This week, New Relic launched its AI agent platform alongside enhanced OpenTelemetry tools, signaling a definitive end to the era of passive monitoring. It represents a shift from “observability” as a noun to “observability” as a verb, where AI agents reason about telemetry, query APIs, and execute runbooks.
Plavno’s Take: What Most Teams Miss
Most teams misunderstand the failure mode of AI in operations. They worry about the AI being “wrong” about a root cause, but the real danger is the AI being “right” about the wrong thing. An agent might correctly identify a failing pod and restart it, but without checks for critical locks or batch transactions it can cause cascading failures.
We see teams rushing to hook GPT‑4 up to their Kubernetes API without a mediation layer. The core mistake is treating the AI agent as a super‑powered SRE rather than a tool that requires strict guardrails, scoped permissions, and a deterministic “human‑in‑the‑loop” for destructive actions.
What This Means in Real Systems
Architecturally, agentic observability requires a shift from a push‑based data pipeline to a pull‑based reasoning loop. The agent needs a “context window” that includes structured telemetry, vector‑searchable incident history, and runbooks.
When an alert fires—e.g., p99 latency breaches 500 ms—the agent queries a vector database for similar incidents, retrieves logs, git commits, and resolution steps. It then proposes actions via a sandboxed API endpoint such as /ops/remediate that validates against a policy engine (OPA) before execution.
Latency matters: a 30‑second reasoning loop may miss time‑outs. Fast‑path deterministic scripts handle known issues, while the AI handles novel diagnostics.
Why the Market Is Moving This Way
The volume of telemetry from micro‑services makes human triage impossible. Alert fatigue leads to outages. Automating response reduces risk, and the maturity of OpenTelemetry (OTel) enables agents to understand data across stacks without custom integrations.
Vector databases make months of incident history semantically searchable, merging monitoring and runbook automation into a single control plane.
Business Value
For a SaaS company generating $100k ARR, every hour of downtime is costly. Typical incident response involves 3‑5 engineers for 2‑4 hours. An agentic system can cut “time to investigate” by ~70%.
Our benchmarks with AI automation show MTTR dropping from 45 minutes to under 15 minutes for Tier 2 incidents. Additionally, automated scaling and leak detection can save 10‑15% of cloud spend.
Real‑World Application
E‑Commerce Auto‑Scaling
During a flash sale, an agentic system detects traffic spikes before CPU saturates, pre‑warms checkout containers, and maintains sub‑200 ms response times.
Fintech Database Deadlock
An agent correlates error logs with slow‑query logs, identifies a lock on user_balance, kills the hung process via an orchestration API, and opens a Jira ticket—all within 90 seconds.
SaaS API Rate Limiting
When a client hits 429 errors, the agent adjusts the tenant‑specific rate‑limit policy and sends optimized code via webhook, turning a churn risk into a support win.
How We Approach This at Plavno
We treat agentic observability as a custom software challenge. We define blast radius, implement tiered permission models, and use AI consulting to map incident response workflows before any code is written.
We enforce JSON schema validation for agent actions, log every decision, and provide audit trails for compliance and continuous improvement.
What to Do If You’re Evaluating This Now
- Read‑Only Integration: Connect the agent to telemetry data without write access. Let it generate incident summaries and compare to actual reports.
- Define the Playbook: Formalize runbooks before automating. The agent can only act on documented procedures.
- Scope the Tools: Start with safe integrations (ticketing, Slack) before granting Kubernetes access.
- Test Failure Modes: Simulate hallucinations and ensure policy layers block unsafe actions.
Conclusion
Agentic platforms turn monitoring from passive observation into automated reasoning. They can dramatically reduce MTTR and free engineers from toil, but without guardrails they can also take down your stack. The winners will treat AI agents as junior engineers with strict supervision, clear instructions, and robust safety nets.
