Taking Control: Why Enterprises Must Build Their Own LLM Ecosystem

Enterprises today are inundated with a growing array of AI solutions, particularly in the area of agentic workflows. Numerous startups—such as Flowise, Dify, and n8n—offer platforms that integrate and orchestrate workflows for specialized tasks. A look at recent Y Combinator cohorts reveals a strong focus on agentic solutions targeting a variety of domains. In healthcare, for example, Hippocratic AI claims its automated nursing agents can handle routine care tasks at under $10 per hour, significantly undercutting the cost of human nurses. Similarly, Adept AI builds agents that can navigate desktop applications to automate complex corporate workflows. Gartner predicts agentic AI will autonomously resolve 80% of common customer service issues without human intervention by 2029. Relevance AI offers teams of AI agents marketed as capable of delivering work at a level comparable to human professionals. However, these claims raise a crucial question: what concrete metrics or benchmarks are being used to substantiate the assertion of “human-quality” performance.

These solutions boost efficiency for narrow, repeatable processes. However, they follow fixed, well-defined steps and lack the adaptability needed for complex challenges. Agentic Workflows are better suited for well-defined and predictable tasks that follow a consistent process whereas Autonomous AI agents excel when tasks are open-ended and dynamic. Simply put, outsourced agentic services can handle today's deterministic routines—but they won't suffice for tomorrow's unpredictable problems. However, Large Language Models (LLMs) are becoming smarter, cheaper, and more capable in performing complex task. In future, it is highly expected that autonomous agents will be able to plan and execute complex tasks with limited and no human interventions.

The economics of AI are shifting. Large Language Models (LLMs) are  also becoming cheaper, and accessible.  Thanks to new open-source releases, competition, and optimised hardware. Open-source models which lately showcasing comparable performance to service based LLM. Which in future, will eliminate expensive licensing fees and can be fine-tuned in-house, yielding huge cost savings. Furthermore, open-source LLMs can be also tailored to business needs without extra vendor costs and privacy concerns. Organizations can build their own AI "brains" on top of proprietary data at a fraction of the cost of a cloud API subscription. This means paying startups to host simple agentic workflows may not be sustainable when the same capabilities can run on cheaper in-house models.

From Workflows to Intelligence

AI agents and agentic workflows are often framed as autonomous helpers, but there's a crucial distinction. Agentic workflows follow a fixed sequence of steps (think: a programmed flowchart), while true AI agents can plan, reason, and adapt on the fly. Today's agentic solutions excel at repetitive, predictable tasks—data entry, routine report generation, or simple decision trees in customer support. But when a problem requires creative problem-solving or handling unexpected situations, these fixed workflows break down.

AI agents excel at handling complex, open-ended, and dynamic tasks that demand flexibility, creativity, and responsiveness to unexpected situations. In contrast, agentic workflows are less adaptable and struggle to re-plan entire processes from end to end. Consider this example: a workflow agent might rinse, wash, and dry a car in order, but a multi-agent AI could dynamically decide to dry first in a rainstorm, skip a step if needed, or call for human help if it spots a mechanical issue. Enterprises should recognize this gap: simple agentic apps solve today's predictable problems, but future challenges demand adaptable, intelligent networks of agents.

Multi-agent AI (Agentic AI) systems function as networks of collaborative agents. In a multi-agent network, specialized AI "workers" communicate and coordinate to solve complex problems. As one PWC report puts it, "Agentic AI generally refers to AI systems that possess the capacity to make autonomous decisions and take actions to achieve specific goals with limited or no direct human intervention.". In practice, a multi-agent approach might involve one agent planning a project schedule, another sourcing data, and a third analyzing feedback—each using its own tools and knowledge while sharing information. By integrating multiple agents (including general-purpose LLMs and domain-specific modules), organizations can tackle much broader tasks than any single chatbot or workflow. Multi-agent systems require orchestration of "diverse agents, tools, and knowledge sources—including general-purpose LLMs and organization-specific systems" so agents can "autonomously organize, delegate tasks, and route processes". The vision is powerful: an internal AI ecosystem that continuously plans, evaluates, and iterates across teams, rather than a static pipeline. BCG Report for further information

Strategic Advantage: Own Your AI Future

"In the fast lane of technological evolution, missing the AI turn today means being outpaced tomorrow. "- PWC

The upside of this path is full ownership and strategic control over your AI infrastructure. By investing now, companies can lock in their data and know-how rather than hand it to outsiders. Enterprises today no longer need to rely on closed, black-box models from a handful of providers—they can leverage open-source innovation while ensuring customization, security, and scalability. In other words, building in-house means you avoid future vendor lock-in.

Cognizant and other leaders are already moving in this direction. Cognizant's open-source Neuro AI Accelerator for multi-agent systems lets businesses prototype and integrate AI agents without being tied to a single platform. As Cognizant's AI CTO puts it, in the era of agentic AI "enterprises must be free to experiment" and having open access to multi-agent tech. In practical terms, when you own the model, you can fine-tune it on your proprietary processes and data, update it on your schedule, and spin up as many agents as needed—all without negotiating usage fees or policy changes imposed by a third party.

Investing in an internal multi-agent AI network is therefore not just about technology; it's about strategic preparedness. Early adopters will have years of experience and iterated models when the field matures. They will have built an "AI-native" culture with data pipelines and training processes in place. By contrast, late entrants may find themselves buying expensive API calls or agentic tool subscriptions—and still reliant on external systems. In short, the future of enterprise AI belongs to those who start building their own AI infrastructure today.