Long before AI dominated headlines worldwide, a young Utkarsh Contractor in 8th grade stumbled upon a simple chatbot named Eliza. That brief interaction with what felt like a sentient computer program sparked a lifelong fascination. “It was love at first sight, I remember clearly it was the time my dad got me my first computer, a portal into a world of limitless possibilities” he recalls, “and I dreamt of a world beyond the Turing Test, where machines could be a human’s best friend.”
That dream matured into a career that’s been as expansive as it is impactful. After earning a degree in Computer Engineering from Bombay University and working on large scale software engineering systems at AT&T and Logitech, Utkarsh played a pivotal role in LinkedIn’s hypergrowth phase – experiencing firsthand the complexities of large-scale data, information retrieval and distributed Machine Learning systems, helping shape LinkedIn’s Search and Enterprise AI ecosystem and scale it from millions to billions of users. His journey later led him to Stanford University, where he specialized in Large Language Models (LLMs), multimodal reasoning and reinforcement learning systems. It was here, during a poster session, that he met the founders of Aisera.
“I always wanted to build an AI startup in a founder capacity,” Utkarsh Contractor shares. That opportunity arrived in the form of Aisera, where he joined as Head of AI and grew into the role of CTO. Over the past 7+ years, he’s helped scale Aisera from a team of six to over 250 innovators globally – pioneering a bold new era of Agentic AI for the enterprise.
Defining a New Era: What Is Agentic AI?
Before Agentic AI, enterprise automation was largely reactive and fragmented. Traditional AI systems excelled at narrow functions – retrieving information, executing scripts, surfacing predictions – but they lacked memory, adaptability, and the ability to take initiative(agency). These systems waited for input, operated in silos, and typically required human intervention to connect the dots between intent and outcome. The promise of AI was real, but its execution often left organizations with a patchwork of assistants, bots, and copilots that solved pieces of problems rather than delivering end-to-end value.
Enter Agentic AI – a new generation of intelligent systems designed not only to assist but to act. Unlike their predecessors, agentic AI systems are built to be autonomous, context-aware, and goal-driven. These are not passive responders; they are proactive collaborators. Agentic AI can reason over multi-modal inputs, adapt to dynamic workflows, and carry out complex, multi-step tasks without hand-holding. It doesn’t just answer a query—it solves the underlying problem, often before the user even realizes it exists.
“Where traditional AI retrieves information, agentic AI reasons and acts,” Utkarsh Contractor explains. “It anticipates needs, automates tasks from start to finish, and learns continuously.”
Augmentation Over Replacement: The Human-Centric Philosophy Behind Agentic AI
At the core of Utkarsh’s AI philosophy lies a powerful conviction: artificial intelligence was never built with the goal to replace humans – it was designed to elevate humans to achieve their highest potential. This belief is more than a design choice; it’s a guiding principle that influences how AI is researched, built, and deployed across Aisera’s customers.
“Too often, the narrative around AI is centered on replacement – on what jobs it will eliminate,” Utkarsh says. “The real opportunity is in amplification—helping people do work they couldn’t do before, or do it better than they ever imagined. The most powerful applications aren’t about machines doing human work—they’re about humans doing superhuman work.”
Agentic AI, in this vision, becomes a force multiplier. It handles the repetitive and the rote, so humans can invest their time and cognitive bandwidth in problem-solving, strategy, and human connection. It’s not about supplanting decision-making – it’s about giving every employee a digital teammate to help them think faster, act smarter, and accomplish more.
This mindset isn’t just a philosophy for his team – Utkarsh Contractor models it in his own daily work. Despite being CTO, he remains actively engaged with engineering and writes code almost every day. Thanks to AI-powered tools like Cursor, he’s increased his own coding output tenfold. “I write 10x more code now because of AI,” he shares. “It’s like having a “digital twin” that accelerates thinking, clears roadblocks, and lets me focus on creativity.”
Building an Agentic Enterprise: How Aisera is Leading the Charge
Aisera enables businesses to deliver transformative work experiences, boost employee productivity, and reduce operational costs with its award-winning AI agent platform.
At the core of Aisera’s platform is a comprehensive system of AI agents that enables organizations to build, deploy, and manage both Aisera-native and third-party agents. These agents autonomously coordinate decisions, orchestrate processes, and execute tasks at scale – reducing manual effort and accelerating business outcomes.
The architecture of Aisera’s platform mirrors the structure of an expert support team:
- Universal Agent: Acts as the cognitive gateway, understanding incoming user requests and tickets, determining intent, and coordinating the most efficient resolution across a system of agents.
- Domain Agents: Specialized agents for IT, HR, finance, customer support, and more. They bring domain-specific intelligence to deliver highly accurate, context-aware responses that adapt to the workflows they serve.
- Task Agents: Focused executors of routine business functions – like processing expense approvals, submitting procurement requests, or booking facilities – integrated seamlessly with systems such as ServiceNow, Workday, and Microsoft 365.
To ensure secure, seamless, and scalable communication between agents, Aisera is at the forefront defining and driving industry standards with Anthropic by contributing to the Model Context Protocol(MCP) community and maturing the Agent-Tool ecosystems to standardize Agentic AI standards, partnered with Google to contribute to protocols such as Agent to Agent(A2A) to further Agent interoperability and Cisco to build and continue to guide open source collectives such as AGNTCY for inter-agent collaboration.
Together, these innovations allow enterprises to orchestrate autonomous decision-making and intelligent execution across diverse systems, delivering results with speed, consistency, and operational clarity.
From Research to Real-World Impact: The Academic Foundation Driving Innovation
Much of Aisera’s innovation stems from Utkarsh Contractor’s deep academic foundation and his unwavering commitment to bridging the gap between research and real-world use cases. As a Senior Research Fellow at Stanford University, Utkarsh is very actively involved with applied research and experimentation at the School of Engineering and the Graduate School of Education that directly addresses real-world challenges.
His work spans from developing AI applications for educational environments to creating agentic AI systems that empower the modern workplace—all anchored in a philosophy of responsible, human-centered artificial intelligence. Utkarsh’s approach reflects a fundamental belief that meaningful AI innovation requires more than computational scale; it demands rigorous evidence-based research, deep domain expertise, and disciplined engineering practices.
This research-driven methodology culminated in a groundbreaking achievement: the development and publication of the industry’s first comprehensive Agentic AI benchmarking study. The research, accepted at ICLR 2025—one of the field’s most prestigious conferences—introduced a novel evaluation framework that holistically measures AI agent performance across enterprise use cases. Beyond the theoretical contribution, the study delivered practical value through the release of accompanying datasets, models, and open-source code, establishing new standards for how organizations can assess and deploy AI agents in real-world business environments.
Through this blend of academic rigor and practical application, Utkarsh Contractor continues to bridge the critical gap between lab research and enterprise-ready solutions, positioning Aisera at the forefront of enterprise AI innovation.
At the heart of the study is a core insight: domain-specific agents significantly outperform generic agents built solely on large language models (LLMs) when it comes to real-world enterprise outcomes.
Why does this matter? Because general-purpose LLMs – while incredibly powerful in open-ended tasks – are not inherently optimized for the structured, compliance-heavy, and nuanced workflows that define IT, HR, finance, and customer support functions. These environments demand agents that understand the specific terminology, context, security protocols, and business logic unique to each domain.
“Relying solely on general LLMs is like asking a generalist to perform surgery,” Utkarsh explains. “You need someone who understands the domain deeply – its edge cases, jargon, systems, and constraints. That’s where domain-specific AI excels.”
To quantify this advantage, the benchmarking study introduced a five-pillar evaluation framework – known as the CLASSic framework – measuring agent performance across:
- Cost: Efficiency in compute and infrastructure utilization
- Latency: Response time under enterprise-grade workloads
- Accuracy: Resolution precision in task completion and intent recognition
- Stability: Consistent performance across diverse environments
- Security: Robustness against policy violations and unauthorized data access
Across every category, domain-specific agentic architectures proved not only more reliable, but significantly more aligned with enterprise-grade expectations.
“These breakthroughs embody what excites me most,” Utkarsh reflects. “We’re building AI agents that are not merely state of the art – but truly transformational and deliver real-world business outcomes. They turn perennial support headaches into friction-free experiences and let people focus on the work that matters.”
This pioneering research has become foundational to how Aisera designs, measures, and evolves its AI agent platform – and reflects Utkarsh’s broader mission: to ground bold innovation in academic integrity, evidence based research and practical relevance. It’s this unique fusion of vision, research, and execution that keeps Aisera at the forefront.
With Great Power Comes Greater Responsibility: Building AI You Can Trust
As AI systems gain more agency and are embedded in everyday business decisions, the stakes for building them responsibly have never been higher. For Utkarsh Contractor, the ethical and responsible foundation of AI is not just a checklist – it’s a core pillar of innovation. His belief is simple but profound: “If AI is to help humanity, it must first align with human values”.
“AI safety and alignment are the next frontier”, Utkarsh Contractor says. “We’re no longer just trying to build systems that can think and act – we’re trying to build systems that should think and act in the right way that aligns with human values and in the best interest of humanity.”
This philosophy is reflected in every layer of Aisera’s development and deployment processes. Central to that approach is TRAPS – Aisera’s governance framework that ensures its platform is Trusted, Responsible, Auditable, Private, and Secure. TRAPS enforces rigorous ethical standards across model architecture, data usage, and customer-facing interactions.
Building on this foundation, Aisera introduced TRAPS+, an enterprise-grade extension that equips organizations with advanced guardrails to build, deploy, and scale AI agents confidently. TRAPS+ ensures:
- Full control and visibility over customer data
- Strict privacy-by-design enforcement
- Built-in audit trails for every agent decision
- Data isolation by default, with no reuse of customer data for LLM training
Aisera’s AI agents are not black boxes. They’re built with explainability in mind – able to cite their reasoning, link to knowledge sources, and provide transparency in decision-making. This ensures users don’t just get answers, they understand how and why decisions are made.
Fairness is another critical area of focus. Utkarsh’s teams train their models on diverse datasets, apply bias-mitigation strategies, and implement internal ethical review protocols that challenge the fairness, compliance, and impact of each AI-driven recommendation. “When AI makes a decision,” he explains, “we ask ourselves: Is it fair? Does it respect access rights? Would we trust this outcome if it affected us? Can we explain the outcome?”
The commitment to ethical AI extends deeply into data privacy and security. Aisera’s platform adheres to strict compliance standards such as GDPR. Customer data is anonymized, encrypted, and processed only with explicit approvals – ensuring that trust is preserved not only through outputs, but through the entire data lifecycle.
Turning Friction Into Fuel: Leading Through the Challenges of AI Innovation
For Utkarsh Contractor, leading AI innovation isn’t just about staying ahead—it’s about thriving at the edge of uncertainty. As the field of artificial intelligence continues to evolve at breakneck speed, he’s faced four persistent and interconnected challenges: pace, performance, proof, and product-market fit. But rather than treating these as roadblocks, Utkarsh sees them as opportunities to sharpen the edge of Aisera’s innovation engine.
1. Pace of Change
“AI reinvents itself every quarter,” Utkarsh says. “What’s cutting-edge today could be outdated tomorrow.” To navigate this, he’s cultivated a learning-obsessed culture at Aisera. Engineers participate in recurring “innovation sprints” where they read the latest research, prototype experimental ideas in sandbox environments, and demo their findings every two weeks. His academic ties – from research at Stanford to ICLR publications – ensure that new discoveries flow straight from academia into real-world applications.
2. Performance in the Wild
Even the best AI models can falter when exposed to the messiness of enterprise data. “Controlled test environments are not the real world,” Utkarsh notes. Aisera tackles this with feedback loops powered by humans in the loop, rapid retraining, and systems that adapt to evolving terminology, workflows, and edge cases. This agility is especially critical in enterprise domains like IT and HR, where contextual accuracy is everything.
3. Proof & the Measure of Good
“We live by the maxim: ‘What you can’t measure, you can’t fix,’” Utkarsh Contractor says. At Aisera, transparency isn’t optional – it’s engineered into the product. Every deployment includes live dashboards that track AI performance against customer-specific KPIs, turning abstract capabilities into measurable ROI. These benchmarks provide a clear line of sight from AI deployment to business value.
4. Product-Market Fit in a Shifting Landscape
“Markets evolve, technologies evolve, and so must your product,” Utkarsh explains. Aisera runs continual user-feedback cycles, stays close to customer needs, and isn’t afraid to pivot—or even retire—features that don’t deliver value. “Staying humble is critical. You can’t fall in love with your own ideas,” he says. “In a landscape as volatile as AI, self-awareness is a superpower.”
Open Letter from Utkarsh Contractor
CTO, Aisera
To every new leader, aspiring founder, and curious mind stepping into the AI world—this moment is yours.
The most interesting work isn’t happening in the obvious places—it’s at the intersections, where disciplines merge and people refuse to accept “that’s just how things are done. If I’ve learned anything, it’s this: don’t just tap on the glass ceiling—break it. Don’t just push against boundaries—redefine them entirely. But don’t forget: boldness without grounding is noise. Every line of code, every experiment, every model you ship—build it on first principles.
Yes, I love prototyping on a hunch. I still do it. But real innovation only sticks when you deeply understand why something works. Ask harder questions. Obsess over fundamentals. Measure impact, not just metrics.
AI isn’t here to replace people—it’s here to replace paradigms. Your job is to pair your domain expertise with intelligent tools to amplify and unlock human potential we didn’t know existed.. Start small. Move fast. Learn faster. Every major product I’ve launched began as something scrappy. Iterate until the insight shines through.
And build with others. Line of Business, Ethicists, engineers, designers—this is a team sport. Fairness, privacy, and transparency must be foundational, not optional.
Stay curious. Stay principled. Stay hungry. The world doesn’t just need AI experts. It needs responsible leaders who dare to imagine better.
— Utkarsh Contractor
CTO, Aisera
In a world flooded with AI hype, Utkarsh Contractor is building what matters. And in doing so, he is leading a quiet revolution that’s transforming enterprises from the inside out.
