Agentic AI and the rise of intelligent enterprise orchestration
In a guest blogpost, IFS’s Vaibs Kumar says agentic AI and model context protocols will move enterprise AI beyond pilots and into scalable, real-world automation.
We are long past the point of questioning whether AI belongs in the enterprise. The real challenge businesses face is getting beyond flashy pilots and moving toward AI that scales. That’s where Agentic AI and emerging Model Context Protocols (MCP) enter the picture. Together, they open the door to systems that assist humans and orchestrate work themselves across teams, data, and applications. This journey shouldn’t be seen as a choice between copilots and Agentic AI; instead, the real opportunity is integrating both to accelerate productivity and innovation across an enterprise.
Most enterprises today are familiar with AI through the lens of co-pilots: helpful assistants that respond to user prompts and make life a bit easier. They summarise documents, generate emails, and answer support questions. Useful? Absolutely. Transformative? Not quite yet.
From co-pilots to true agents
The next leap forward lies in moving from reactive tools to proactive agents. Agentic AI is about understanding a goal and taking steps to achieve it rather than only responding to queries. These agents can be triggered by a user prompt, of course – but also by a sensor alert, a system threshold being crossed, or even a change in weather. They handle multi-step processes autonomously by pulling context from multiple systems and adapting in real time.
In many ways, co-pilots and agents are complementary models. A co-pilot might help you kick off a request, then hand it off to an agent to execute at scale. This interplay, where humans remain in the loop for higher-level decisions and oversight, while AI handles repetitive or time-sensitive tasks autonomously, is essential for efficiency and innovation. The next generation of intelligent enterprise systems will be defined by this interplay.
The role of Model Context Protocols
Of course, for any of this to work, AI needs access to the right context, and that’s a bigger challenge than it sounds. Enterprises don’t suffer from a lack of data, they suffer from fragmented, siloed, and inconsistent data access.
That’s what Model Context Protocols (MCP) aim to solve. Think of MCPs as the connective tissue between enterprise data and AI agents. Traditional APIs are great for structured, predefined queries. MCPs go the next step – they help language models dynamically understand and navigate complex business data without needing custom connectors for every use case.
Imagine an AI agent that can pull a field service agent’s schedule, then imagine that it also understands the semantics of job urgency, traffic patterns, and parts availability. An AI dispatch agent combines real-time telemetry and supply data to continuously optimise scheduling. That’s not a one-off or science fiction – it keeps listening and keeps adapting. That’s Agentic AI in action, and actually, it’s already starting to take action.
Why proof of concept isn’t enough
So why aren’t more organisations doing this? Simple: most are stuck in the proof-of-concept mode. It’s not the AI models that are the issue – it’s the missing scaffolding around them. Too often, companies underestimate the importance of data infrastructure, orchestration layers, and governance. Without that foundation, even the most impressive AI demos never leave the lab.
MCP can help here by providing a scalable, reusable interface between AI systems and the enterprise context. It is still early, and features like authentication and access control are maturing, but the open-source community is moving quickly. We’re optimistic about its trajectory.
What comes next
The most exciting shift is the potential to redefine how enterprise work gets done.
Today, there’s a gap between IT teams that manage systems and business users who understand the real-world processes. Agentic AI can bridge that divide. By working across existing systems and adapting to business intent, these agents orchestrate work instead of just supporting it.
In the next few years, we’ll see this move from concept to reality. AI agents will go from helpful assistants to full-blown process orchestrators, reshaping how work flows through the enterprise. When paired with model-aware context protocols, they’ll enable intelligent systems that are genuinely enterprise-aware.
This next step in AI’s evolution amplifies what’s possible by combining AI’s scalability with human judgment and empathy. The real winners won’t be the ones with the most advanced AI, but those who integrate copilots and agentic AI the most meaningfully into their operations.
Vaibs Kumar is the vice president of technology at IFS, a cloud enterprise software company.