From Pilot to Powerhouse: Prayas Lohalekar Turns Agentic AI into Enterprise Mainstays

After more than a decade of leading deep tech programs, Prayas Lohalekar now leads global teams in moving AI agents from trial phases to high-impact, real-time deployment.

Building a Foundation That Withstands Pressure

For over ten years, Prayas Lohalekar has led global initiatives across CRM transformations, automation cycles, and major data rebuilds. These programs not only removed friction from outdated systems but also laid the foundation for a secure, scalable, and autonomous AI deployment within the enterprise.

“We started by fixing everyday bottlenecks,” he explains. “When core systems became reliable, autonomy was the logical next step.”

His early projects eliminated manual workflows and unified fragmented data structures. Once systems became stable and interconnected, executive interest surged around speed and predictive insight.

During a high-stakes trading-platform merger, Prayas brought together engineering, analytics, and risk teams to develop an ‘observability conscience’—a semantic telemetry layer that translated chaotic signals into plain-language narratives tied to revenue, compliance, and customer experience. In early-stage testing, the system flagged seventeen instances of irregular agent behavior. Each issue was resolved weeks ahead of audit deadlines, avoiding regulatory escalations or extended follow-up. “Telemetry shouldn’t be background noise,” he says. “Leaders need to see what matters—when it still matters.”

Building Autonomy with Accountability

As agentic systems grow more capable, static controls no longer suffice. Prayas pushed for a real-time policy engine that calibrates permissions based on behavior and context. High-risk actions trigger immediate scrutiny, while agents that follow stable patterns earn broader access. Each decision includes a recorded rationale, which is logged automatically for audit and traceability purposes.

The results speak for themselves. In its first six months, the engine ran without a single unplanned lockout and was quickly adopted by multiple units, including trading, treasury, and risk. Internal metrics revealed a 2x increase in agent throughput and a 33% reduction in audit preparation time, resulting in savings of dozens of hours per cycle. “Autonomy should grow the same way trust does,” he says. “Gradually, based on demonstrated outcomes.”

Prayas also treats AI delivery as a strategic lever, rather than a series of experiments. Instead of isolated pilots, his approach maps a portfolio of use cases across teams, supported by shared infrastructure, governance, and reusable components. In one case, this method reduced redundant spend by 22% and halved deployment time across three enterprise-grade use cases. “When everyone sees the same dependency map,” he says, “you avoid rework and move faster together.”

Staying Ahead of the Chaos

Anticipating failure isn’t pessimism—it’s prudence. Prayastrains his teams to think beyond the launch date and prepare for volatility from the outset. Agent deployments mature through defined stages—shadow, advise, and act—ensuring controlled progression and operational resilience. Predictive backups stand ready to absorb strain when conditions shift.

That preparation proved its worth last spring when a sudden 30 percent surge in trading volume struck the platform. The agents held their ground, processing every transaction promptly and without disruption. “End users forget smooth launches. They remember outages,” he tells his teams. “Our job is to prevent the outage.”

Building What Others Adopt

The operational models Prayas introduced—observability conscience, autonomy gates, and shared delivery pipelines—have spread far beyond their initial testbeds. More than forty agile teams across three continents now train new hires using these frameworks, which are built into their workflows.Senior managers report faster release cycles, fewer audit burdens, and a culture shift toward reusable systems. “When something keeps saving time,” he says, “people don’t need to be told to use it. They just do.”

These systems didn’t just work in isolation—they’ve become part of the firm’s enterprise architecture playbook, now influencing technology strategies across multiple U.S.-based client portfolios.

Now, Prayas is testing AI ecosystems where meta-agents translate executive priorities, such as reducing latency or settlement risk, into coordinated action backed by clean, auditable logic. Strategy, delivery, and compliance leaders now collaborate early to validate results and pressure-test the outcomes.

“The future isn’t just smarter systems,” he says. “It’s systems that understand what they’re doing, can prove they did it right, and keep getting sharper long after we log off.”

From legacy rebuilds to intelligent agents capable of defending every action they take, Prayas Lohalekar equips enterprise teams with more than tools. He provides them with a tested blueprint to move fast, govern clearly, and build resilience from day one.