Enterprises have begun deploying autonomous and semi-autonomous AI agents in real-world scenarios. But a new operational challenge has emerged: how can we be sure what those agents are doing, why they are doing it, and whether their behavior aligns with our goals?
In agentic environments, AI models no longer react directly to a single prompt, but instead create ongoing decision loops during which they use tools, access data, and interact with other agents and systems.
Agent observability applies established observability principles—visibility, traceability, and accountability—to AI agents. This extends beyond basic model monitoring to capture telemetry about agent decisions, execution paths, data inputs, tool calls, and outcomes. With the right observability signals in place, teams can detect anomalous behavior, diagnose failures, and evaluate performance in real time as agents operate, rather than after problems surface.
Observability is a necessary component to running AI safely and effectively at scale. IT teams, AI developers, and enterprise stakeholders all need to understand how agent observability helps debug complex agent workflows, optimize performance and cost, and maintain confidence that agents are behaving as intended. Just as IT infrastructure and applications require observability to operate reliably, AI agents demand the same level of operational transparency as they take on more responsibility inside the enterprise.
What Is AI Agent Observability?
Modern agents, powered by large language models, do not follow deterministic paths. Their behavior can shift based on context, data availability, and model state. This makes visibility essential for understanding outcomes.
AI agent observability is the practice of gaining visibility into the internal states, decisions, and performance of autonomous AI agents as they operate. This concept builds on traditional software observability—logs, metrics, and traces—but extends it in important ways. In addition to execution signals, teams need insight into probabilistic reasoning, planning steps, tool usage, and intermediate decisions.
Agent observability becomes even more critical in multi-agent systems and dynamic environments. Organizations often deploy agents across multiple platforms and frameworks, which makes it difficult to maintain a unified view of behavior, risk, and performance without a consistent observability layer. Capabilities such as data security posture management play a role by surfacing how agents interact with sensitive data and whether those interactions align with organizational controls.
Simple AI scripts may only require basic logging and error tracking. Advanced agentic AI systems—those that plan, call tools, retrieve data, or collaborate with other agents—demand deeper observability to support safe operation and troubleshooting.
Why AI Observability Matters in the Real World
AI agents are probabilistic and autonomous. They can hallucinate, mis-handle retrieved context, or misuse tools in ways that look plausible until something breaks. And because agents can take actions, their errors can produce more severe downstream impacts than mistakes from chat-only systems.
Additionally, AI agents are moving into high-impact workflows where errors carry real consequences: financial decision support, security operations, and clinical and operational processes in healthcare. In these settings, transparency and explainability are necessary to manage risk and demonstrate responsible use. For example, research and practitioner guidance for the financial services industry has repeatedly emphasized that explainable AI supports compliance, trust, and risk governance. Healthcare transparency research similarly frames visibility into AI-driven decisions as essential for safe, accountable use.
Observability is what turns agent behavior into operationally manageable systems: it supports root cause analysis, reduces time to diagnosis, and provides the evidence trail needed for governance frameworks that emphasize measurement, monitoring, and ongoing risk management. For security teams, capabilities like threat monitoring can complement agent observability by correlating suspicious activity with the data and systems an agent has touched.
Table 1. How observability affects different agentic scenarios
Practical scenario | Observable agent behavior | Non-observable agent behavior |
Hallucinated output triggers a bad decision | Traces show retrieved sources, intermediate steps, and confidence signals for root cause analysis (RCA) | Only the final answer is visible; RCA becomes guesswork |
Tool call causes unintended change | Logs tie the action to a specific tool invocation, parameters, and permissions | Action appears as “something changed,” with no attributable chain |
Sensitive data exposure | Telemetry shows what data was accessed and where it flowed | Data access is opaque; exposure may be discovered long after the fact |
Compliance/audit inquiry | Evidence trail supports reporting and control validation | Limited artifacts; teams rely on manual reconstruction |
Key Telemetry Data and Metrics for Monitoring AI Agents
Effective AI agent observability depends on collecting the right telemetry. At a minimum, this includes detailed input and output logs, records of tool permissions and call sequences, decision paths, error rates, and execution traces. Together, these signals make it possible to reconstruct what an agent attempted to do, what it actually did, and where things may have gone wrong. For agentic systems that plan and act over multiple steps, visibility into intermediate decisions is often more important than the final output.
For LLM-based agents, teams also need insight into context window usage, token consumption, and latency across prompts, retrieval steps, and tool calls. Tracking token efficiency helps control cost and performance, while latency metrics highlight bottlenecks that can compound across long-running or multi-agent workflows. Time-series data ties all of this together, allowing organizations to observe trends over hours, days, or weeks and spot gradual degradation or sudden anomalies in agent performance. By integrating telemetry with anomaly detection and other security-aware signals, teams can correlate agent activity with unusual patterns that may indicate misconfiguration, misuse, or emerging risk.
The observability capabilities within Rubrik Agent Cloud are designed for these realities. The platform provides visibility into how agents interact with data, how actions unfold over time, and how behavior changes as agents scale across environments. This approach goes beyond generic monitoring tools by tying agent performance directly to data security and operational context.
Use Cases: Where Agent Observability Makes a Difference
AI agents are moving into operational roles across the enterprise. Agent observability turns autonomous AI systems in the following areas into operational assets that teams can measure, manage, and trust:
IT operations: Observe agents that automate remediation or incident prioritization, with clear traces showing why specific actions were taken and how they affected system stability.
Security: Track how agents triage threats, recommend controls, or initiate responses, making it possible to validate decisions and correlate activity with broader security signals.
Customer support: Audit agent interactions to review escalation paths, tool usage, and hallucinated or inaccurate responses that could affect customer trust.
Coding copilots: Monitor code suggestions and applied changes, especially when agents interact with production systems or repositories tied to sensitive workloads.
- DevOps and CI/CD: Observe deployment and QA agents managing infrastructure as code, helping teams diagnose failed releases or unintended configuration drift.
Regulated industries: Monitor agent access to sensitive data such as healthcare records or financial documents, supporting compliance and controlled data usage alongside capabilities like simulated cyber recovery.
Best Practices for Implementing Agent Observability
Agent observability should not be treated as an add-on. It needs to be built into agent design, deployment, and operations from the beginning so teams can detect risk early, diagnose issues quickly, and maintain confidence as agents scale.
Here are six AI agent abservability best practices that can help you get started:
Set clear observability goals: Define what matters most—auditability, performance tuning, identifying high-risk agents, detecting adverse actions, debugging complex workflows, or supporting forensic analysis after an incident.
Instrument agents from day one: Log reasoning paths, capture tool access and usage, record identity and permission context, track failures, and measure time-to-action for critical decisions.
Use structured logging and centralized pipelines: Standardized schemas and centralized observability tools make it possible to correlate activity across agents, platforms, and environments in real time.
Establish alerting and dashboards: Monitor for anomalous behavior, policy violations, and degraded performance, and surface issues before they impact users or systems.
Test observability in staging: Use synthetic workflows and edge cases to validate that telemetry, alerts, and dashboards behave as expected under realistic conditions.
Adopt centralized agent operations platforms: Platforms like Rubrik provide a unified view across agent development frameworks, helping teams connect agent behavior with other data risk signals.
Build Trust Through Agent Observability
Agent observability is fundamental to running AI systems that are reliable, accountable, and high-performing. As agents scale across domains and take autonomous action, organizations need continuous visibility into how decisions are made and how those decisions affect data and systems.
At enterprise scale, monitoring is not optional—it’s essential for managing risk, diagnosing failures, and maintaining operational control. Rubrik supports this approach with Rubrik Agent Cloud and strong data visibility and resilience practices that help organizations operate secure, observable, and auditable AI systems.
Interested in building secure, observable AI systems? Contact Rubrik today.