AI agents still struggle with multi-step reasoning and reflection. AI systems such as AlphaFold, for instance, have already revolutionized the prediction of protein structures, a cornerstone in drug discovery. Advanced GenAI chatbots can help us independently plan vacations, manage calendars, shop for users, and streamline countless daily tasks. Indian IT services company Wipro predicts that enterprises will reimagine business processes and value streams with AI Agents this year, following which AI Agents will generate new revenue streams, innovate business processes across industries, and boost profitability, operational efficiency, and customer experience in the coming years. The global AI market, which includes AI agents, is projected to reach $1.8 trillion by 2030. In customer service alone, AI agents are projected to save businesses over $80 billion annually by 2026 through automation. Yet, AI agents face significant challenges that could stall their momentum. AI agents, for instance, still struggle with multi-step reasoning and reflection. For example, an agent tasked with booking a vacation might secure a flight but fail to align hotel check-in times, leading to inconvenient results. Without more sophisticated reasoning and long-term memory, these agents risk being limited to simple, well-defined tasks. These systems require substantial processing power to complete tasks involving web searches, real-time data analysis, and multi-step operations, making them prohibitively expensive for smaller businesses and individuals, and limiting their broader adoption. AI agents also raise concerns around ethics and security, adding to the fear of AGI since they work autonomously. Left unchecked, for instance, they could potentially be weaponized to scale phishing attacks, spread misinformation, or exploit cybersecurity vulnerabilities. An AI agent managing stock trades, for instance, could misinterpret market signals or act on incomplete data, leading to catastrophic financial losses. Until these systems consistently demonstrate accuracy and dependability, widespread trust will remain elusive. 3. India will back its AI ambitions with robust infrastructure The Union Cabinet’s $1 billion allocation for the IndiaAI Mission and approval of four semiconductor projects signal India’s ambition to reduce electronic imports and establish itself on the global AI and semiconductor stage. While India lags behind the US and China, which have invested heavily in semiconductors and AI startups, the ₹ 1.5 trillion worth of projects aim to bolster the country’s microchip-making ecosystem, focusing on design, fabrication, assembly, and testing. Tata Electronics’ semiconductor plant, which is under construction in Assam, is expected to start producing ‘Made in India’ chips by 2026 and export them to multiple countries for use in the automobile and telecom sectors. View Full Image Tata Electronics’ semiconductor plant, under construction in Assam, is expected to start producing chips by 2026. Though the approved 28-40 nanometre (nm) fabs won’t enable cutting-edge AI chips (sub-5nm), the ₹ 10,371.92 crore allocated to the national AI mission will boost computing capacity, support local AI models, and promote AI startups. India plans to secure 10,000 or more graphics processing units (GPU) through public-private partnerships to overcome their scarcity—GPUs are critical in training AI models. Additionally, the IndiaAI Innovation Centre will focus on developing indigenous large multimodal models (LMMs) and domain-specific AI applications, addressing a current gap in India’s AI ecosystem. However, India’s LMM landscape remains underdeveloped, with only a handful of models, such as Sarvam AI’s OpenHathi and CoRover.ai’s BharatGPT. In comparison, China boasts over 130 language models. Many of India’s 22 official languages lack sufficient digital data, which the proposed IndiaAI Datasets Platform aims to address by offering quality non-personal datasets to startups and researchers. On the semiconductor front, India’s agreements with Singapore, the US, and the European Union demonstrate its commitment to reshaping the global semiconductor supply chain, which is currently dominated by the US, China, South Korea, Vietnam, and Taiwan. These efforts aim to reduce dependency on imports for components vital to AI applications, electronics, and electric vehicles. India also boasts a strong talent pool of over 2,000 semiconductor chip design engineers and houses R&D centres for major global companies, including Intel, Nvidia, AMD, and Qualcomm. India’s LMM landscape remains underdeveloped, with only a handful of models, such as Sarvam AI’s OpenHaathi and CoRover.ai’s BharatGPT. The country is also setting its sights on becoming a global data centre hub. India currently has about 150 data centres, with major players such as Amazon Web Services, Microsoft Azure, and Google Cloud rapidly expanding their capacities. Rising AI adoption, 5G rollouts, data localization mandates, and the growing demand for edge computing are driving this expansion. However, India accounts for only 3% of global data centre capacity despite holding 20% of global data. Crisil Ratings projects the Indian data centre industry will double its capacity to 2-2.3 gigawatts (GW) by FY27. JLL India anticipates a 604 MW increase, while Icra forecasts operational capacity will surpass 2,000 MW by FY27, backed by investments of ₹