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Would you give a random person on the internet access to your laptop, emails, internal tools, or customer data? Probably not. Yet this is exactly the level of access many companies and individuals are beginning to grant AI agents, often without fully understanding the implications or laying the groundwork.
Agentic AI's "boom" was something rather expected in 2026 - our CTO highlighted it as one of the trends at the beginning of the year. Their increasing popularity, which somehow began with the wave of OpenClaw hype, is now flooding the market with AI-powered agents. Even big AI players are shifting their focus to agentic assistants – Anthropic is expanding Claude Code and Cowork into different industries, OpenAI has decided to pull the plug on Sora in favour of Codex, Microsoft is working to integrate similar features into its existing Copilot tool, and Google is building their own AI agent.
At this point, almost anyone with some technical knowledge (or just enough determination) can build an agent that can support them in various ways, from basic tasks like managing their email inbox to more specialised ones, deeply integrated into their professional workflows. Yet, is it really safe?
It’s not about questioning the power of AI agents, but asking: how much access are you willing to give and under what conditions?
Just like real users, AI agents can take action and make decisions. As autonomous systems, backed by Large Language Models (LLMs) as the core and artificial intelligence, they act on behalf of the users to achieve specific, complex goals. Unlike other traditional AI tools like ChatGPT, Claude, or Gemini, they don’t wait for a prompt but act proactively. This means that AI agents can act independently, choose execution paths, and make decisions in real time, without asking for permission.
But how do they do that? AI agents use “tool calling,” which, in practice, means they don’t have to rely on pretrained knowledge but can search for information on the fly or extract it from external resources. They are also able to learn and adapt to users’ expectations over time. All thanks to “memory” - where they can store past interactions, which, as a result, enables agents to personalise their future responses.
The extent of actions expands beyond just writing/generating text. AI agents can:
Yes, they can speed up many workflows and empower employees in repetitive tasks. Many individuals see them as personal assistants, creating multiple agents, each handling a different specialisation and supporting them in specific tasks.
But AI agents should be treated as “high-risk tools” – ones that need strict instructions, guidelines, and human oversight to prevent them from burning your budget, damaging your image, or, worse, exposing your business to compliance or security risks.
We have already seen or heard of situations when AI agents went rogue, from deleting the production database and trying to hide it to lying to customers, or approving buggy code. In fact, 88% of organisations confirmed or suspected AI agent security or privacy incidents in the last year.
These risks are based on real cases; therefore, leaders and organisations planning to utilise AI agents in their workflows should set clear boundaries and limitations before writing the first prompt. And be aware of the risks that come with AI agents.
Traditional software is deterministic, which means the same input should produce the same output every time. But AI agents don’t work that way – the same request can produce completely different responses, reasoning paths, or actions (which is also true for AI tools in general).
It all depends on things like:
As a result, AI agents are fundamentally harder to predict or test than traditional systems. In low-risk environments, this may be acceptable. However, in business-critical or highly sensitive, compliant clinical workflows, it can be a serious concern.
For instance, in 95% of cases, an agent can correctly classify a support ticket. Still, in the remaining 5%, it may route sensitive information incorrectly, triggering unintended actions or even exposing sensitive data to third parties.
This creates a new challenge for leaders: they are no longer managing only software logic but also probabilistic, unpredictable AI agents that have a real impact on operations.
Observability is one of the biggest operational challenges. In traditional systems, when something fails, teams can trace back the logic – execution rules, which conditions failed, or where exactly the error occurred. But AI agents are far less transparent.
The AI Agent Index, a project featuring researchers from MIT and Stanford, among others, found a “significant transparency gap” – with only a few of the available agents having any formal safety or evaluation documentation.
In many cases, it’s unclear how agents prioritise different actions, what’s behind the tools they use, or why they ignore or misinterpret specific instructions. And that lack of visibility makes debugging harder, auditing more complex, and accountability significantly weaker. It also poses an ethical risk as it’s challenging to assess a model’s trustworthiness.
For companies operating in regulated environments, limited auditability equals a governance and compliance problem.
Excessive access and broad permissions are among the most overlooked and underestimated issues. Many AI agents can access internal systems, external APIs, private/professional inboxes, or customer databases. And unrestricted or limited access, combined with probabilistic behaviour, creates unnecessary exposure.
AI agents can retrieve sensitive information they shouldn’t have access to in the first place, perform actions outside their intended roles, or become vulnerable to prompt-injection attacks. The safest implementations follow the same principle used in cybersecurity for years: least privilege access.
For example, using an AI agent to optimise short-term investments without a strict, non-negotiable spending limit or approval thresholds means it’s practically free to spend any amount of budget based on perceived ROI signals or optimisation plans. This poses a significant financial risk, as overspending or misallocation can occur quickly. Not to mention compliance risks due to a lack of auditability or predictable financial oversight.
Agents should have access only to the data and tools required to do their tasks and permission to perform a limited set of actions. Otherwise, organisations can become vulnerable to security and compliance risks.
AI agents can deliver tangible business value - but usually not in the fully autonomous scenarios often presented online. The strongest use cases include environments with controlled access, observable actions, and, most of all, human oversight.
Internal copilots are currently one of the most effective implementations. Helping teams search documentation, summarise information, or support internal workflows can create measurable productivity gains without introducing operational risk. The same applies to read-only analytics assistants that can help teams gain insights faster without letting the agent modify systems or trigger actions.
Software development is another area. AI agents can assist with code generation, debugging, or documentation work because engineering teams already have the needed processes in place (including review, testing, and deployment). They can speed up execution, but engineers must validate the outcomes before anything reaches production.
The pattern is consistent across successful implementations: the more controlled the environment, the more reliable the business value is.
So, the question is - how can using AI agents expose businesses to vulnerabilities? The rule might be as simple as that: if a potential mistake can be expensive, irreversible, or hard to detect, AI agents shouldn’t operate alone.
In high-risk scenarios, including financial operations, management of real patient data, or autonomous decision-making using AI agents, strict human oversight is required, especially in compliance-heavy industries. If not a complete ban, for now.
More autonomy doesn’t automatically mean more efficiency – the opposite is frequently true. The broader the access and the higher the independence, the greater the need for governance, monitoring, and safeguards.
That’s why the most mature AI agent strategies are focused on augmenting teams, reducing operational friction, and introducing automation gradually within clearly defined boundaries.
The biggest mistake companies make with AI agents is treating them like fully reliable systems from day one. In reality, they should be approached more like “high-risk tools” with access to company tools and data: capable, but still requiring boundaries and supervision.
The safest implementations usually start with limited access and clearly defined responsibilities. There’s a major difference between an agent that can read information and one that can trigger actions, modify records, or interact with external systems.
Therefore, human oversight remains critical, especially in workflows involving customers, finances, or sensitive data. Even advanced AI agents can behave inconsistently, misinterpret context, or make incorrect decisions.
That’s why mature implementations focus on guardrails:
Chasing maximum autonomy is not the right path with AI agents. Instead, companies that build control, visibility, and governance into the system from the outset are the ones that can see the best results. And can mitigate risks.
The hype around AI-powered technologies slightly outweighs the reality. Of course, they can deliver measurable benefits for businesses, but only under strict oversight and boundaries. Rushing is neither the most effective nor the safest way to implement or develop these tools; the key is to establish a safe environment where AI agents can’t cause damage.
More access requires more monitoring and safeguards, which can introduce greater complexity. Because building an AI agent is just the starting point, what comes next is ensuring they don’t put your business at risk.
So, the question “how much access are you willing to give and under what conditions?” remains critical when introducing AI agents into workflows, whether professional or private.
