1. What Is Agentic AI — And Why It Raises New Challenges
- From foundational models to agentic AI: While LLMs have largely served in “chat,” content generation, or coding roles, the leap to agentic AI involves agents that independently perform tasks, manage workflows, invoke tools, and make decisions based on changing inputs. These agents blur lines between user intention and machine action.
- Risks and blind spots: This higher level of autonomy creates new issues: resource consumption that exceeds expectations, unanticipated queries or behaviours, model drift, security challenges (like prompt injection or misuse of tools), and difficulties in attributing responsibility when something goes wrong.
2. Observability: Not Just Monitoring, But Deep, AI‐Aware Transparency
- MELT data expanded: The core observability signals—Metrics, Events, Logs, Traces (MELT)—remain fundamental. But with agentic AI, you also need data around decision paths, tool usage, token consumption, and model output quality.
- Front‐end + back‐end + AI workflows: Many organisations have mature visibility on user behaviour, front-end performance, infrastructure, and databases. What’s often missing is continuous, contextual visibility across the full agentic workflow—how AI agents interact with tools, trigger downstream processes, or consume computational and storage resources.
3. Real‐World Costs of Poor Observability
- Escalating resource usage: A financial institution example revealed that an automated AI reporting workflow — originally adopted for its ROI — eventually cost 10× more than the manual process it replaced. Without observability in place, the organisation couldn’t detect the degrading performance and runaway CPU usage until after a large overage. (Derived from statements by Mimi Shalash.)
- Downtime and trust: Failures in agentic systems can cascade, affecting end-users and customers. Issues left undetected reduce confidence and can damage reputation, not just revenues.
4. New Tools & Innovations in AI-Driven Observability
Recent product announcements illustrate how observability is evolving to meet the demands of agentic AI:
| InnovationKey Features |
| AI Troubleshooting Agents | Automatically sift through telemetry (logs, infrastructure events, recent deployments) to diagnose root causes and suggest fixes, reducing Mean Time To Resolution (MTTR). |
| Event Correlation & Alert Noise Reduction (Event iQ) | Tools that group related alerts, reduce false alarms, and help focus teams on what matters most. |
| Monitoring AI First-Class Components (AI Agent Monitoring, LLM Monitoring) | Special telemetry for AI-specific behaviours—reasoning chains, tool interactions, token usage. Ensuring that AI systems are visible and auditable. |
| Business Insights & Digital Experience Analytics | Connecting application performance and AI agent behaviour to business KPIs, user experience, revenue flow so observability isn’t just a technical tool but strategic. |
5. Best Practices & Guiding Principles for Observability with Agentic AI
To effectively monitor agentic AI in enterprise settings, several practices are becoming important:
- Integrate observability from day zero: Don’t treat observability as after-the-fact. Design AI systems with monitoring, tracing, and logging built in from the development stage.
- Trace decision paths: Capture not just “what happened” (error, slow query), but why—what tools an agent chose, what prompt/contexts fed it, how it reasoned.
- Balance granularity vs cost: Very detailed logs/traces are useful but can generate huge volumes of data (storage, cost, noise). Decide what to measure, when to sample, and what to aggregate.
- Correlate across domains: Link front-end user experience, back-end infrastructure health, and AI behaviour so that you can see where problems originate and how they ripple through.
- Define SLAs/KPIs for AI agents: Beyond general uptime or latency, include AI-specific metrics like output accuracy, hallucination rate, resource usage, cost per transaction, ethical compliance.
- Governance, compliance & ethics: Ensure audit trails exist, decisions are explainable where necessary, data privacy/ security rules applied, especially when agents handle sensitive data.
6. The Strategic Imperative: ROI, Trust, Resilience
- Showing value to leadership: Many organisations are past the experimentation phase. Boards and executives want proof of return—financial savings, risk reduction, faster delivery, better customer satisfaction. Observability gives concrete evidence.
- Avoiding tool sprawl: As enterprises adopt multiple monitoring tools for different layers (security, DevOps, front-end, etc.), maintenance burden, cost, and fragmented visibility increase. Unified observability platforms that incorporate AI-aware features are emerging as a solution. Mimi Shalash emphasises that consolidating tools (while investing in cognitive alignment and onboarding) is part of the path forward.
- Operational resilience: In a digital-first world, failures are not just technical issues—they have reputational, regulatory, financial implications. Observability helps maintain resilience by enabling proactive detection and correction of issues before they escalate.
Conclusion
As organisations accelerate adoption of agentic AI and increasingly rely on AI agents to automate workflows, make decisions, and generate content, observability ceases to be optional—it becomes foundational. Monitoring must evolve beyond traditional infrastructure and front-end metrics to encompass AI behaviours: reasoning paths, tool usage, decision trails, cost and performance signals specific to AI. Only with robust observability can enterprises maintain control, demonstrate ROI, prevent runaway costs, and ensure trust, resilience, and alignment with business goals in the age of autonomous AI agents.
Source:indianexpressGPT
