The conversation around artificial intelligence is quickly evolving. A new wave of research highlights a critical shift: AI systems are no longer just tools that analyze data and make predictions, they’re beginning to operate as autonomous agents capable of reasoning, planning, and acting on their own. A recent McKinsey report notes that unlocking the full potential of agentic AI will require reimagining workflows entirely, with agents not bolted onto existing processes but placed at the center of them.
To unpack what this means for businesses, governance, and the future of trust in AI, we spoke with Sachin Jain, SVP Technology at Eventus Security Inc, an industry expert who has been closely analyzing how organizations are preparing for this transition. Jain describes agentic AI as the evolution from assistive systems to autonomous actors, and he believes this leap, while powerful, raises urgent questions of accountability and regulation.
“Traditional AI has largely been reactive—classifying data, predicting outcomes, or recommending next steps,” Jain explained. “Agentic AI goes further by reasoning, planning, and taking autonomous actions to achieve goals. Instead of suggesting what a human should do, it can actually execute the task—whether that’s scheduling, processing, or coordinating across systems.”
The safest applications for agentic AI today lie in low-risk, rules-based tasks. Think customer service queries, routine workflow management, supply chain optimization, or automating back-office reconciliations.
But when it comes to areas with direct human impact—like healthcare treatment approvals, fraud determinations, or credit scoring—the line is clearer: human oversight must remain. The path forward, experts argue, will be staged adoption—first in low-stakes environments, then gradually expanding into more sensitive domains.
When AI Gets It Wrong
The question of accountability is already pressing. What happens when an AI agent freezes a bank account, denies a patient service, or blocks a critical workflow?
“The AI itself is not responsible—it’s a tool, not an independent actor,” said the expert. Responsibility lies with the organizations deploying these systems, supported by accountability frameworks involving developers and integrators. Much like businesses remain liable for errors in software or third-party services, they must also own the outcomes of AI-driven actions. Clear escalation paths, audit trails, and redress mechanisms are essential safeguards.
Ensuring accountability, however, isn’t just about handling mistakes—it also depends on having the right regulations and governance frameworks in place.
Current frameworks like the EU AI Act and GDPR provide important guardrails, but they were designed for earlier stages of AI. Agentic systems introduce new challenges around liability, decision rights, and cross-system orchestration.
“Regulation needs to evolve to cover continuous monitoring, mandated kill switches, dynamic risk categorization, and real-time assurance,” the expert noted. Unlike one-time certifications, oversight must be ongoing and adaptable.
A key distinction is also needed between AI that advises and AI that acts. Advisory systems, which surface insights for human decision-makers, carry limited risk. By contrast, agentic AI can alter workflows or affect individuals directly, making tiered governance—similar to how aviation distinguishes between decision-support and autopilot—critical.
Trust as the Defining Barrier
Like any transformative technology, agentic AI must earn trust before it can be widely adopted. Historical patterns show that even powerful tools—from cloud computing to robotic process automation—saw adoption accelerate only after clear evidence of reliability and value.
Though a lot of vendors claim that AI agents could have measurable efficiency gains, reduced errors, and faster turnaround times, independent verification of these results remains limited, and real-world adoption will ultimately depend on building trust and demonstrating consistent reliability.
Sachin Jain echoes this sentiment: “Agentic AI will be widely adopted, but trust will still define the boundaries of its use. Support functions—claims processing, fraud detection, medical documentation—will see the deepest integration first, where efficiency gains are clear and risks are manageable. But in high-stakes decisions like medical diagnoses or loan approvals, humans will remain firmly in the loop.”
Unlike earlier technologies, agentic AI must earn trust not just for reliability, but for its autonomous decisions that can directly impact people, processes, and compliance.
