The race to build AI agent frameworks is less about helping developers and more about controlling the next major platform layer in technology, similar to how cloud computing transformed infrastructure into a recurring revenue business. Big Tech companies including Microsoft, Google, Amazon, and Salesforce are all investing heavily in frameworks that help businesses create, manage, and orchestrate custom AI agents for specific industries and use cases. This competition mirrors the early days of cloud computing, when companies fought to define the dominant platform that developers would build on .

What Are AI Agent Frameworks, and Why Do Companies Need Them?

Building AI agents from scratch using coding languages like Python or JavaScript requires significant time and effort, especially when scaling to enterprise needs. AI agent frameworks solve this problem by functioning as "operating systems" for autonomous software, complete with built-in features and functions that developers can leverage without starting from zero .

Popular open-source frameworks include LangChain, LlamaIndex, and LangGraph for core development. Meanwhile, Big Tech companies offer tightly integrated solutions such as AutoGen from Microsoft, Vertex AI Agent Builder from Google, Agents for Bedrock from Amazon, and Agentforce from Salesforce . These frameworks have become the building blocks that other businesses use to enable their agentic transformation.

How Are Companies Monetizing AI Agent Frameworks?

The monetization strategies vary widely across vendors. Some charge subscription fees for platform access, while others use usage-based pricing tied to agent activity or outcome-linked pricing where they take a share of transactions executed by agents . For example, Bolna AI charges customers on a per-minute basis, with entry-level pricing starting as low as 2.5 rupees per minute. Razorpay's Agent Studio, currently in early access, offers a 30-day free trial, after which pricing depends on the specific agent and use case, ranging from subscription fees to per-action or outcome-based charges .

Interestingly, frameworks themselves are often free to use. Tools like AutoGen or CrewAI carry no direct cost, but companies monetize the underlying infrastructure, models, or applications built on top of them .

Where Are Indian Startups Positioning Themselves in the AI Agent Stack?

Most Indian startups in the agentic AI space are focusing on the orchestration and application layers rather than building foundational models, according to industry analysis . This strategic positioning reflects relatively lower entry barriers and faster paths to monetization compared to building foundational models from scratch.

Razorpay introduced its Agentic AI Studio in partnership with Anthropic's Claude model, currently being tested with partners such as Swiggy and Zomato. The platform enables AI agents to place orders and complete payments, and has also tied up with companies like PVR Inox, BigBasket, and LinkedIn .

"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."

Khilan Haria, Chief Product Officer at Razorpay

Voice AI startup Gnani.ai recently launched Inya, a platform designed to help enterprises rapidly build and deploy voice agents. The multi-agent platform gives customers access to prebuilt workflows along with all necessary configurations to quickly develop AI agents. In many cases, partners and customers have been able to build and deploy voice AI agents within 30 minutes .

Similarly, Bengaluru-based Bolna AI operates in the orchestration layer, enabling enterprises to deploy multilingual voice agents across different call scenarios. 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 .

Steps to Understanding the AI Agent Framework Landscape

• Foundational Layer: Companies like OpenAI and Anthropic build the underlying language models that power AI agents, requiring massive computational resources and research investment.
• Framework Layer: Big Tech firms like Microsoft and Google provide orchestration tools and development platforms that make it easier for developers to build agents without coding from scratch.
• Application Layer: Indian startups and specialized vendors build purpose-built agents for specific use cases like voice commerce, payment processing, or customer service, directly serving end customers.
• Orchestration Layer: Middleware companies connect agents to existing business systems like Shopify, WhatsApp, Slack, and accounting software, enabling multi-step workflows and real-time triggers.

Why Experts Say the Real Value Is at the Top of the Stack

While foundational models and frameworks remain important, long-term value will be created at higher layers of the stack, according to industry experts . As models become more available and frameworks more standardized, the real differentiation will come from how effectively organizations use them to drive business outcomes.

"Over time, models will become more available, and frameworks more standardised," explained Ashvin Vellody, Partner at Deloitte India. "The real differentiation will come from how effectively organisations use them to drive business outcomes."

Ashvin Vellody, Partner at Deloitte India

Enterprises' access to powerful frontier models and falling costs of inference have acted as key incentives for this shift. Companies now recognize that value will not come from the model alone, but from making the technology easier to use for a much larger pool of developers and builders who can create product-grade solutions .

However, experts caution that while frameworks can be monetized directly, the real monetization will likely be indirect. Revenue will ultimately come through models, infrastructure, and applications built on top of frameworks .

What Does NVIDIA's Move Into Agentic AI Signal About the Market?

During the recently concluded NVIDIA GTC 2026 event, the chip giant announced NeMoClaw, built on OpenClaw, a blockbuster agentic AI framework that allows building AI agents that can run on personal devices. NeMoClaw adds a security and privacy layer to OpenClaw agents, making them suitable for enterprise usage . NVIDIA also introduced Nemotron, a family of open models that help in building specialized agentic AI systems.

This move marks NVIDIA's further expansion into the agentic AI stack as it looks to deepen its presence in AI software and agent development. NVIDIA joins companies like Google, Amazon, Microsoft, and Salesforce that are building AI agent frameworks. The competition is reminiscent of the early days of cloud computing, where firms competed to define the dominant platform layer that developers would build on .

The rush to build AI agent frameworks is driven by real enterprise demand, supply-side push from technology vendors, and a genuine market shift. For companies, building agent frameworks is less about tooling and more about owning the next platform layer. Much like APIs and cloud platforms before them, these frameworks can lock developers into ecosystems, shape usage patterns, and unlock multiple monetization levers .