by Akhil Verghese, co-founder and CEO of Krazimo
Enterprise AI is evolving rapidly, but not always in the direction most executives envision. The conversation is still centered around models and their performance benchmarks, latency, and cost per token. But in practice, these are not the factors that determine success.
The real change happens at the workflow level, where systems no longer execute isolated tasks but coordinate multi-step processes across tools, data, and decision points.
This change is what defines agentic AI. And this is where most organizations remain unprepared.
The transition from models to workflows
Many companies started their AI journey with single-model use cases (i.e. chatbots, summary tools, or basic automation). These systems are relatively easy to deploy, but they provide limited long-term value.
Agentic AI changes the equation. It introduces systems that can plan, act, and iterate across multiple stages, often interacting with internal tools and data sets along the way.
But it’s not just a technical upgrade. It’s an operational question. Building reliable multi-agent systems requires integrating business knowledge, implementing safeguards and ensuring predictable behavior within defined constraints.
Organizations fail when they treat agent systems as an extension of automation, when in reality they require a different level of design discipline.
What actually defines an agentic system
The distinction between traditional automation and agentic AI is subtle but essential. Automation executes predefined steps. Agentic systems make decisions in a structured environment.
This does not mean completely removing humans from the process. In fact, the opposite is true. Humans must oversee the transition to autonomy until the system has proven that it can operate reliably within its scope. Without this oversight, systems become either too rigid to scale or too unpredictable to trust.
Why infrastructure decisions are now strategic
Most discussions about enterprise AI still treat infrastructure as a secondary concern.
AI systems rely on internal processes, proprietary knowledge and operational workflows. The question is not only how to process the data, but also where it should be and who controls it. There’s no one-size-fits-all answer, but it’s a question businesses need to ask themselves much earlier in the process. For workflows directly related to a company’s core competency, this control becomes essential.
Another concern is that while cloud-based systems offer flexibility, they also introduce long-term uncertainties regarding costs and dependency. As model providers adjust their pricing to support their operations, the economics of cloud-based intelligence could change significantly.
In contrast, locally hosted open source models, while more complex to implement, can provide greater control, greater stability, and greater cost effectiveness over time.
Governance is no longer optional
As agent systems take on more responsibility, the risks associated with them become harder to ignore. Incorrect results, unauthorized access to data or poorly calibrated decisions are no longer isolated errors but operational liabilities, and can increasingly become legal errors.
The reality is that organizations will likely soon be held fully accountable for the actions and outcomes of their AI systems.
In practice, strong governance starts with structure. Data should be clearly labeled and categorized. Access must be strictly controlled. Each agent must operate within a defined perimeter, with authorizations adapted to their role.
Most importantly, AI workflows should be treated as if humans were performing them. Every action must be recorded, reviewed, and attributed to a responsible party. Systems can act, but responsibility always lies with individuals.
Integration: the discrete bottleneck
While models and governance receive the most attention, integration is where many AI initiatives stagnate. The challenge is not only technical compatibility but also operational alignment.
Enterprise systems are often fragmented, with limited APIs, inconsistent data access, and restrictive terms of service. Even when integration is technically possible, it may not be permitted within the existing constraints of the tools used.
The challenges vary greatly, but they often boil down to a few basic questions, including:
- A lack of accessible interfaces
- Limitations of system interoperability
- Mismatches between how data is stored and how it should be used
Agent systems cannot simply be layered on top of existing infrastructure. They must be designed from the outset with integration in mind.
The role of strategic partnerships
There is growing recognition that companies should build a network of specialist suppliers to manage the different components of their AI systems. In theory, this makes sense; However, in practice this often creates more complexity than it solves.
The real value comes not from bundling multiple vendors, but from working with partners who understand how to tailor systems to the organization’s specific data and workflows. In the field of enterprise AI, the most difficult problem is adapting the technology to business realities. This adaptation requires deep understanding, not just technical ability. Companies that can’t develop the tools they need in-house should look for long-term AI partners, not single-purpose contracts.
What Business Leaders Should Do Now
For organizations investing in AI today, the priority should not be clarity over speed. It starts with defining processes, labeling data, and establishing what success actually looks like.
From there, systems can be built in a phased and controlled manner, either in-house or with the right partner. This approach may seem slower at first, but it avoids having to dismantle poorly designed systems later. Once AI is integrated into daily operations, it’s much harder to go back than to get it right the first time.
The reality check
The real state of agentic AI in the enterprise is not that of a complete transformation but that of a transition. The technology is capable. The models are moving forward. But the infrastructure, governance and operational maturity needed to support them are still catching up.
The organizations that recognize this gap and address it will be the ones that move beyond experimentation and toward lasting value. Others will continue to build systems that work in theory but fail in practice.
Akhil Verghese is the visionary founding leader of Krazimoleading the company’s mission to bring trusted, enterprise-grade generative AI to market. With an engineering background at one of the strongest technology companies, he founded the company to deliver AI solutions built on engineering rigor, workflow clarity, and measurable business results. Under his leadership, Krazimo is focused on guiding enterprises in AI adoption (strategy), creating multi-step workflow automation, deploying fetch-augmented generation (RAG)-based multi-agent systems, and executing rapid AI-assisted full-stack development.
