India Is Racing Towards The New Frontier Of AI
Earlier this year, the AI Impact Summit in New Delhi raised awareness on the ever expanding role AI is playing in daily life… and how India plans to be at the forefront of this space moving forward. That begs the question: what’s the next big thing in the world of AI?
Many enterprises believe the answer to that question is the incorporation of agentic AI – systems that leverage AI to reason, plan and complete a wide variety of organizational tasks. Enterprise usage of agentic AI can be categorised in 4 different levels:
Level 1: Chain | Level 2: Workflow | Level 3: Partially Autonomous | Level 4: Fully Autonomous |
Rule-based robotic process automation (RPA) with pre-defined actions and sequences. | Actions are pre-defined, but the sequence can be dynamically determined by AI using LLMs, routers, etc. | Given a goal, the agent can plan, execute and adjust a sequence of actions using a domain-specific toolkit with minimal human oversight. | With little to no oversight, these agents proactively set goals, adapt to outcomes and possess capacity to even create their own tools in order to meet objectives. |
Use Case: Extraction of invoice details from PDFs into separate databases. | Use Case: Drafting of customer emails through intelligent LLM capabilities. | Use Case: Resolution of customer support tickets across multiple systems. | Use Case: Strategic research agents with the capacity to independently discover, summarise and synthesise information. |
Most AI adoption currently seen in enterprises lies between Level 1 & 2. As capabilities rapidly improve, Level 3 & beyond is when it starts to get transformational – an inflection point that McKinsey describes as a ‘moment of strategic divergence’ where early movers will redefine competitive dynamics.
India is leading the charge in this as well, with the 2025 BCG AI At Work survey ranking the nation second globally when it comes to AI agent integration. Yet, the entire movement is shrouded in a state of introductory flux: 77% of respondents believe AI agents will be important in the next three to five years, yet only 33% say they have a proper understanding of what these agents actually are.
What Needs To Change For Enterprises To Fully Take The Agentic Leap?
Research from Gartner shares a similar dichotomy in terms of what the future looks like for enterprise agentic AI in the next 3 years:
More Integrations Gartner predicts that by 2028, 33% of enterprise software applications will contain agentic AI capabilities – rising from less than 1% in 2024. Furthermore, 15% of day-to-day work decisions will be accomplished autonomously by then. | 🔁 | More Failures Gartner also predicts that by the end of 2027, more than 40% of agentic projects will fail or get cancelled due to reasons like:
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The difference between success and failure will hinge on the way organizations integrate AI agents into their business processes and technical infrastructures. Companies that win the agentic AI race won’t be the ones that deploy fastest – they’ll be the ones that build the foundations required to make AI agents actually function properly.
These new foundations are critical because the truth is, current infrastructures are not suitable for the elevated requirements of agentic AI. Even at this nascent stage, teams that bolted agent execution onto existing infrastructure consistently reported:
- Higher incident rates
- More security concerns
- Greater operational overheads
The Two Pillars Driving New Agentic AI Infrastructure
The biggest shift in AI in recent years – something that agentic AI is driving – has been a focus from training to inference. While training also requires massive levels of data and computing, the process is also quite predictable. On the other hand, inference tasks are relatively small in isolation, but they can add up fast.
For agentic AI to really flourish into transformational use cases (Level 3 & beyond), enterprises must create an event-driven, resilient infrastructure that can orchestrate complex, long-running processes across distributed systems. This will require inference capabilities that are far beyond what your current infrastructure can probably deliver, whether it’s:
- Persistent learning & memory across a robust, governed data infrastructure
- Coordination capabilities spanning multi-part tasks and multi agent workflows
- Real-time orchestration & decision-making in order to carry out complex requests
New infrastructure foundations that support all these instances of enhanced inference must be built with 2 key pillars:
Pillar 1: Holistic + Persistent Context | Pillar 2: Scalable + Flexible Compute |
For agentic AI to autonomously reason & execute to the best of its abilities, you must create data architectures that deliver business context that is reliably used by agents – and the humans governing them. This involves:
| Once context is established, it is time to incorporate a corresponding compute infrastructure that enables agents to conduct all these various tasks in scale:
However, running persistent infrastructure for these agents drives up costs, as there is a chance that these systems can remain idle at times due to the volatility of inference tasks. Therefore, cloud-based ephemeral approaches that spin up resources only when needed seem to be the prevailing method of accommodating these agentic workflows. |
Many public cloud options in the market are now ramping up their capabilities to handle these exponentially increasing compute requirements – announcements of several gigawatt-scale AI cloud data centre facilities in India is testament to this. However, as agents get more advanced and cloud-hosted models continue to improve, many enterprises – especially those in highly regulated industries – are increasingly preferring to create personalized hybrid clouds spanning public infrastructures and private setups in order to truly maximize agentic AI.
The Other Must-Haves In Enterprise Agent Deployment
The two aforementioned pillars will provide the initial infrastructure required for you to run agentic AI. However, due to its complexities – both in terms of processes and the way it will ultimately shape your work culture – several must-have components (specific to agentic AI) must be added on top of this:
- Observability: Once operationalised, your enterprise must create an observability layer that helps track & analyze agent performance, system health & potential risks across all your environments and throughout the entire agent lifecycle. OpenTelemetry is considered to be the current open-source standard for real-time agentic AI monitoring.
- Interoperability: Firstly, you must create individual isolated environments (sandboxes) for these agents where untrusted code can run without affecting the host system or other workloads. Then, you must combine individual agent sandboxing with environment-level orchestration – where agents can seamlessly interact with other systems and agents. Model Context Protocol (MCP) is becoming an emerging standard for supporting agent interoperability across multiple systems & vendors.
- Security: Your enterprise must construct a security framework to address novel agentic AI risks such as prompt filtering, response enforcement, data security and external access control. If your AI agents are dealing with highly sensitive information, they must be governed by access controls even stricter than what you use for your human users.
- Governance: Considering the worldwide concerns about the use of ‘responsible AI’, it is important to embed governance right from the start to ensure AI deployments operate within clearly defined ethical, operational & compliance boundaries.
- Change Management: Finally, the dynamics of human-AI collaboration is key to making all of this work. Your enterprise needs comprehensive change management programs that address employee concerns about how AI agents will augment rather than replace humans. The focus must be on integrating AI agents as teammates rather than tools.
How Do You Start Deploying AI Agents Into Your Enterprise?
During the overhaul of your existing infrastructure, the primary goal should be developing agentic pipelines that can execute reliably across a wide range of use cases. Once that is achieved, it is important to take things step-by-step:
Step 1: Start off with high-impact, low-risk use cases that address specific business pain points – several popular initial agentic AI missions include customer service automation and document processing.
Step 2: Define these initiatives through measurable KPIs. You should be striving for accuracy rates above 95% and task completion rates above 90%.
Step 3: Maintain coherent state management over time, with feedback mechanisms that catch mistakes before they cascade.
Step 4: Once these particular use cases are deemed successful, responsibly expand scope and usage of agentic AI across other parts of your business.
Of course, this is where a managed service partner well-versed in this domain – like iValue – can prove to be the difference. Click here to speak to one of our experts about the kind of enterprise use cases you can start transforming today with the incorporation of agentic AI.
