56 stories
In cloud-native operations, few scenarios are as maddening as the “phantom slowdown.” This happens when Grafana or Kubernetes dashboards show The post Where do you look when Kubernetes is healthy but requests stall? appeared first on The New Stack.
Token-level metrics, tiered logs, and OTel traces joined by a single trace_id.Continue reading on Towards AI »
When a CI/CD pipeline fails, its dashboard can usually show you what went wrong. Pipeline observability tools should help you understand why, whether the cause is a flaky test, an overloaded build agent, or a problem elsewhere in the delivery process.
In my previous article, I explored executor independence: the idea that different execution mechanisms should be able to satisfy the same operational specification. By executor independence, I mean th
How dynamic shredding makes the vast majority of queries in Logfire a thousand times faster, with early adopter customers reporting more than 1000x improvement on query performance.
Your agent is live. The service is healthy. But are its answers getting better? By connecting production traces, curated data, The post How to turn AI production feedback into better agents appeared first on The New Stack.
On-demand CPU and memory profiling for Cloudflare Workers and Durable Objects is now available. Now you can generate interactive flamegraphs directly in production to quickly track down memory leaks and performance bottlenecks.
Prasanna Vijayanathan and Renzo Sanchez-Silva share how Netflix tackles observability across 38M events/sec. They discuss replacing reactive monitoring with an AI-driven operational ontology and agentic workflows using Claude and graph databases.
Ryan is joined by Yanbing Li, Chief Product Officer at Datadog, to talk about applying observability to non-deterministic AI agents, blurring the boundaries between software development and production workflows, and navigating emerging challenges in AI security and tokenomics.
An AI SRE agent can help troubleshoot an incident. But can you trust it to decide on its own when The post Most AI incident tools still leave a key decision to humans appeared first on The New Stack.
AI agents are most powerful when grounded in real organizational data. In this demo, discover how to use Elastic Agent Builder to create an incident-investigation agent powered by Elastic Observability data (logs, metrics, and traces) to quickly diagnose application performance regressions.
TIL: Using Parseable with Datasette for OpenTelemetry traces I saw Parseable in a Show HN today - it's a new observability platform with both an open source (AGPL) Rust implementation (a single ~180MB binary), an "Enterprise" version with extra features and a cloud hosted option.
Dynatrace closed its $915 million acquisition of Arize on October 1, joining Arize’s tracing and evaluation of AI agents to The post Dynatrace’s $915M Arize deal bets AI agents are just another app to monitor appeared first on The New Stack.
Palo Alto Networks introduced a new approach to AI-driven observability last week, signaling a shift from dashboards and manual incident The post XCOR launches to trace outages in minutes. It still pages engineers. appeared first on The New Stack.
Susan Chang explains how Elastic transitioned from siloed, ad-hoc AI agent evaluations to a unified, production-grade framework. She discusses balancing LLM-as-a-judge with deterministic rules, bridging Python data science evals with TypeScript production code, and implementing deep tracing to…
TL;DR: Pick a gateway for moderation classification by the evidence it preserves, not by how many model names fit behind one key. Require every provider path to produce the same small JSON contract, validate it before accepting the result, and record why routing or fallback happened.
The least complex cheap LLM API gateway that works for logistics moderation is the one that makes every accepted report traceable to a tenant, a queue item, and an estimated cost before a human-review deadline is threatened. A single API key is convenient, but it is not the control plane.
TL;DR: For a small B2B SaaS, combine grouped exceptions, structured logs, and a few aggregate failure metrics, then let one polling worker correlate them by request ID or trace ID.
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