Observability is making AI Agents accountable
Over 80% of Indian organisations are exploring the development of autonomous agents, indicating a substantial shift towards agentic AI. Despite the resources allocated towards the pilot phase to adopt AI agents, only 29% of businesses have the capacity to fully scale agentic AI initiatives.
Several challenges stand in the way. Concerns around errors with real-world consequences, bias, hallucinations and data quality continue to slow down deployment. Rising costs, along with a lack of governance frameworks for managing agentic systems, have added further uncertainty.
Furthermore, agentic AI systems often operate as “black boxes,” offering limited visibility into how they function or how decisions are made. This lack of transparency makes it difficult to detect and address issues like bias, hallucinations, or toxic outputs. It also creates challenges in cost control as it limits insight into token usage or system behaviour, leading to unexpected bills and inefficient resource allocation. This is where intelligent observability becomes critical. It offers deep visibility into the model, service layers, frameworks, and data sources powering these agents. With an observability platform in place, organisations gain the transparency and context needed to understand their behaviour, optimise performance, and build trust, setting the foundation to broaden agentic AI integration responsibly and effectively.
The transparency gap is slowing agentic AI adoption
Unlike traditional AI models, agentic AI systems don’t operate on static rules with predefined outcomes. Instead, they perceive, plan, adapt, learn, and make decisions on the go, making them inherently unpredictable. This flexibility brings greater autonomy and efficiency, but it also makes these systems harder to monitor and evaluate. Furthermore, agentic AI systems interact with multiple tools and services to break down and complete complex tasks. This complexity makes it difficult for businesses to monitor agent behaviour in real time, detect issues such as hallucinations, bias, or toxicity, and ensure outputs align with compliance or governance standards. Without clear oversight, trust in the system takes a backseat, especially when errors carry real-world consequences.
A bird’s-eye view into the functioning of agentic AI is essential. It helps reconstruct how decisions are made, identify failure points in interactions, and verify that outputs align with internal business expectations. Lack of visibility also impacts cost control. Without detailed visibility into token usage, system calls, or performance metrics, AI spend can rise quickly and unexpectedly, especially when working with third-party vendors. Pricing models and usage tracking vary depending on how these agents are built and how they interact with other systems, further complicating cost, resource optimisation, and business-wide AI agent adoption.
The imperative for intelligent observability
In order to make agentic AI trustworthy and scalable, businesses need more than just surface-level monitoring. Intelligent observability shifts the focus from simply identifying “what went wrong” to understanding “why” and “how” behind system behaviour. Collecting and correlating vast volumes of telemetry data, including metrics, events, logs, and traces (MELT), observability platforms help teams visualise how data moves across systems, services, and tools. This end-to-end visibility provides context that is otherwise missing, especially in agentic systems where outcomes are not always predictable or easy to trace.
With machine learning and AIOps capabilities built in, these platforms continuously analyse data, helping detect anomalies and performance drifts in real-time. Instead of relying on manual correlation across siloed tools, intelligent observability automates root cause analysis, linking together cues from logs, metrics, and traces to accurately lay bare underlying issues. Observability also plays a role in cost control. When agentic systems operate behind APIs and obscure layers, businesses often lack visibility into resource usage. Observability platforms help track token usage, resource consumption, generate alerts for unusual activity, and provide insights that support cost optimisation efforts.
Most importantly, intelligent observability makes agentic systems accountable for their actions. This forms the basis for self-healing systems, where observability platforms automatically trigger remediation workflows in response to detected issues. For example, scaling infrastructure components in response to surges in workload, or disabling non-critical features temporarily to preserve core functionality during periods of degraded performance. As agentic AI becomes a core part of enterprise workflows, the ability to observe, understand, and govern its behaviour will be critical. Intelligent observability delivers the clarity and control businesses need to confidently move forward.
Scaling with confidence
As businesses begin integrating agentic AI into operations, the potential for automation, speed, and precision is enormous. However, maximising its potential and scaling its influence demands careful, real-time oversight. Intelligent observability makes this possible by offering holistic visibility into system telemetry, agent outcomes, and resource usage.
Through continuous monitoring and analysis, observability ensures that agentic AI generates maximum value and operates within budget thresholds while remaining aligned with business goals and regulatory requirements. It also enables automated self-healing, making agents accountable for their actions by initiating corrective steps when anomalies are detected. Scaling agentic AI responsibly starts with embedding intelligent observability into the foundation. It provides the guardrails businesses need to maximise value, ensure reliability, and expand agentic AI operations with confidence.
