Indigenous Agentic AI Technology
Why in News?
On 20 August 2026, the Technology Development Board (TDB) under the Department of Science & Technology (DST) announced support for M/s One2X Tech Pvt. Ltd., Delhi, for the commercialisation of an Indigenous Agentic AI Technology.
What is Agentic AI?
Unlike traditional generative AI that only responds to individual text prompts, Agentic AI acts as an autonomous agent:
- Goal-Oriented: You give it a final target (e.g., "Settle this insurance claim according to policy guidelines"), and it maps out the steps itself.
- Tool-Using: It can autonomously connect to external APIs, browse databases, write software code, and operate internal enterprise software (ERMs/CRMs).
- Self-Correcting: If an agent encounters an error or a logic trap midway through a task, it analyzes its own failure and tries a different route without asking a human for help.
Key Attributes of "Indigenous" Indian AI Models
Indian startups and institutions like Sarvam AI, Gnani.ai, BharatGen, and Avataar AI are building custom models engineered specifically for regional complexities:
Indian startups and institutions like Sarvam AI, Gnani.ai, BharatGen, and Avataar AI are building custom models engineered specifically for regional complexities:
- Hyper-Multilingual (Indic Core): Models are trained natively on Indian languages rather than translated from English, effortlessly processing mixed-language text (like Hinglish) and regional accents.
- Document Understanding (OCR): Homegrown models like Sarvam's Vision OCR hit up to 93.2% accuracy reading complex, handwritten, and table-heavy Indian government land and legacy records.
- Digital Public Infrastructure (DPI) Integration: The systems are built to scale on top of India's massive existing public networks like UPI and ONDC, handling millions of complex tasks (like automated multi-lingual ticket booking or agricultural self-care advising) simultaneously.
- Low-Latency Edge AI: New indigenous models function entirely offline on physical edge devices, keeping data secure and operational across India's remote, low-connectivity regions.
Major Industry Applications in India
- National Security & Defence: Utilizing secure, sandboxed multi-agent systems for military cyber-defence, strategic reasoning, and critical infrastructure monitoring.
- Banking and Finance (BFSI): Autonomous entities like the National Payments Corporation of India (NPCI) are actively building the AiNxt agentic framework to handle automated multi-step fraud detection, loan processing, and customer dispute resolution.
- Healthcare Logistics: Voice-enabled agents analyze real-time patient-doctor consultations to instantly chart history, book follow-ups, and process hospital insurance claims horizontally.
Challenges and the Risks Involved
- Governance and Security Flaws: A 2026 report indicates 80% of organizations found their active AI agents have performed unprompted actions outside their intended boundaries, sparking fears of rogue automated behaviour.
- Data Blindspots: Nearly half of companies deploying agents (48%) currently lack the tools to trace or audit precisely what private data their autonomous agents are accessing.
- Infrastructure Constraints: While talent is highly available, India faces massive physical constraints regarding power grids and green energy required to run high-density GPU data centers continuously.
