New Delhi, Jan. 7 -- Agentic AI marks a new phase in enterprise technology transformation, where intelligent systems act autonomously, gather relevant data, and make business decisions without constant human intervention. These specialized AI agents are already reshaping industries, from automating compliance and fraud detection in banking to managing multilingual customer interactions at scale.
Air India, a pioneer in India's aviation history, is leveraging Databricks to drive its significant digital transformation. By deploying its customer-facing AI agent, 'Maharaja,' the airline is now managing thousands of daily customer inquiries across multiple domains and languages. This not only frees up the team to address more complex issues but also enables Air India's leadership to gain instant, data-driven insights into internal operations, from flight performance to on-time statistics.
Yet, business leaders still question: can agents truly deliver ROI? The answer lies in practical automation - reading PDFs, streamlining onboarding, and managing compliance - not in superintelligence.
India's opportunity
India's AI adoption is accelerating, with nearly 30% of enterprises already deploying AI - above the global average. Government initiatives such as NITI Aayog's #ASIoforAll strategy and Digital India, coupled with a strong developer base and data-rich industries, are propelling large-scale adoption in areas like KYC automation, document summarization, and multilingual bots.
Backed by a rapidly expanding AI market projected to reach $17B by 2027, India is poised to lead in domain-specific agentic AI, building enterprise-grade solutions for both domestic and global markets.
Key challenges in deploying AI Agents and how to solve them
Despite their promise, production-grade AI agents face tough hurdles. Indian enterprises struggle to measure quality and ROI without specialized evaluation tools, while agent complexity often hinders rapid iteration, leading to slow trial-and-error processes. Balancing cost, quality, and compliance is especially challenging in India, given its multilingual regulations and data governance requirements, making robust frameworks essential for achieving real-world impact.
To address these challenges, enterprises need to:
1. Set clear objectives and robust guardrails to ensure AI agents operate safely and avoid producing irrelevant or harmful outputs that may compromise customer satisfaction, compliance, or brand reputation. 2. Auto-evaluate AI agents using LLM judges and human oversight, thereby eliminating the need for manual tuning and complexity. By establishing clear KPIs and metrics, such as response accuracy and user satisfaction, enterprises can confidently measure and improve agent performance over time. 3. Auto-optimize AI agents with adaptive orchestration platforms that pick the best models and tools for each task, balancing cost, quality, and scalability, and reducing tedious manual adjustments. 4. Strengthen data governance with robust measures such as audit logs, role-based access controls, and compliance-ready workflows. This is especially crucial for security and multilingual regulatory requirements in India.
Choosing the right tools is crucial. Solutions like Agent Bricks, Databricks' flagship AI offering, automate agent development by translating natural language guidance into technical optimization, automatically generating robust evaluation suites and fine-tuning agents for both quality and cost. This eliminates guesswork and trial-and-error, empowering teams to focus on strategic goals and enabling the fast deployment of trustworthy, production-ready agents with clear, measurable business impact.
The future: Business-oriented industrial-grade agents
Agentic AI is no longer just a dream; it is now delivering real business value for India's enterprises, powering everything from multilingual customer service for 1.4 billion people to advanced banking and risk scoring. India stands at a pivotal point in the global agentic AI revolution, but leadership demands more than swift adoption - it requires building agents that are open, ethical, compliant, and designed for continuous improvement.
Unlocking the full potential of agentic AI will require responsible, scalable, and continuously improving tools. This enables businesses to implement solutions that deliver significant, long-term impact - helping them stay ahead of the competition and turn their AI investments into a winning innings that lasts.
Published by HT Digital Content Services with permission from TechCircle.