By Nalin Agrawal, Director of Solutions Engineering, Dynatrace
India’s AI adoption is moving quickly from experimentation to deployment. According to a Government of India assessment, 87% of Indian enterprises are actively using AI solutions, while only 26% have achieved AI maturity at scale. At the same time, the IndiaAI Mission is expanding access to shared AI compute infrastructure, accelerating AI development and deployment across India.
These numbers point to an important transition: AI is moving beyond experimentation into customer experiences, employee workflows and core operations, increasing the complexity of the infrastructure supporting it.
But as AI moves deeper into production, another question deserves equal attention: can enterprises actually see what their AI systems are doing, understand why they are behaving that way and control what happens when something goes wrong?
For years, technology leaders have measured digital systems through indicators such as uptime, latency, error rates and infrastructure utilisation. These remain important. But AI introduces new operational questions. Why did an AI agent take particular action? Is its output accurate and aligned with the business objective? Has its behaviour changed over time? And what is the cost of delivering that outcome at scale?
The first step is seeing how the AI infrastructure itself is performing. AI observability gives enterprises visibility into usage, cost, response times and model availability across the services they rely on. This helps teams understand whether AI applications are performing as expected and identify issues before they affect customers or operations.
But as AI becomes more autonomous, infrastructure visibility is only part of the picture. Enterprises also need to understand what their AI agents are doing.
As AI agents move pilots into production, they increasingly interact with multiple models, applications, data sources and external services. An issue in one part of the environment can affect the behaviour of another, making it difficult to understand where a problem originated.
An agent might retrieve information, use a tool, encounter an error and change its approach before completing a task. AI Agent Observability helps teams trace these actions and understand how an agent arrived at an outcome, including issues such as unexpected actions, policy violations or declining quality.
The distinction is important: AI observability focuses on the health, performance and cost of the infrastructure supporting AI, while AI Agent Observability focuses on how autonomous systems behave and execute tasks.
The limits of traditional monitoring
Traditional monitoring solutions were designed for relatively predictable technology environments. When an application slowed down or a server reached capacity, teams could investigate a defined set of technical signals and work towards a root cause.
AI changes this equation. An AI application can be technically available and still produce an unreliable outcome. Its behaviour may change because of shifting data, model updates, prompt changes or unexpected interactions between systems. A system can therefore appear healthy while delivering inaccurate responses, consuming more resources than expected or making decisions outside organisational policies.
For enterprises, this means understanding AI requires more than checking whether the underlying technology is available. They also need to understand whether AI is delivering the intended outcomes.
From observability to control
As AI agents take on more responsibility, enterprises need to establish clear boundaries around what they can and cannot do. The objective should not be to keep humans manually involved in every AI decision, as this quickly becomes unsustainable as autonomous systems scale. Instead, organisations need supervised autonomy, where AI can operate at machine speed within defined policies, with human intervention reserved for higher-risk decisions and exceptions.
An agent might be allowed to identify a performance issue or categorise an internal request automatically. A decision affecting a financial transaction, customer entitlement or critical production system may require human approval.
This distinction will become increasingly important as AI moves into sectors where trust, traceability and accountability are essential.
State of SRE and Platform Engineering 2026 research reflects this shift. 67% of SREs identify monitoring AI models as their top AI use case, highlighting how visibility into AI behaviour is becoming a core operational priority as enterprises move from experimentation to production.
The answer is to build observability into AI systems from the beginning, with visibility into both performance and autonomous actions.
India’s AI growth needs an operational foundation
India’s AI growth makes this challenge particularly relevant. As AI becomes more deeply embedded in enterprise operations, the ability to operate it reliably and responsibly will become as important as the infrastructure supporting it.
The Government of India’s responsible AI agenda also emphasises accountability, transparency, safety and human oversight. The IndiaAI Mission’s Safe and Trusted AI pillar includes areas such as explainability, auditing and governance testing. These priorities reinforce the need for enterprises to have visibility into how AI systems behave and the controls to intervene when they do not behave as intended.
For CIOs, this means treating AI as an operations challenge as much as a technology investment. Three principles should guide the approach:
Instrument early. Build visibility into AI workflows from the outset rather than trying to retrofit it later.
Measure the right outcomes. Look beyond infrastructure availability to quality, accuracy, performance, cost and policy compliance.
Automate with guardrails. Allow AI to act autonomously where appropriate, while retaining human oversight for consequential decisions and exceptions.
India has an opportunity to build an AI ecosystem that is not only large, but dependable. The next phase of AI maturity will not be defined simply by how many models organisations deploy or how much compute they can access. It will depend on whether enterprises can operate AI with the visibility, accountability and controls needed at scale.
The next generation of digital leaders will not simply deploy more AI. They will build the foundations needed to make it trustworthy at enterprise scale.