Airrived Introduces Agentic Observability for AI Agent Monitoring and Risk Management

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Airrived will launch Agentic Observability, a significant enhancement to its enterprise Agentic OS designed to provide comprehensive visibility into AI agent operations.

Airrived introduces Agentic Observability to monitor AI agent activities and associated risks

Airrived will launch Agentic Observability, a significant enhancement to its enterprise Agentic OS designed to provide comprehensive visibility into AI agent operations. This system enables organizations to track AI agent behavior throughout the entire process, from initial data input into the platform, through reasoning and execution phases, to the final business outcomes.

Key questions include:

who developed this agent? What resources does it access? What actions is it authorized to perform? Which AI tools does it utilize? What data is being processed? Is human oversight required for its decisions? Ultimately, what decisions does it make, and what are the financial or operational consequences?

Agentic Observability consolidates all these insights into a unified control plane.

The shift in enterprise technology from rule-based software to autonomous agents that reason, decide, and act necessitates new governance frameworks, according to Anurag Gurtu, CEO of Airrived. Transparency is essential for effective management, he stated. Agentic Observability offers visibility spanning data intake, decision-making, execution, and resulting outcomes.

“Transparency is essential for effective management,” said Anurag Gurtu, CEO of Airrived.

Tracking the agent lifecycle

Airrived’s solution provides oversight across the complete agentic workflow: enterprise integration, context lake, agentic applications, individual agents, actions taken, and final outcomes. Organizations can follow data as it transitions from enterprise systems into Airrived’s Context Lake, through agentic applications and specific agents, and ultimately to the actions and results generated by those agents.

Unlike conventional systems

Unlike conventional systems that merely confirm agent execution, Airrived reveals the full operational environment surrounding each action. For every agent and agentic application, the platform discloses its creator, administrator, associated users, roles, permissions, and authorized actions, along with whether human approval is required before execution.

The platform also identifies the underlying AI tools

The platform also identifies the underlying AI tools used to develop and operate each agent, including both Airrived’s proprietary technologies and third-party AI systems integrated by customers.

Context Lake: Establishing enterprise context for agents

At the core of Airrived’s Agentic OS is Context Lake, a centralized repository that aggregates data and operational context from diverse enterprise systems. This integration allows Airrived to monitor agents within the broader enterprise ecosystem, rather than as isolated processes.

Organizations can trace the contextual information

Organizations can trace the contextual information available to an agent, its access permissions, subsequent actions, and resulting outcomes, creating a transparent chain that directly links enterprise data to AI-driven decisions and outputs.

Aligning technical processes with business impacts

Agentic Observability extends beyond infrastructure metrics by directly connecting AI execution to business and operational outcomes. Depending on the application, this includes security alerts, identity management, high-risk vendor notifications, and root-cause analysis across IT operations.

The system establishes a clear trail

The system establishes a clear trail from enterprise data entering an AI system to the final decision or action it produces.

Monitoring risk and financial implications

Airrived also addresses two previously opaque aspects of AI operations: exposure of sensitive data and AI cost management. Organizations can track the presence and movement of confidential information, such as personally identifiable information (PII), payment card data (PCI), and protected health information (PHI), throughout agentic workflows.

Simultaneously, token and model usage tracking

Simultaneously, token and model usage tracking provides visibility into AI operational costs, transforming previously unclear financial metrics into measurable AI FinOps data.

Every agent must have an accountable owner

Every agent must have an accountable owner, every action requires explicit permissions, every interaction with sensitive data demands transparency, every AI expenditure needs financial oversight, and every decision must produce observable results, Gurtu emphasized.

Establishing the foundation for agentic enterprise infrastructure

As organizations transition from testing individual AI assistants to managing networks of autonomous agents, Airrived positions observability as a critical infrastructure component for enterprise AI, comparable to identity and access management today.

The Agentic OS integrates Context Lake

The Agentic OS integrates Context Lake, AI applications, agents, orchestration, reasoning, models, governance, observability, and infrastructure into a single platform. With Agentic Observability, enterprises can develop, deploy, and monitor agents while maintaining full transparency into their activities.