AI
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Agentic AI has become the defining tech trend of 2026, yet most business leaders are still scrambling to answer a more fundamental question: who's actually in control?

According to the recent EY survey released in March 2026, 78% of leaders admit that AI adoption is already outpacing their organisation's ability to manage the risks it creates.

The timing is not coincidental, as throughout 2025, AI initiatives moved from experimentation to heavy industrialisation. As a result, enterprise spending on generative AI has surged to $37 billion over the past year. However, it's not the popularisation of the AI ecosystem that shifted the conversation toward control and governance, but rather the emergence of a completely new generation of AI models, known as agentic AI.

What Agentic AI Actually Is (And Why 2026 Is Its Year)

Traditional AI systems operate simply: they wait for a prompt and produce a standalone answer. Agentic systems, on the other hand, are platforms/software/programs that:

  • Take a high-level objective
  • Break it into subtasks
  • Select the right tools
  • Execute a plan
  • And adapt to the changes in the conversations to achieve users' objectives

Despite such an elaborate logic path, they still operate within a defined boundary in the business environment.

However, what appears to be a single AI agent is usually a network of specialised agents, each owning a piece of the workflow and tied together by an orchestration layer that keeps them on track toward the overall goal.

As the system operates completely autonomously, AI implementation becomes more lucrative for modern companies. Agentic AI can automate workflows that previously required entire teams, like sales outreach, pipeline research, and compliance checks.

That trajectory explains why 2026 feels like the year of AI agents. The range of tasks that AI agents can complete with an 80% success rate has been doubling roughly every seven months. Running a GPT-3.5-level system costs 280 times less than it did in 2022. Hardware costs are falling 30% a year. At that pace, 2026 will be the year businesses run out of reasons not to deploy agentic AI.

The AI Governance Gaps No One Is Talking About

The security conversation around agentic AI tends to focus on the obvious: data breaches, prompt injection, model vulnerabilities. The deeper problems are less visible.

Agentic AI deployment is not a project with a finish line. It is an ongoing operation that demands continuous monitoring, process adaptation, and a dedicated team managing it as the business around it changes. Most organisations are not staffed for that. They treat AI implementation like a software rollout — launch it, move on — and that is where things quietly break down.

Open-source models pose another issue. When anyone can modify the underlying architecture, achieving data privacy becomes impossible. For any business handling customer data, implementing an open-source-based AI poses uncontrolled risks.

This raises a more fundamental problem: agentic AI is only as reliable as the logic it runs on. Without exhaustive testing of every edge case, these systems will find gaps and act on that logic loop. Agentic AI is not a magic pill, and, in some cases, solid automation using standard code is 10x more reliable if you expect your processes to be executed according to the rules and procedures 100% of the time.

When AI Disproved to Be a Magic Pill: The Real-World Cases

The risks outlined above have already moved beyond being hypothetical. They are already playing out, often quietly, but occasionally–very publicly.

In January 2024, delivery firm DPD disabled its AI customer service chatbot after a routine system update stripped away its guardrails. A frustrated customer prompted it to swear, write poetry about how useless it was, and declare DPD "the worst delivery firm in the world." The company called it an error. In practice, it was a live demonstration of what happens when AI is deployed without adequate logic testing and oversight. The case became a reputational damage broadcast across social media within hours.

The stakes are not always so visible. In September 2025, Press Gazette revealed that three linked PR agencies — Signal The News, Relay The Update, and Inform The Audience — had been bombarding British journalists with what appeared to be AI-generated press releases populated by fake experts. Former police officer "Pete Nelson" and chef "Daniel Harris", both cited across major outlets including the Daily Mirror, the New York Post, and the Daily Express, could not be found anywhere online. Emails to the agencies went unanswered. The Sun's head of travel told Press Gazette her team had been "inundated with AI releases from what is becoming ever more obvious are invented AI PRs." Still, stories ran anyway.

Two different industries, two different failures, but the same root cause. AI deployed without a governance structure, without accountability, and without anyone 'in the room' who would be responsible for what it did next.

The Hybrid Approach: What is it and What it Isn't

The answer to the governance gap is not to slow down AI adoption. It is to stop treating AI as a replacement for human judgment and start building systems where each does what it actually does well.

A genuine hybrid approach means creating an operational ecosystem where AI handles the volume — routine requests, repetitive tasks, predictable interactions — while human agents focus on what AI cannot reliably do: complex cases and edge cases, where outcomes depend heavily on critical thinking.

What it is not matters just as much. Dropping a chatbot into an existing workflow is not a hybrid model, but a simple feature add-on. And having humans passively monitor AI outputs is not hybrid working either, but just supervision without participation.

In day-to-day operations, the line between "automate this" and "don't automate this" is more specific than most businesses expect. Take a SaaS company. Subscription queries, account access issues, payment questions–these follow predictable patterns, the answers exist, and customers are simply asking because they do not want to search. They are ideal candidates for automation. Customer support vendors like EverHelp, which have already tested the hybrid system, have proven that a well-trained AI agent can handle up to 85% of such tasks at scale, providing instant responses without degrading the customer experience.

Technical issues are a different matter. When a bug surfaces or a server goes down, the product team may not yet know what has happened, let alone have a resolution. An AI agent without that context will follow its training and produce a confident-sounding answer, leading the user in the wrong direction. In a crisis moment, that is not a minor inconvenience. It is the fastest way to permanently lose a customer. And that's the reason why human-routing pathways remain a deliberate feature, as some situations still require the judgment and care that only an agent can provide.

Existing Governance Solutions–What Are They?

The companies that AB tested AI integrations have found that the best way to control it is to program the AI to recognise the boundaries of its own competence and forward those cases to a human. Such a design decision shows how AI governance can look at the operational level–as a system that knows where and when to stop.

At the regulatory level, frameworks are catching up. The EU AI Act became fully applicable in August 2025, making human oversight and risk management mandatory for high-risk AI systems. But though standards set boundaries and frameworks set standards, neither tells a business what to do when an AI agent encounters a situation it was never trained for, and a customer is waiting. That's the place for the real governance work to happen, and in 2026, it can't be left to chance.