Agentic AI is Not Just a New Tool; It’s a New Operating Model for the Enterprise

Sharif Berdi, the Senior Director for Data & AI Engineering at Inception (a G42 company), says, the vision isn’t a single super-agent, but a network of specialised agents that collaborate, just like high-performing human teams

How do you define Agentic AI, and how does it fundamentally differ from traditional chatbots or rule-based automation?
Agentic AI has introduced a new way for enterprises to harness intelligence. No longer is intelligence simply a reactive tool, but rather a strategic enabler. Traditional chatbots or rule-based systems handle tasks by following instructions. They do not have the ability to interpret, adapt, or anticipate. Instead, they rely on pre-programmed scripts to respond to isolated inputs.

Agentic AI, on the other hand, solves problems. It doesn’t wait to be told what to do; it interprets intent, maintains memory across interactions, adjusts to dynamic input, and collaborates with other agents and humans to reach outcomes. Think of it as an assistant that doesn’t just follow commands but proactively navigates the complexities of enterprise operations.

We recognised the limitations of legacy automation—such as inflexibility, siloed data, and lack of real-time insights—and designed solutions to overcome them. We view Agentic AI as intelligent systems that can sense, decide, and act across different parts of an organisation. This philosophy powers our (In)Business Suite, which embeds autonomous AI agents into key business areas like procurement, process automation, productivity, and customer experience.

For example, (In)Business Procurement does more than automate contract workflows. It identifies high-performing, sustainable suppliers, accelerates sourcing-to-award cycles, ensures compliance, and drives measurable savings. Meanwhile, our (In)Business Productivity and (In)Business Process modules empower teams to deploy no-code AI agents that coordinate workflows, surface knowledge, and make intelligent decisions often faster, more accurately, and at greater scale than human-led systems. Agentic AI is not just a new tool; it’s a new operating model for the enterprise.

Beyond task automation, what complex, multi-step problems are agentic AIs uniquely positioned to solve that current AI models cannot?
The true promise of Agentic AI lies in its ability to execute high-impact, multi-step problem-solving by coordinating across domains, tools, and objectives with reasoning, foresight, and contextual adaptation. Traditional AI models can automate narrow tasks while AI agents can manage entire workflows, make decisions, and even learn from evolving environments. These systems are capable of autonomous, outcome-driven orchestration in real time.

We are building Multi-Agentic solutions into the foundation of our enterprise products. These agents learn from sentiment, personalise responses, and help human teams deliver empathetic, real-time support which is far beyond what static chatbots can achieve. This enables enterprises to move from reactive automation to intelligent transformation by solving problems that are too fluid, complex, or involve high stakes for traditional models.

How much autonomy should Agentic AI have in decision-making, and where should humans remain in the loop?
Autonomy in Agentic AI should be both contextual and intentional, built around the principle of augmentation and not replacement. In regulated, high-stakes industries such as finance, healthcare, or public safety, full autonomy without human oversight is potentially risky. Instead, we need a tiered approach that distinguishes between low-risk operational tasks and high-impact strategic decisions.

This dynamic orchestration where agents proactively flag issues, surface insights, and suggest next actions, while humans make final calls is where Agentic AI shows its true value. It’s not about full autonomy; it’s about intelligent delegation.

Our products are designed to escalate when ambiguity is high, confidence scores are low, or when ethical, legal, or reputational thresholds are at stake. We empower enterprises to define custom guardrails that ensure decisions adhere to their compliance protocols and organisational ethics. Agentic AI thrives when it operates within these boundaries: autonomous where it drives efficiency, collaborative where it enables better judgment. Ultimately, the goal isn’t to eliminate human involvement but to elevate it.

What’s the next frontier for Agentic AI—will we see AI “agents” collaborating like human teams?
Absolutely. The vision isn’t a single super-agent, but a network of specialised agents that collaborate, just like high-performing human teams. Imagine a marketing agent identifying a shift in consumer sentiment, triggering a pricing agent to re-optimise offers, while a supply chain agent adapts distribution – all in real time, with minimal human input. This decentralised collaboration opens the door to adaptive enterprises that are faster, leaner, and more responsive to change.

Will open-source models keep pace with proprietary Agentic AI systems, or will there be a widening gap?
Open-source has always been the engine of rapid innovation, and I don’t see that changing. In fact, some of the most impressive breakthroughs in large language models have come from open communities. However, agentic systems need orchestrators, memory modules, secure APIs, and real-world alignment layers.

Proprietary systems may move faster in production-grade deployment, especially when backed by dedicated infrastructure and domain-specific fine-tuning. But open-source will continue to challenge the status quo, especially in driving transparency, accountability, and local innovation. The future is likely to be hybrid: proprietary strength paired with open-source vitality.