by Akhil Verghese, Co-founder & CEO of Krazimo
Enterprise AI is moving quickly, but not always in the direction most leaders think. The conversation is still centered on models and their performance benchmarks, latency, and cost per token. But in practice, those aren’t the factors determining success.
The real shift is happening at the workflow level, where systems are no longer executing isolated tasks but coordinating multi-step processes across tools, data, and decision points.
That shift is what defines agentic AI. And it’s where most organizations are still unprepared.
The shift from models to workflows
Many enterprises began their AI journey with single-model use cases (i.e., chatbots, summarization tools, or basic automation). These systems are relatively easy to deploy, but they offer limited long-term value.
Agentic AI changes the equation. It introduces systems that can plan, act, and iterate across multiple steps, often interacting with internal tools and datasets along the way.
But this isn’t just a technical upgrade. It’s an operational one. Building reliable multi-agent systems requires integrating company knowledge, enforcing guardrails, and ensuring predictable behavior within defined constraints.
Organizations fall short when they treat agentic 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 critical. Automation executes predefined steps. Agentic systems make decisions within a structured environment.
That doesn’t mean removing humans from the process entirely. In fact, the opposite is true. Humans need to supervise the transition to autonomy until the system has proven it can operate reliably within its scope. Without that supervision, systems either become too rigid to scale or too unpredictable to trust.
Why infrastructure decisions are now strategic
Most enterprise AI discussions still treat infrastructure as a secondary concern.
AI systems rely on internal processes, proprietary knowledge, and operational workflows. The question isn’t just how to process data, but where it should live and who controls it. There’s no universal answer, but it’s a question enterprises need to ask much earlier in the process. For workflows tied directly to a company’s core competency, that control becomes essential.
Another concern is that while cloud-based systems offer flexibility, they also introduce long-term uncertainties around cost and dependency. As model providers adjust pricing to sustain their operations, the economics of cloud-based intelligence may shift significantly.
In contrast, locally hosted open source models, though more complex to implement, can offer greater control, stability, and cost efficiency over time.
Governance is no longer optional
As agentic systems take on more responsibility, the risks associated with them become harder to ignore. Incorrect outputs, unauthorized data access, or poorly calibrated decisions are no longer isolated errors but operational liabilities, and may increasingly become legal ones.
The reality is that organizations will likely soon be held fully accountable for the actions and outputs of their AI systems.
In practice, strong governance starts with structure. Data must be clearly labeled and categorized. Access must be tightly controlled. Every agent must operate within a defined scope, with permissions aligned to its role.
More importantly, AI workflows should be treated as if humans were executing them. Every action should be logged, reviewed, and attributable to a responsible party. Systems can act, but accountability still belongs to people.
Integration: The quiet bottleneck
While models and governance receive most of the attention, integration is where many AI initiatives stall. The challenge isn’t just technical compatibility but 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 permissible within the existing constraints of the tools being used.
The challenges vary widely, but they often fall into a few core issues, including:
- A lack of accessible interfaces
- Limitations on system interoperability
- Mismatches between how data is stored and how it needs to be used
Agentic systems cannot simply be layered on top of existing infrastructure. They must be designed with integration in mind from the start.
The role of strategic partnerships
There is a growing assumption that enterprises should assemble a network of specialized vendors to handle different components of their AI systems. In theory, this makes sense; in practice, however, it often creates more complexity than it solves.
The real value doesn’t come from assembling multiple vendors but from working with partners who understand how to tailor systems to the organization’s specific data and workflows. In enterprise AI, the hardest problem is adapting technology to the realities of the business. That adaptation requires deep understanding, not just technical capability. Businesses that can’t develop the tools they need internally should seek long-term AI partners, not single-purpose contracts.
What enterprise leaders should do now
For organizations investing in AI today, the priority should not be clarity over speed. That starts with defining processes, labeling data, and establishing what success actually looks like.
From there, systems can be built in a phased, controlled manner, either internally or with the right partner. This approach may feel slower at the outset, but it prevents the need to unwind poorly designed systems later. Once AI becomes embedded in daily operations, reversing course is far more difficult than getting it right the first time.
The reality check
The real state of agentic AI in the enterprise isn’t one of full transformation but one of transition. The technology is capable. The models are advancing. But the infrastructure, governance, and operational maturity required to support them are still catching up.
The organizations that recognize this gap and design for it will be the ones that move beyond experimentation and into sustained value. The rest will continue to build systems that work in theory but fail in practice.
Akhil Verghese is the visionary founding leader of Krazimo, steering the company’s mission to bring reliable, enterprise-grade generative AI to the market. With a background that includes engineering experience at one of tech’s strongest firms, he founded the company to deliver AI solutions built on engineering rigor, clarity of workflow, and measurable business outcomes. Under his leadership, Krazimo focuses on guiding businesses through AI adoption (strategy), creating multi-step workflow automation, deploying multi-agent systems based on retrieval-augmented generation (RAG), and executing rapid full-stack AI-assisted development.
