Agentic AI is no longer theoretical. These autonomous systems don’t just follow prompts, they pursue goals, make decisions and execute actions with minimal human oversight.
According to Deloitte, 25% of companies using generative AI will pilot agentic systems in 2025, rising to 50% by 2027. Adoption is already ahead of schedule: a Techstrong survey found 72% of tech leaders say their organisation is actively using agentic AI today.
This acceleration presents a serious governance challenge. As UN Secretary-General António Guterres warned, “The runaway development of AI without guardrails is deeply concerning”. When these agents act unpredictably or dangerously, the fallout isn’t theoretical.Business leaders must accept a new reality: autonomy without oversight creates business risk at scale. As Microsoft’s Satya Nadella put it, “We have to take the unintended consequences of any new technology… and think about them simultaneously with the benefits”.
What separates innovation from liability isn’t the intelligence of the AI, it’s the strength of the governance behind it.
Most AI tools today respond to prompts, wait for instructions and then act. Agentic AI is different. It operates with intent. These systems are designed to pursue objectives, take initiative and adapt without needing constant direction.
This shift is significant. As Deloitte explains, agentic AI refers to autonomous software agents that “complete complex tasks and meet objectives with little or no human supervision.” These tools go beyond answering a query and will act on your behalf, sometimes across multiple systems and steps.
The vision isn’t hypothetical. According to Bill Gates, “Agents are smarter. They’re proactive – capable of making suggestions before you ask for them… They accomplish tasks across applications [and] improve over time because they remember your activities and recognise patterns.”
In business terms, an AI agent might draft an email and also decide who to send it to and when, based on historical context. Or it might autonomously adjust cloud infrastructure in response to usage trends or re-route logistics after detecting supply chain disruption.
For leaders, the key shift is this: agentic AI introduces autonomy into once deterministic environments. It allows systems to make judgement calls. And that means organisations aren’t just adopting automation - they’re handing over control and adopting autonomy as well. This amplifies both the opportunity and the risk.
Autonomy brings speed, scale and adaptability, but it also introduces a margin of error that’s no longer human. Agentic AI can misinterpret intent, overstep boundaries, or escalate situations without oversight. When these systems act, they don’t pause to ask for permission, making the risks immediate and potentially (very) expensive.
Even a small miscalculation by an AI agent can spiral into chaos. Without intervention, it can execute flawed decisions faster than any human team could react.
In 2012, a defect in an algorithm at Knight Capital triggered $440 million in losses in under 30 minutes by placing thousands of erroneous trades, an incident that nearly bankrupted the firm. Now, although this example isn’t related to agentic AI, it shows how easily a mistake can cost a business a lot of money.
The pace of AI is part of the problem. With agentic AI, there’s often no time to course-correct once something goes wrong.
When a human makes a bad decision, responsibility is clear. With agentic AI, it’s murky. These systems learn, evolve and act on internal logic that may be difficult or impossible to explain. This “black box” behaviour makes legal responsibility harder to assign.
According to the National Law Review, an AI system’s autonomous actions increase the risk of harm while making it “difficult to trace” the root cause and “hold any party accountable”. Yet regulators are beginning to respond. The emerging consensus is that organisations, not the algorithms, will be held liable.
AI agents don’t have judgment or ethics, they operate on logic and training data. That means the way they act can be biased, intrusive, or tone-deaf without realising it. And when this happens, the reputational damage can be swift.
75% of chief risk officers say AI use already poses a risk to their organisation’s reputation. These failures can range from discriminatory recruitment algorithms to customer service bots that issue inappropriate responses. Once public, even small lapses can erode trust, trigger investigations, and damage brand perception.
Agentic AI often needs access to sensitive data and systems to operate effectively. That makes it a tempting target and a potential insider threat. In 2023, Samsung engineers inadvertently leaked confidential source code by pasting it into ChatGPT prompts. The AI didn’t steal the data, but the mechanism created new risk.
This type of exposure is more common than many realise. A recent study conducted by Harmonic Security found that in the last quarter of 2024, 8.5% of prompts made in GenAI tools contained sensitive data, including customer data, billing and authentication information, employee and payroll data and more. Without oversight, agentic AI could trigger similar breaches, whether through human error, misconfigured access, or autonomous decisions.
Agentic AI isn’t just a technical risk, it’s a business risk. When these systems fail, they don’t fail quietly. They create disruption, financial loss, reputational fallout and regulatory scrutiny. And in many cases, it happens faster than leadership can react.
A clear example is Zillow’s AI-driven home buying programme. The model wasn’t fully agentic, but it was highly automated. Zillow’s algorithm misread the signals when the housing market shifted and purchased thousands of overvalued homes.
The company lost nearly $500 million and had to shut down the initiative, laying off a quarter of its workforce. As CEO Rich Barton admitted, the algorithm had a “high likelihood… of putting the whole company at risk”.
The worrying part? Many businesses aren’t prepared for this kind of fallout.
A 2024 PwC survey found that only 58% of executives had assessed the risks of AI within their operations, despite widespread adoption. This gap between enthusiasm and readiness means many companies are exposing themselves to unnecessary risk, including:
AI mistakes don’t just harm IT systems, they disrupt the entire business. And without governance, the damage can be difficult to contain.
Agentic AI doesn’t need to be feared, but it does need boundaries. Business leaders must treat autonomous systems like any other high-stakes function by governing them, monitoring them and holding them accountable. The following five actions offer a practical framework for staying in control.
Not every decision should be left to an AI. Set clear rules about what agentic systems can do autonomously and where human approval is mandatory. This includes defining thresholds for actions, such as financial limits, access controls, or escalation triggers.
As Gartner’s Svetlana Sicular puts it, AI governance is “the process of assigning and assuring organisational accountability, decision rights, risks, policies and investment decisions for applying AI.” That starts by codifying the limits, because what your AI can’t do is just as important as what it can.
Real-time visibility is essential. If an agentic system goes off-track, you need to know immediately. That means continuous monitoring, alerting on anomalies and logging every action the AI takes.
PwC stresses that AI risk management must be “woven into every step of developing, deploying, using and monitoring AI-based technologies.” Use dashboards, watcher agents, or human review to maintain control over the AI’s behaviour before it causes damage.
It’s not enough to know what your AI did. You need to understand why. That means designing for explainability from the outset, ensuring every decision can be traced back to inputs, rules, and logic.
A 2024 McKinsey survey found that 40% of companies saw a lack of AI explainability as a major barrier to adoption. In regulated sectors, being able to explain an AI’s decision could be the difference between compliance and violation. Build audit trails, decision reports and oversight into every AI workflow.
Every agentic AI system should have a named owner - someone accountable for its operation, oversight and outcomes. Yet in many businesses, this role doesn’t exist, leaving a dangerous gap when something goes wrong.
In the public sector, the U.S. Government now mandates that all federal agencies appoint Chief AI Officers and submit governance plans. The private sector should follow suit, whether through formal roles or cross-functional oversight teams.
Don’t wait for a failure in production. Test your AI in safe environments, simulate edge cases, probe for unexpected behaviours and red team the system before deployment. This reveals vulnerabilities that typical testing may miss.
Big tech firms like Google and OpenAI have invested heavily in AI red teaming to uncover model weaknesses. Businesses should adopt a scaled-down version: sandbox new agents, simulate worst-case scenarios, and only grant full autonomy once the system has proven safe under pressure.
Aztech IT provides the core infrastructure, monitoring, and compliance services businesses need to govern agentic AI effectively. From 24/7 oversight via our Security Operations Centre to strict access controls through Identity and Access Management, we help organisations control what their AI systems see, do and touch.
Our cloud governance consulting ensures AI workloads are built and tested in isolated, policy-driven environments, protecting production systems from unintended actions. Through Compliance-as-a-Service, we align AI behaviour with GDPR and sector-specific regulations, helping clients avoid costly breaches of data privacy or accountability.
We also conduct AI-specific risk audits, reviewing system architecture and failure modes before agents go live. Our readiness programmes and Microsoft Copilot workshops guide teams through safe, staged adoption, user training and oversight, blending it into one practical roadmap.
Whether you're piloting agentic tools or tightening controls around existing automation, Aztech gives you a clear path to harness AI safely, without sacrificing governance, compliance, or control.
Agentic AI is powerful. It can make decisions, take action and deliver outcomes faster than any team could manage on its own. But that same autonomy makes it dangerous when left unchecked. Without clear rules, real-time oversight and responsible ownership, these systems can cause damage faster than you can respond.
The stakes are high. As Satya Nadella warned, “I don’t think the world will put up anymore with any of us coming up with something [in AI] where we haven’t thought through safety, equity and trust”.
Governance isn’t bureaucracy. It’s the foundation of safe innovation.
The good news is that businesses that take governance seriously gain protection and the confidence to scale AI responsibly, knowing it won’t undermine the business they’re trying to build.
With the right playbook - clear boundaries, monitoring, explainability, ownership and rigorous testing - agentic AI can be a competitive advantage, not a liability.
If your organisation is using - or planning to use - AI, now is the time to act. Don’t wait for the unintended consequences to show up. Govern your AI before it governs you.