As AI becomes embedded in decision-making, organizations can no longer rely on systems they cannot explain. Visibility into how AI behaves — including how it reasons, how it interacts with data, and how it evolves over time — becomes a prerequisite for trust. Sid Bhatia, Area VP & General Manager for the Middle East, Turkey & Africa at Dataiku explains how AI Observability is what makes that visibility possible.
Very soon, AI agents will be everywhere. For a snapshot of how adoption could evolve, it would be prudent to observe the United Arab Emirates, where early adoption of technologies has become a tradition across both the public and private sectors. While the buoyancy of the nascent UAE agentic AI market is difficult to gauge, one estimate predicts the nation’s overall autonomous systems market, of which agentic AI is a part, could top US$4 billion by 2033.
Already, UAE organizations have chosen to embed AI agents into everyday live corporate workflows, giving them jobs from coordination to decision-making. APIs connect agentic AI to core systems and databases, and agents have even begun to work with other agents. But while focusing on the potential rewards – greater efficiency, enhanced accuracy, reduced costs – how many enterprises have made progress on understanding the risks agents pose?
Let’s start by remembering that agentic AI does not rely on prompts. An agent is built to embark on multi-step operations and given significant freedom in its execution. Agents operate probabilistically, which is why they can adapt in real time. Taken together with their collaboration with other agents, complexity compounds rapidly when each of these agents can call multiple others and each can run multiple tools. Humans have little insight into what decisions the AI agent makes or into the reasoning behind them. Often, all that can be seen is that an agent invoked a service. There is no visibility of why. We can see successes and failures without any traceability of how they arose. Observability has become the number-one issue in autonomous AI.
“Observability has become a necessary part of AI governance, a way of guaranteeing operational reliability, cost management, and compliance. It removes agentic AI from its black box and makes it a manageable, compliance-ready asset.” – Sid Bhatia, Area VP & General Manager for the Middle East, Turkey & Africa at Dataiku
Say ‘no’ to grey areas
Given the business’s responsibilities to its industry, market, and government, there can be no grey areas in agentic AI. Uptime metrics are of no help when something goes wrong. Postmortems do not, of themselves, restore market confidence. Agent decisions must be traceable; risks must be detectable. AI observability is the term we use to describe the methodology that captures the telemetry of AI operation – every logical step, every decision, every API call, every model interaction.
Underpinning observability is what we call MELT (Metrics, Events, Logs, Traces) data. Metrics measure performance and cost (e.g. latency, token usage, and model accuracy); events include everything from API calls to human handoffs; logs are records of interactions (prompts, outputs, and so on) used for debugging or audits; and traces connect the other telemetry, recording the full path of a workflow.
Observability has become a necessary part of AI governance, a way of guaranteeing operational reliability, cost management, and compliance. It removes agentic AI from its black box and makes it a manageable, compliance-ready asset. Of course, derisking multi-agent environments – where agents can call not only multiple tools but also other agents with the same invocation capabilities – is much more complicated. In these environments, observability must also capture agent-to-agent interactions.
Observability in practice
To integrate observability into governance, we must integrate it into the AI lifecycle. Pre-deployment evaluation must determine an agent’s reliability and dashboards must show its minute-by-minute progress, including the presence of model drift. The agent and its monitors must be guided by policies that prevent unsafe or non-compliant actions. It is in these steps that we make agentic AI fit for purpose and for scalability. Indeed, we can justifiably claim that observability does not impede innovation. It enables it. Technical and line-of-business teams can use the insights brought to them by observability to experiment safely with new ideas while making sure confidence in production systems never slips. It is understood that delivery of observability is essentially delivery of a cultural change within the business – one in which designers and users of agents understand the necessity of being able to see each step as it unfolds.
To deliver effective observability will require more than standalone monitoring tools. To capture the complex interactions of prompts, models, tools, data, systems, and policy, organizations will need a unified, enterprise-grade AI platform that unites data preparation, model development, deployment, and governance. With observability treated as part of the AI lifecycle, teams gain insights across data pipelines, models, and agent workflows. They will see everything that occurs, from initial user input and prompt construction, through model inference and tool calls, to the final output. Users will be able to dissect cross-layer lineage, observing data sources, feature transformations, model versions, and agent decisions. Real-time data on token usage, performance, and failure points will be fed to users from multiple agents through the enterprise AI platform.
Enterprise AI cannot survive without observability. Agentic AI’s rapid rise means systems move from concept to field operations too quickly for current oversight approaches to adequately capture. Agents are de facto colleagues to human employees. They are customer-facing team members with real-world responsibilities. As such, their potential for real-world impact cannot be ignored. Relying on a system that behaves in ways that cannot be explained is a recipe for non-compliance. We must know how AI reasons, how it uses data, and how it evolves over time. Only then can we claim to trust it. Transparency is critical to that trust. And observability is critical to transparency.
