Agentic AI in the Wild: Real Use Cases Beyond The Hype
Agentic has become a buzzword, but there are really exciting things happening across industries such as finance, health, and cybersecurity.
It’s no secret that “Agentic” has been the AI buzzword of 2025. The problem is, most of the conversation still feels abstract. It's hard to know what’s real, what’s working, and what’s just noise.
In this piece, I want to cut through that. I’ll walk through examples of agentic AI that we find most interesting at Merantix Capital — from financial services to science to healthcare — and share a few thoughts on what makes the best systems stand out.
What is Agentic AI
For the uninitiated, let's briefly define Agentic AI: it’s essentially an AI system that can independently assess a problem, plan a series of actions, execute that plan across tools, and learn from the outcome.
But not everything branded as "agentic AI" today actually earns the title.
Cutting Through the Noise
The term “agentic” has been stretched so far it covers everything from basic automation tools to genuinely autonomous systems. Buyers that think they are getting an AI agent can sometimes end up with a narrow tool with no feedback loop, no memory, and no ability to improve. (Microsoft Copilot, for instance, is a useful service but not yet capable of managing complex workflows without human oversight).
At Merantix Capital, we look for agentic systems that are solving painful, high-friction problems inside the industries that run the world. The teams that matter combine technical firepower with deep domain expertise. They build agents that are not just clever, but dependable, battle-tested and capable of delivering compounding results.
Payments infrastructure, scientific research, healthcare delivery, cybersecurity — these are just a few of the frontiers where agentic AI will prove itself. I want to provide a few examples of innovative solutions in these spaces.
Payments
Payments is one of the first sectors where agentic AI is stepping beyond mere recommendation into real-world action. Traditionally, even the most capable AI systems were bottlenecked: they could suggest transactions but not actually execute them. The problem was not intelligence, it was infrastructure. Existing payment systems were built for human users, not autonomous agents.
Skyfire, Nevermined, and Payman are three startups that represent different but complementary solutions to this bottleneck.
Skyfire built a payment network designed specifically for AI agents to spend money safely. Rather than tying agents directly to a user's bank account, Skyfire issues each agent a wallet ID funded via traditional methods like ACH or wire, but all transactions conducted by agents happen in digital assets like USDC. This architecture allows businesses to tightly control spending through real-time dashboards while enabling machine-speed execution. In beta deployments, enterprises like car parts firm Denso used Skyfire to automate procurement payments, allowing agents to complete supply chain transactions without human intervention.
Nevermined, meanwhile, is building the "PayPal for AI agents," focusing on AI-to-AI commerce. It allows agents to pay one another for services autonomously, using crypto rails optimized for dynamic pricing, metered usage, and trustless transactions. Their approach emphasizes decentralized financial infrastructure, helping agents bypass the rigid, account-based systems that slow down human-driven commerce.
Payman tackles the other side of the problem: letting AI agents pay humans directly. Payman’s platform links agents to traditional banking systems and supports payments not only in USDC but also through fiat rails like ACH and wires. This makes it one of the first to bridge agents into the human economy, enabling scenarios where an AI agent could automatically hire a freelancer, monitor task completion, and release payment without manual coordination. Unlike Skyfire and Nevermined, which still transact primarily in digital assets, Payman enables actual bank transfers alongside crypto.
Most early agentic payment systems rely on digital assets like USDC or stablecoins because traditional banking infrastructure is not yet designed for autonomous, machine-initiated transactions. While Payman has made inroads into traditional rails, the broader financial ecosystem is still catching up.
This emerging layer of infrastructure, including wallets, safeguards and programmable transaction limits hints at what broader autonomous commerce could look like. As the rails mature, there is enormous potential for founders to build orchestration layers, audit and compliance tools, fraud prevention systems, and seamless agent-to-agent marketplaces on top.
Science and Healthcare
Scientific research and healthcare both suffer from massive knowledge bottlenecks: too many papers to read, too many variables to test, too many patients to monitor. Traditional workflows are slow, heavily manual, and ripe for transformation by agentic systems.
AI Scientist-v2 is an agentic system that performs the entire scientific method autonomously. It generates hypotheses, designs and simulates experiments, evaluates results, and iterates toward better answers. Then, it writes up those findings as full academic papers.
Introduced in ‘The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search’, by Yutaro Yamada et al in April 2025, this model contains a remarkable leap forward both AI and science: a fully AI-generated paper with no human ghostwriting that passed peer review. That’s a first in scientific publishing.
Here’s how it works:
The system explores multiple experimental pathways in parallel, simulating experiments based on probabilistic models and refining hypotheses over time.
Each hypothesis is scored for novelty and plausibility.
Promising results are bundled into a structured research paper using a modular writing agent trained on scientific publication norms.
Then, the paper is submitted for blind peer review. This time it got in.
This is autonomous research. AI that contributes new knowledge, not just insights. And it raises critical questions not just about what AI can do, but what roles remain essential for humans in the scientific process.
Another important example is Causaly, a biomedical research company that developed an agentic AI platform autonomously planning and executing multi-step research workflows by drawing from a 500 million–fact biomedical knowledge graph and external scientific literature.
Causaly’s agentic system reportedly prioritizes relevant evidence, updates findings dynamically as new data emerges and traces every insight back to its source to maintain scientific transparency.
In healthcare, Hippocratic AI, co-founded by Munjal Shah alongside experts from Johns Hopkins, Stanford, and Google, is tackling the shortage of frontline medical staff.
Hippocratic AI’s platform trains agentic healthcare assistants to handle non-diagnostic tasks like post-discharge follow-ups, chronic disease coaching, and medication reminders, while maintaining a rigorous safety-first approach validated through clinical trials involving over 5,000 nurses and 500 physicians. By late 2024, Hippocratic AI reported that its agents had achieved safety parity with trained nurses and pharmacists, formed partnerships with major health systems like Memorial Hermann and Universal Health Services.
These examples show that agentic systems aren’t just accelerating research or augmenting healthcare, they are starting to expand the boundaries of what is possible by performing tasks that were once considered uniquely human.
That said, there are still major opportunities ahead. Today, most scientific and healthcare agentic systems operate mainly in digital environments, working with existing data rather than interacting directly with the physical world.
This is partly because it’s easier to coordinate digital tools than to manage real-world variables like lab equipment or patient care. However, there is a huge need for experts to build agentic systems that can also automate wet lab experiments, manage clinical trials, and coordinate ongoing patient care — areas where sensitivity to real-world context, safety, and adaptability are essential.
Cybersecurity
Cybersecurity is a race where traditional defenses are falling behind. New vulnerabilities emerge daily, software evolves constantly, and attackers adapt faster than human teams can respond. Quarterly penetration tests, static firewalls, and reactive patching are no longer enough. The next wave of defense needs systems that think, move, and adapt on their own.
Terra Security deploys fleets of autonomous agents that continuously probe enterprise environments like web apps, APIs, and cloud assets without waiting for human instructions. Each agent specializes in a class of exploits such as SQL injection, authentication bypass, or privilege escalation, and plans new attacks dynamically as it learns about the target’s structure.
This shifts cybersecurity from point-in-time testing to continuous, self-directed red teaming. Instead of waiting months for a vulnerability report, companies receive real-time findings prioritized by exploitability and business risk.
Terra uses a hybrid model where human analysts verify AI-generated findings to ensure accuracy and relevance. This combination of machine scale with human oversight addresses one of the biggest weaknesses in traditional penetration testing: inconsistent accuracy and lack of operational context.
Despite these advances, cybersecurity remains wide open for agentic innovation. Most systems today focus on offense. Defensive agentic systems, capable of predicting threats, patching vulnerabilities, and neutralizing attacks without human help, are still rare. There is enormous opportunity for security-native founders to build the next generation of autonomous, self-healing defenses. (In our own portfolio, check out Company Shield).
The best is yet to come
The best agentic projects rise above the noise because they deliver genuine autonomy where others still depend on human intervention. The breakthroughs in payments, research, healthcare, and cybersecurity show that when agentic systems are built for real-world complexity, they do not just streamline workflows, they open up entirely new ways of operating (check out our recent Substack with LogicStarAI’s founder for another example in the engineering space).
Founders who combine technical strength with deep industry knowledge, and who focus relentlessly on solving high-friction problems, will define the next era. The future belongs to those who can turn agentic AI from a buzzword into real-world performance.
As always - if you are ideating or building in this space, we should talk!
For an in-depth pulse check on the Agentic AI hype, be sure to join our AI Breakfast on this topic, being held this coming Thursday, June 12th on the Merantix AI Campus: https://lu.ma/7oazc49z