

Agentic AI is only safe in production if it is governed from the start. Most articles on agentic AI skip this entirely, focusing on speed and capability. That omission is dangerous in enterprise environments.

Why Governance Determines Survival

Consider the data from organizations deploying agentic AI today:

Source	Key Finding
Gartner (June 2025)	40%+ of agentic AI projects will be canceled by end of 2027 due to escalating costs, unclear ROI, or inadequate risk controls
Capgemini	Only 2% of organizations have deployed AI agents at scale; 12% have partial scale; 23% in pilot phase
IBM EMEA (2025)	Two-thirds of surveyed EMEA enterprises report significant productivity gains from AI, but most lack governance structure
Uber Engineering	Agentic RAG improved acceptable answer rate by 27% and reduced incorrect advice by 60%

The cancellations are not happening because agentic AI does not work. They are happening because organizations deployed it without governance: no staging environment, no risk assessment, no IT approval workflow.

In a regulated industrial operation, that is not just a project failure. It is a compliance and security exposure. An agent connected to your ERP without risk review could trigger material unintended transactions. An agent capturing operational data without data classification could leak credentials or PII. An agent in your CMMS without an approval workflow could schedule maintenance in ways that violate safety policy.

Governed agentic AI (staging-first, risk-assessed, IT-approved) is not slower than ungoverned AI. It is the only kind that reaches production safely and stays there.

Governed vs Ungoverned: The Difference

Consumer vibe-coding tools (Lovable, base44, Bolt) deploy fast. They have no staging, no risk assessment, no approval workflow. They are built for individual developers or small teams prototyping side projects. In an enterprise operations environment with 100+ users, PII, equipment control, and regulatory requirements, they create governance nightmares.

“To get real value from agentic AI, organizations must focus on enterprise productivity, rather than just individual task augmentation. They can start by using AI agents when decisions are needed, automation for routine workflows and assistants for simple retrieval. It’s about driving business value through cost, quality, speed and scale.”

Anushree Verma, Senior Director Analyst, Gartner (Source)

Governed agentic AI enforces that discipline. Every solution goes through a staging environment first. A risk assessment team (security, compliance, operations) reviews the agent’s logic, data access, and decision rules before it touches production. IT approves and owns the deployment. The process takes days, not months, but nothing reaches production without review. Read more on enterprise AI governance for industrial IT and why consumer vibe-coding tools fail in enterprise.

How to Choose Your First Use Case

If you are considering agentic AI for your operation, start where the economics are most obvious and the risk is lowest. The same starting heuristic works across ports, mining, manufacturing, aviation, and field service.

Start where dark data is worst. Radio dispatch threads, WhatsApp shift logs, maintenance handovers on clipboards: these are high-signal workflows with clear decision criteria and measurable outcomes. An agent that captures your shift handover and surfaces pending items to the incoming crew eliminates a 10-minute manual huddle and reduces missed carryover items. That is easy to measure and easy to defend.

Evaluate three criteria. Does the workflow touch existing enterprise systems (SAP, Maximo, ERP)? Can the decisions be guided by clear operational policy (for example: “Schedule PM only if MTBF falls below 500 hours”)? Is there a measurable KPI baseline to beat (turnaround time, downtime reduction, crew availability)? If you answer yes to all three, it is an agentic AI candidate.

Start small and systematic. One workflow. One squad. Measure before and after. Prove the business case. Then scale horizontally to other workflows. That is how every durable agentic AI deployment happens.

If you want a sharper picture of the terminology before you scope a use case, the questions below cover the definitions, distinctions, and tool comparisons that come up most often.

Your Operation Already Has the Signal

Your operation already has the signal. It just is not connected. The agents that move the needle in industrial operations are not general-purpose tools. They are built for your specific systems, your data, and the decisions your team makes on the ground. Tell us the process you have given up on fixing. We build a working solution first. You pay only if it earns its place. Book a demo.