From dashboards to agentic observability
Agentic observability is the practice of designing observability data, tools, and workflows so autonomous AI agents can interpret telemetry, make decisions, and safely act on production systems without depending on human-readable dashboards. Cloud operations are being rebuilt for AI-driven and autonomous agents that are now a larger part of modern software systems, turning observability from a human console into a machine-actionable substrate for reasoning, governance, and control.
The headline change is not more charts; it is that operations are “going headless.” “AI agents won’t log in to view dashboards. They’ll pull what they need through APIs, reason about it, and act.” In this world, traditional observability teams face a stark choice: either treat telemetry as a product for agents, or risk unleashing powerful systems with poor context and weak guardrails.
The pressure is already visible. In a survey of 250 IT decision-makers, 84% reported increased cloud complexity and 69% said it is outpacing their operating model. That gap is what agentic observability tries to close—by making observability the foundation for AI agent monitoring, autonomous cloud operations, and AI-driven incident response, not an afterthought bolted onto dashboards.
New Relic and Azure: observability as an AI control plane
The clearest sign that observability is becoming a control plane comes from how major platforms are retooling for AI agents. One provider has announced New Relic Autopilot and New Relic Ground Truth, extending its observability stack so AI agents—not only engineers—can investigate incidents, retrieve context, and recommend fixes. Autopilot is framed as an automated SRE agent that starts work as soon as an alert fires, triaging incidents, hunting for root causes, and scoping remediation paths on top of governed telemetry.
This is agentic observability in action: observability as a data substrate for automated SRE work, not a reporting layer. Ground Truth goes further by giving external agents—like code assistants or custom orchestrators—API access to cleaned, contextual telemetry, with access rules and audit trails baked in. One large enterprise measured a 1.1% error rate across more than 1,300 users, a reminder that AI agents can be reliable but not infallible.
On another front, the Azure Copilot Observability Agent is now generally available, built on Azure Monitor to correlate logs, metrics, traces, topology, and operational context across agents, applications, infrastructure, and services. Its design assumes agents as first-class operators: systems generate signals, agents interpret them, take action, and learn from outcomes. By connecting observability, diagnosis, optimization, and remediation in a single platform, Azure Copilot ties insight directly to action and makes governance—policy, auditability, guardrails—a central part of autonomous cloud operations.
Verifiable execution and cryptographic trust for AI workflows
If observability is going to drive control, trust in the telemetry and workflows becomes non‑negotiable. That is the motivation behind Dapr 1.18, which introduces what Diagrid calls Verifiable Execution to bring cryptographic trust, provenance, and tamper-evident execution records to distributed applications and AI agents. Instead of trusting logs on faith, teams can ask: who initiated this workflow, what exactly ran, and has the history been altered?
New features like Workflow History Signing use SPIFFE-based identities to sign execution histories, making them tamper-evident and independently verifiable. Workflow History Propagation and Workflow Attestation extend this chain of custody across services and agents, so every hop in a complex AI workflow contributes to an auditable record. Together, these capabilities create “a model in which the history of a workflow becomes as trustworthy and auditable as the data it produces.”
This is a quiet but profound shift for AI agent monitoring. Historically, workflows optimised for durability and fault tolerance, not provenance. Now, with AI agents approving financial transactions or accessing sensitive data, verifiable execution becomes as important as uptime. Observability vendors that ignore this cryptographic layer risk shipping tools that tell you what happened—but cannot prove it to regulators, auditors, or downstream systems.
Auditability and governance: Obligra Verify and the new evidence layer
Trust is not only about how workflows ran; it is also about why specific decisions were made. That is the gap Obligra is targeting with the general availability of Verify, a system of record for AI-assisted operational decisions. As AI moves from experiments into daily operations—customer service, claims, fraud checks, healthcare workflows, financial decision support—the hard questions show up weeks or months later: what information did the AI see, what did it output, what context was present?
Verify responds by recording a complete decision context: prompts, responses, workflow context, timestamps, metadata, retrieval identifiers, environment details, and supporting evidence. This goes far beyond standard logs that only show that “something happened.” According to the company, this detailed record is aimed squarely at compliance review, operational investigations, legal inquiries, audit readiness, risk management, governance, and executive oversight.
In the era of autonomous cloud operations, this kind of evidence layer is not optional. Agentic observability that triggers actions without an audit trail is an incident report waiting to happen. By exposing a console, API, SDKs in Node.js and Python, infrastructure-as-code modules, and implementation guidance as part of its general availability release, Verify signals that observability must now archive the “why” behind AI-driven incident response and business decisions—not only the “what.”
The new operating model: from insight to controlled autonomy
The bigger picture is that cloud operations are entering a new era as AI-driven and autonomous agents take on a larger share of work. Observability is foundational to this shift because agents depend on real-time understanding of system behavior to reason, adapt, and act. But observability alone is not enough. The operating model that wins will combine agentic observability, verifiable execution, and decision recordkeeping into a governed loop of insight, action, and review.
One provider already reports customers using its Observability Agent to reduce manual effort, accelerate incident resolution, and improve operational clarity. Another shows AI SRE agents delivering measurable results with documented error rates. Meanwhile, Dapr proves you can attach cryptographic chains of custody to AI workflows, and Verify demonstrates how to preserve the full context of AI-assisted decisions for later review.
The conclusion is blunt: if your observability stack still assumes a human at the dashboard, it is outdated. Operations are shifting from reactive management to a continuous, agent-driven lifecycle of learning, adaptation, and control. Azure Copilot and similar agentic frameworks already connect observability, governance, and optimization so that insight flows into action with guardrails. The strategic question now is not whether you will adopt AI agents, but whether you will give them trusted, auditable, and constrained power—or leave them flying blind.
