NVIDIA’s recent GTC 2026 event saw the unveiling of NeMoClaw, built upon the OpenClaw framework. OpenClaw enables the creation of AI agents capable of running on personal devices. NeMoClaw enhances these ‘Claws’ with added security and privacy, targeting enterprise applications.
While the effectiveness of NeMoClaw in delivering enterprise-grade AI agents remains to be seen, this move indicates NVIDIA’s increasing investment in the Agentic AI stack. NVIDIA also introduced Nemotron, a family of open models designed to aid in building specialized agentic AI systems.
NVIDIA joins companies like Google, Amazon, Microsoft, and Salesforce in developing AI agent frameworks. This competition mirrors the early days of cloud computing, as companies vie to establish the dominant platform for developers.
The Agentic Transformation Wave
Enterprises can currently build AI agents from scratch using languages such as Python and JavaScript. However, this process can be time-consuming and resource-intensive, especially when scaling.
Agentic frameworks offer a solution by providing built-in features and functions. These frameworks are increasingly viewed as the ‘operating systems’ for autonomous software, eliminating the need to start from scratch.
Popular open-source AI agent frameworks include LangChain, LlamaIndex, and LangGraph. Major tech and enterprise tech companies like Microsoft, Google, Amazon, OpenAI, and Salesforce offer AI agent frameworks integrated with their cloud and AI ecosystems. These include tools such as AutoGen, Vertex AI Agent Builder, Agents for Bedrock, and Agentforce, which are becoming fundamental components for enabling agentic transformation.
Ashvin Vellody, Partner at Deloitte India, notes that real enterprise demand, a supply-side push from technology vendors, and a genuine market shift are driving Big Tech’s pursuit of AI agent frameworks.
Enterprises’ access to powerful models and decreasing inference costs are key incentives. Vellody adds, ‘Companies now recognize that value will not come from the model alone. It will come from making the technology easier to use for a much larger pool of developers and builders who can create product-grade solutions. Enterprises want a practical way to embed these capabilities into business workflows.’
Indian Players Crowd Orchestration, Application Layers
For companies, building agent frameworks is about platform ownership. Like APIs and cloud platforms, these frameworks can create ecosystem lock-in, shape usage, and unlock monetization.
In India, this trend is emerging.
Fintech startup Razorpay recently launched its Agentic AI Studio in partnership with Anthropic’s Claude model. The platform is being tested with partners such as Swiggy and Zomato, enabling AI agents to place orders and complete payments, also partnering with PVR Inox, BigBasket, and LinkedIn.
Unlike foundational frameworks such as Google’s Vertex AI or OpenAI’s Agents SDK, which are built on their own underlying models, Razorpay operates at the orchestration and application layers, using Claude as the base model.
Agent Studio functions as a marketplace and builder platform, allowing businesses to deploy AI agents for specific use cases. It enables businesses to define workflows in plain English, connect agents to systems like Shopify, WhatsApp, Tally, QuickBooks, and Slack, and trigger actions based on real-time payment events.
‘We deliberately don’t build foundational models. That’s Anthropic’s and OpenAI’s domain,’ said Khilan Haria, chief product officer at Razorpay. ‘Instead, we focus on making that intelligence actionable within real-world commerce contexts, at scale.’
Most Indian startups in the agentic AI space are concentrating on orchestration and application layers.
Voice AI startup Gnani.ai launched Inya, designed to help enterprises rapidly build and deploy voice agents. ‘It is a multi-agent platform that gives customers access to prebuilt workflows along with all the necessary configurations to quickly develop AI agents,’ said Ganesh Gopalan, cofounder and CEO of Gnani.
Gnani’s Inya platform includes an orchestration layer that manages interactions at scale while minimizing latency. Gopalan added, ‘In many cases, partners and customers have been able to build and deploy voice AI agents within 30 minutes.’
Bengaluru-based Bolna AI operates in the orchestration layer, enabling enterprises to deploy multilingual voice agents. Noida-based Squadstack primarily operates at the application layer, while also building orchestration capabilities for production-grade deployments focused on revenue and customer experience workflows.
Indian firms’ concentration in these layers is due to lower entry barriers and faster monetization.
Experts believe that long-term value will be created at higher layers of the stack.
‘Over time, models will become more available, and frameworks more standardised,’ said Vellody. ‘The real differentiation will come from how effectively organisations use them to drive business outcomes.’
Answering Monetisation Questions
The pursuit of AI agent frameworks is about owning the monetization layer of AI. Companies like Microsoft and Salesforce are positioning their frameworks as the gateway for enterprise AI, similar to how cloud platforms transformed infrastructure into a recurring revenue business.
This creates revenue streams. Vendors can charge subscription fees, usage-based fees, or outcome-linked pricing.
Frameworks like AutoGen or CrewAI are often free, but companies monetize the underlying infrastructure, models, or applications built on them.
Pricing models vary. Bolna AI charges customers per minute, while Razorpay’s Agent Studio offers a free trial, with pricing depending on the agent and use case.
‘While frameworks can be monetised, the real monetisation will likely be indirect,’ said Arun Chandrasekaran, VP analyst at Gartner. ‘Revenue will ultimately come through models, infrastructure, and applications.’
For markets like India, cost efficiency, multilingual capabilities, and pre-built agents are critical. Chandrasekaran noted that lightweight, modular, and open frameworks are likely to find greater traction than heavy enterprise stacks.
As enterprises move to production, value will accrue to those who control AI agent orchestration, integration, and monetization at scale. The landscape of AI agent frameworks is likely to consolidate into dominant platforms.
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