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Still betting on one AI model? It’s slowing you down. Contextual interop + multi-LLM agentic AI is how smart enterprises scale, adapt, and dominate.
Why is AI important in the banking sector? | The shift from traditional in-person banking to online and mobile platforms has increased customer demand for instant, personalized service. |
AI Virtual Assistants in Focus: | Banks are investing in AI-driven virtual assistants to create hyper-personalised, real-time solutions that improve customer experiences. |
What is the top challenge of using AI in banking? | Inefficiencies like higher Average Handling Time (AHT), lack of real-time data, and limited personalization hinder existing customer service strategies. |
Limits of Traditional Automation: | Automated systems need more nuanced queries, making them less effective for high-value customers with complex needs. |
What are the benefits of AI chatbots in Banking? | AI virtual assistants enhance efficiency, reduce operational costs, and empower CSRs by handling repetitive tasks and offering personalized interactions. |
Future Outlook of AI-enabled Virtual Assistants: | AI will transform the role of CSRs into more strategic, relationship-focused positions while continuing to elevate the customer experience in banking. |
In the race to implement AI, many enterprises fall into the trap of believing that choosing the "best" language model is all it takes. Whether it’s GPT-4, Claude, Gemini, LLaMA, or any of the other top-tier LLMs, the assumption is simple: pick the smartest model and run everything through it.
But here’s the reality: no single model — no matter how advanced — can optimally handle every task across every context.
As enterprises shift towards agentic AI architectures and intelligent agent-led workflow automation, relying on one fixed LLM is like hiring one person to be your CEO, software engineer, support agent, legal advisor, and graphic designer — all at once. It’s inefficient, expensive, and ultimately ineffective.
Let’s dive into why contextual interoperability is the new gold standard — and how agentic systems can adopt it to build scalable, intelligent AI ecosystems.
For deeper insight into how agentic design solves enterprise-level problems, check out 7 Enterprise Problems Only Agentic AI Can Solve.
Every LLM has its strengths and blind spots:
So why force one model to stretch across every use case?
Smart orchestration means choosing the best tool for the job — and enabling LLM switching at runtime within your agentic workflows. Learn more about how to select the right LLM for each agent in The Hidden Engine Behind AI Agents: Choosing the Right LLM.
Contextual interoperability is the ability for multiple models — and the agents using them — to:
This creates agentic workflows where:
Without interoperability, these would remain disjointed, stateless, and brittle.
With it, they become fluid, intelligent, and contextually aligned.
Just switching models isn't enough. You need a layer that retains memory, understands task state, and dynamically routes agents and models to the right action — without losing track of the user’s original goal.
That’s where Model Context Protocol (MCP) comes in. Explore why it’s considered foundational to enterprise-grade AI design in Why MCP is the Key to Enterprise-Ready Agentic AI.
MCP enables:
This is critical for agent-based systems where tasks are segmented, handed off, and need to be orchestrated without losing coherence.
Let’s say you’re deploying an Agentic AI system for enterprise support:
This orchestration requires at least 3 different model types — each optimized for its own role.
Trying to run all of that through a single model:
Think of modern enterprise AI as reasoning meshes — interconnected agents that:
These networks are governed by an orchestration layer that:
This is the essence of Agentic AI — not just running models, but orchestrating reasoning at scale.
Many AI teams still think in terms of “choosing the right model.” But the new design principle is: “Use the right model for the right task, at the right time — in a context-aware, agent-led framework.”
This requires:
It’s no longer about picking one model and scaling it. It’s about building an intelligent mesh of collaborating agents. And as IT leaders take on more strategic AI responsibilities, it’s crucial to understand their evolving role — as explored in How IT Departments Are Becoming the HR of AI Agents.
With contextual interop in agentic systems:
You also gain reliability via fallback logic — if one agent fails or produces low-confidence output, another takes over.
The idea that one LLM will do it all is outdated — and increasingly a bottleneck for growth.
Agentic AI isn’t about deploying a supermodel. It’s about composing smart agents that collaborate, adapt, and think.
Enterprises that embrace this model will:
Your AI ecosystem shouldn’t rely on one brain. It should be built like a network of AI agents — each excellent at one thing, and all talking to each other.
Ready to architect your multi-model AI future? It starts with contextual interoperability — and it scales with agents who know when to switch gears and how to work as a team.
Fluid AI is an AI company based in Mumbai. We help organizations kickstart their AI journey. If you’re seeking a solution for your organization to enhance customer support, boost employee productivity and make the most of your organization’s data, look no further.
Take the first step on this exciting journey by booking a Free Discovery Call with us today and let us help you make your organization future-ready and unlock the full potential of AI for your organization.
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