Performance Over Promises: Where Agentic AI Actually Wins

There’s a lot of noise in AI right now. Everyone’s calling themselves “agentic.” Everyone’s building copilots. Everyone claims to be reinventing the enterprise. But for founders building in this space — and investors trying to back them — the real questions haven’t changed.

What problem are you solving? For whom? And does your product actually perform — in latency, accuracy, and trust? What value is the product delivering?

As a repeat founder and longtime investor, I’ve spent my career looking for these answers across platform shifts: from on-prem to SaaS, SaaS to cloud-native, and now from cloud-native to AI-native. At Neotribe, we invest in breakthrough technologies that stretch the imagination, and the rise of agentic AI is one such shift we’re watching closely. It’s not just a new layer on top of software. In many cases, it’s replacing software altogether.

The most interesting agentic companies don’t just help humans work faster, they do the work. And that forces a new question for founders: are you a productivity enhancer, or are you the system of execution?

Front Office vs. Back Office

I break most enterprise agentic startups into two categories: front office and back office. There are promising signs in both, but the dynamics are very different and founders in each space should take note of these differences.

In the front office, where customer experience and revenue generation are on the line, expectations for AI performance are particularly high. Agentic systems are beginning to take on complex, cross-functional GTM challenges that traditional SaaS tools only surface — never solve. RevSure is a great example. They’re focused on a persistent pain point for B2B marketing and sales teams: understanding and optimizing the full funnel in the modern GTM landscape.

RevSure doesn’t just report on pipeline health. It builds an integrated, real-time GTM graph from systems like CRM, sales automation, ad platforms, and ABM tools. Then it analyzes touchpoints, surfaces conversion drivers, and makes AI-powered recommendations on where to spend, what to prioritize, and how to improve outcomes.

In a world where marketing complexity is rising and budgets are under scrutiny, agentic tools like this don’t just offer insight—they deliver execution.

RevSure does this through a framework that is quickly becoming the formula for deploying agents in the enterprise: a “Team of Agents” built from an array of agents that specialize in specific—but coordinated—tasks. In another glimpse of future functionality, RevSure offers a robust agent builder, allowing teams to create additional agents that serve needs as unique as their team, while still ensuring the agents operate as a team alongside human ‘coworkers.’

In the back office, I’m drawn to workflows that are overlooked, yet mission-critical — and where trust is hard-earned. Auquan and Auditoria are standouts.

When Auditoria started, they applied NLP to finance workflows, extracting structured data from vendor emails, managing invoice processes, handling repetitive transactional tasks. Initially, traction was slow. Finance leaders didn’t trust opaque systems handling sensitive workflows.

But as GenAI has matured — offering more transparency, reasoning, and controllable outputs — the same workflows became automation-ready. Today, Auditoria is managing these processes autonomously, not as a helper but as a responsible agent in the workflow. It’s a great example of how trust, timing, and execution matter more than the buzzwords.

Auquan is pushing agentic AI into the core knowledge work that underpins the financial system. Financial services is a particularly ripe domain for this transformation — document-heavy, regulation-bound, and high-stakes, with deep needs for precision, speed, and trust.

Auquan automates highly complex workflows across private equity, private credit, asset management, insurance, and investment banking. Instead of UI macros or templated outputs, its agents navigate and synthesize large volumes of financial data, legal documents, analyst reports, and internal files. The result? Structured outputs like IC memos, credit assessments, and market risk summaries that used to take hours or days of skilled work.

That full-spectrum capability — from information retrieval to synthesis and decision-support — is why Auquan is trusted by some of the largest financial institutions in the world. It doesn't just boost productivity, it augments judgment and helps teams move faster with more confidence.

Common Theme: User Empathy + AI Excellence

What unites companies like RevSure, Auditoria, and Auquan is a shared approach to building agentic AI: they focus on delighting their ICP through previously impossible technical innovation. This requires four things:

1. Deep empathy for the user’s context and workflow: in-depth understanding of the needs and frustrations of individual users and teams
2. Keen insight into the sector and its unspoken bottlenecks: the friction that slows down decision-making, drains resources, or creates organizational drag
3. Technical excellence and AI-first product building: Not just an AI-native approach, but staying at the forefront of the incredible rate of change 
4. Supercharged Time To Value: when these other pieces come together, your ICP should feel the most immediate impact possible. There is considerable AI hype—to stand apart you need to prove it ASAP

The winners of the AI agent space must be obsessed with their user and reimagining that user’s workflow to make them ever more successful at their jobs. Only from that position of deep user understanding can they deploy targeted, trustworthy AI systems that transform work in ways no traditional software ever could.

As we have shown, these agents will be doing important work on teams across the enterprise, but what is the nature of that work? A clear pattern we see is that agents today are deployed on must-do (but not necessarily mission-critical) workstreams that are necessary but not sufficient for a high-performing team's success. This proves out the claims made by agentic AI startups: agents are meant to free up people and teams to focus on higher-level, more strategic work that can transform their impact.

There are many tasks where there is a minimal gulf between 'being done' and 'being done well,' and this is where senior leaders are most excited for agents to have an impact. Knowledge work requires many tedious and rote tasks that simply must be done, but the agentic era shows us a future where those tasks don't need to be completed at the expense of the most value-additive aspects of meaningful work.

The Litmus Test

Every wave of technology brings hype. But performance is what endures. I always come back to three questions when evaluating agentic AI startups:

1. Is it doing real work — not just suggesting it?
2. Is it trusted by the teams it serves?
3. Can it prove performance in terms the buyer actually cares about — speed, accuracy, cost, or risk reduction?

If the answer is yes, there’s something there.

The best companies in this space don’t start with the tech. They start with a deep understanding of the workflow and then they make it disappear.

That’s the kind of breakthrough that doesn’t just improve business, it reimagines it. And that’s exactly the kind of innovation we see coming, and the companies we love to support.