AI agents are running a riot across Indian IT firms. From 150 to 200, and even over 300 AI agents are being deployed by companies, embedding them across sectors to automate workflows and decisions.
Startups are just as active. Tracxn tracks dozens of Agentic AI players in India. A Deloitte report says 80% of Indian enterprises are exploring agentic AI, with nearly half focusing on multi-agent collaboration for complex tasks.
Demand is real and supply is racing to meet it. But the real question is: Who’s building thinking systems, and who’s just dressing up scripts in AI buzzwords?
Not All is Rosy
There are a lot of pilots happening on agentic AI, according to a senior executive at an IT giant. However, the biggest hurdle remains accuracy of a model.
“That demands two important elements: one is bringing contextual evidence into the RAG model, and the second is fine-tuning that model with respect to the domain context, which is a very hard thing to do,” he told AIM.
He pointed out that while different types of agents are available, their real-world performance is still in question.
When AIM asked Ramprakash Ramamoorthy, director of AI research at Zoho, about how the AI agents will be priced, he said that agent adoption rates are still poor across the industry, not just in Zoho.
“There is just a lot of hype around it. So we want customers to see value. And only when we get the, you know, reiteration that customers are seeing value, we will have to start pricing it,” Ramamoorthy shared.
“…So we are playing a wait and watch game here, it is a very conscious decision,” he said.
NimbleEdge co-founder and CTO Neeraj Poddar shared a similar perspective with AIM saying that often these agents are just automations masked as agents without any intelligence or decision making baked in.
Utkarsh Kanwat, engineer at ANZ, unpacked why he’s still bullish on AI, but deeply sceptical of fully autonomous agents. Kanwat has built over a dozen production-grade agent systems, from UI generation to DevOps automation. He argues that it isn’t that the agents don’t work, but just that they don’t work like the industry thinks.
One of his projects, a conversational database agent, failed because of context window costs. Early queries were cheap, but by the tenth, each request had over 150,000 tokens, costing several dollars just to maintain context.
Kanwat said that AI agents only do 30% of the job. The real work is in how humans instruct, constrain, and integrate them effectively, that 70% is invisible labour.
To him, today’s agent hype mirrors the blockchain bubble, though AI agents do work, just not as magically as advertised.
During a recent webinar organised by Telangana AI Mission (T-AIM), assistant professor at the Software Engineering Research Center, IIIT-Hyderabad Karthik Vaidhyanathan also acknowledged that agents are not yet there.
“We are trying to be there, although there is a lot of hype going around.”
He added, “We are trying to do a lot of POCs and saying we are already seeing the difference. But, I feel there needs to be a lot more work done…because there is a lot of uncertainty in these components.”
He underscored the importance of a lot of work needed in trust, observability, security, among other aspects.
The True Agent
Truly intelligent AI agents, according to Poddar, should have world knowledge and reasoning capabilities, be able to process context and take actions reliably to complete the user’s tasks.
“Context here includes user’s inputs/preferences, tool’s inputs/outputs and ability to evaluate/verify current thinking and replan course of action as needed,” he shared.
He added that the best AI agents so far have been coding agents as their outputs can be verified in a deterministic manner. “Beyond that a lot of agent adoption has been to gather data from different sources in IT companies and do research and analysis instead of taking any real-time actions that can mutate the data,” he said.
Jaspreet Bindra, CEO of AI&Beyond, said that the autonomy goes beyond basic automation, which follows fixed, rule-based workflows. “A clear threshold emerges when the agent exhibits goal-directed behavior, learns from feedback, and dynamically modifies its actions,” he said.
On where the handoff from human control occurs, Bindra explained it’s at the point where the agent is trusted to make decisions under uncertainty, not just selecting predefined options, but generating novel actions aligned with desired outcomes.
“The level of autonomy granted,” he added, “depends on the risk appetite of the deploying system.”
What needs to be done?
Referring to a recent incident in which Replit agent went rogue, Poddar said, “We need to build an observability stack in agent orchestration, including built-in traceability provided by LLM providers like OpenAI/Anthropic which is currently missing.”
When asked if there is any structured way Indian IT firms or startups are testing and validating the real-world performance of these agents, Poddar said that the country is still in early phases of reliability. “However, we see good adoption of AI coding agents – even though they are mostly getting used as AI co-pilots with developers getting huge efficiency and productivity gains.”
Safety and guard rails frameworks like Llama Guard and Llama Stack, according to him, are a step in the right direction to create building blocks for reliable and verifiable agents in production by doing pre and post validation.
In the same webinar organised by T-AIM, Kaushiki Choubey, engineering lead at Lloyds Technology Centre India, explained that in preparing AI agents for production in enterprises, three factors must be balanced — explainability, control, and performance.
“Explainability is critical, especially for regulators, auditors, and customers who need to understand AI decisions. Control must be applied carefully, too much can limit the agent’s autonomy. Performance focuses on speed, accuracy, and latency.”
She added that often, teams prioritise high-performing models to reduce cost or improve speed, but these models tend to lack explainability, which ultimately becomes the most important factor in real-world deployment.
Who’s stopping the agents?
During a recent podcast episode of ‘From the Horse’s Mouth: Intrepid Conversations with Phil Fersht,’ hosted by HFS Research CEO Fersht, Workato’s CEO Vijay Tella highlighted that although generative AI and large language models exist, organisational thinking and system design have not yet fully shifted to accommodate agentic AI’s potential for autonomous and goal-driven action.
Poddar said that reliability of agents is itself a biggest hurdle requiring enhanced models and observability, adding that the ability to loop in humans for fallback is often missing.
He said that inference costs at scale can be prohibitive, making on-device agents ideal for many use cases. Current models also struggle with multi-step planning and execution, limiting complex task handling, according to him.
Poddar concluded that though 2025 is touted as the “year of AI agents,” he believes it is still the beginning. “Enterprise adoption and integration that deliver true value will happen in the order of 2-3 years,” he emphasised.
[With Inputs from Ankush Das]
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