The artificial intelligence of recent years is no longer just a recommendation engine for what to watch on Netflix. It’s evolving into something far more powerful and complex: autonomous agents—digital entities that don’t just analyze or respond but take real-world action.
These AI agents are digital delegates: they receive a task, navigate complex environments, perform sequences of actions, learn as they go, adapt to changes, and interact with systems or people in real-time. Functionally, they behave like humans—only faster, more scalable, and always "on."
Their capabilities are rapidly expanding. An agent can now process incoming emails, prioritize them by urgency, write personal or draft responses, schedule meetings, book flights, generate presentations, draft contracts, and even conduct document reviews. In healthcare, such agents might analyze medical imaging, cross-reference lab results with up-to-date research databases, and recommend physicians by specialty, location, and cost.
One remarkable example is a healthcare agent based on GPT-4, designed to assist chronic patients. It integrates with medical records, asks patients natural-language questions, tracks medication adherence, flags anomalies for doctors, and even schedules appointments based on the healthcare provider’s calendar. For elderly or digitally challenged patients, it serves as a tireless, patient, and always-available personal assistant.
But herein lies the danger. If the data an agent relies on is flawed, partial, or biased, it can draw the wrong conclusions and act accordingly. Unlike humans, AI agents lack natural judgment, intuition, or moral boundaries. They only know what they’ve been taught.
One incorrect data point, one missing entry, or one unreliable source can derail the entire chain of actions. Worse, the agent will proceed with full confidence, creating the illusion that its recommendation is correct, even when it’s dangerously wrong. This becomes particularly risky in sensitive sectors such as healthcare, law, security, or finance, where trust is critical.
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That’s why two layers of intervention are crucial. The first is technical: systems that monitor data quality—checking for consistency, detecting anomalies, assessing certainty levels, and recognizing context. The second is regulatory: setting clear limits on what agents can do independently, ensuring they augment human judgment rather than replace it.
Ran BronsteinPhoto: CourtesySeveral companies are already building this much-needed layer of protection around AI agents:
• Upriver monitors agents in real-time, detects irregularities or data issues, and intervenes immediately to ensure safe, reliable behavior.
• Foundational focuses on data quality—cleaning, verifying, and tracking bias—to ensure that agents operate on accurate, standardized input.
• AIM specializes in securing agents that operate in sensitive environments like healthcare and defense, ensuring they follow strict operational protocols.
• Prompt Security protects the instructions agents receive, preventing manipulations like prompt injections that can corrupt actions or leak sensitive information.
Still, AI agents are not the enemy—they are a massive opportunity. They save time, optimize workflows, improve decision-making, and expand human capability. They can work while we sleep, monitor dozens of variables simultaneously, and get better the more they know us.
But precisely because of this potential, the responsibility for data integrity, transparency in decision-making, and operational oversight is more critical than ever. In a world where agents act at superhuman speed, the quality of the foundation they rely on will determine how far—and how safely—we go.
Ran Bronstein is an entrepreneur, advisor, and investor, co-founder of Simbionix, with exits totaling $420 million, Partner to AI and innovation-driven startups