TLDR: Over 100 agentic AI startups have launched in India within the last two years, focusing on systems that perform autonomous actions. While enterprise applications show promise, consumer adoption faces significant challenges due to low demand, retention issues, and a lack of integrated infrastructure. Despite these hurdles, spending on agentic AI in India is projected to grow from USD 2.1 billion in 2023 to USD 10.4 billion by 2028, with enterprises reporting productivity gains but also grappling with data, infrastructure, and talent challenges.
India has witnessed a remarkable surge in its artificial intelligence landscape, with over 100 agentic AI startups emerging in the country within the past two years. These innovative companies are dedicated to developing AI systems capable of understanding prompts and executing autonomous actions, ranging from intelligent assistants to sophisticated workflow automation tools. This rapid growth positions India as a significant player in the evolving global AI market.
According to data from Tracxn, there are currently 109 active agentic AI companies in India. These startups are building tools that can not only generate text or images but also act on behalf of users, completing tasks, automating workflows, and mimicking decision-making processes. On paper, this represents a futuristic vision for AI integration.
However, the practical reality reveals a significant divide between enterprise and consumer adoption. While there is a clear demand for agentic AI solutions such as coding copilots and workflow agents for autonomous QA testers in startups and large enterprises, direct-to-consumer products face minimal demand. Despite claims of a consumer boom by some startups, reliable data to substantiate this is scarce, and concerns around retention and monetization persist in the Indian market. Only a minuscule percentage of India’s over 750 million smartphone users are currently utilizing AI agents.
Several companies have attempted to pioneer consumer-facing agentic AI. Krutrim, backed by Ola’s Bhavish Aggarwal, launched ‘Kruti,’ a personal AI agent designed for tasks like booking cabs, ordering food, generating images, and conducting research. Fractal, traditionally an enterprise-focused firm, introduced tools like Kalaido and Vaidya. Gnani AI entered the consumer space with ‘Inya AI,’ enabling users to create plug-and-play voice/chat agents. Yet, many of these tools are currently functioning as proof-of-concept apps rather than fully realized consumer products, with most platforms remaining in beta, offered for free, or targeting developers and enterprise teams.
Even government initiatives like Bhashini, a voice translation tool, have struggled to gain sustained consumer traction, highlighting the broader challenge of achieving product-market fit in the consumer agentic AI space in India. The core issue lies in the lack of widely adopted platforms where these agents can seamlessly integrate and the low consumer trust and understanding of autonomous systems. For instance, Kruti’s current limitation to Ola services and its existence as a separate app make it less appealing for users who already juggle multiple applications.
In contrast, the real traction for agentic AI in India is predominantly within enterprises, albeit at a slower pace than anticipated. Companies like Meritto and RevRag are developing agents for specialized workflows in sectors such as education and BFSI, focusing on tasks like lead qualification, sales automation, and call center support. Ashutosh Singh, co-founder and CEO of RevRag, noted the slow sales cycles and bureaucratic decision-making in India but emphasized that Indian clients do pay, stating, “It’s not about inferior tech or lack of money. It’s a game of volume and patience.” Sarvam, for example, developed the Samvaad platform for creating conversational voice agents in Indic languages for businesses, choosing not to launch a consumer app due to the complexities of scaling for the general population.
Despite these adoption challenges, the financial outlook for agentic AI in India is robust. Spending on AI, particularly agentic AI, is projected to soar from USD 2.1 billion in 2023 to USD 10.4 billion by 2028, representing a compound annual growth rate (CAGR) of 38 percent, according to a report by IDC and UiPath. Currently, 40 percent of organizations have already adopted agentic AI, with C-suite leaders expecting a 3–4x return on investment within the first year. Another 56 percent plan to adopt agentic AI within the next two years, and 46 percent anticipate rolling it out within the next six months.
However, deployment remains in early stages, with 64 percent of organizations conducting proof-of-concept projects without defined budgets, and only 19 percent having clear spending plans. Leading sectors in adoption include manufacturing, retail and wholesale, and healthcare and life sciences. Enterprises face hurdles such as biased datasets, complex data engineering, outdated IT infrastructure, cybersecurity threats (53% concerned about data privacy breaches), lack of transparency in AI decision-making (47%), and potential misuse by malicious actors (46%). The scarcity of skilled IT talent (53%) and regulatory uncertainty (47%) also pose significant bottlenecks.
DebDeep Sengupta, Area Vice President, South Asia, UiPath, stressed the importance of building a strong foundation for responsible and scalable deployment, including investing in high-quality, diverse datasets and implementing governance frameworks. He also highlighted the need to modernize legacy systems with flexible, cloud-native platforms. Tarun Dua, Founder and MD of E2E Cloud, added that AI literacy must extend beyond engineers to legal, compliance, and operations teams to prevent agentic systems from becoming ungovernable at scale.
Also Read:
- Enterprise Adoption of Agentic AI: Moving from Concept to Practical Implementation
- AWS Champions Agentic AI for Transformative Telecommunications Operations
While 80 percent of organizations report significant productivity gains and 73 percent value the flexibility of using multiple foundation models, the success of agentic AI in India at scale hinges on developing robust infrastructure and integrated interfaces. Without sustained local adoption and a clear demonstration of consumer need and willingness to pay, many Indian agentic AI startups risk becoming export-oriented tech demos or building for a non-existent domestic market.
