# Traces The path of a request through an application. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Agent Anomaly Detection, now in Private Preview on the Gemini Enterprise Agent Platform DevFeed: [Agent Anomaly Detection, now in Private Preview on the Gemini Enterprise Agent Platform]() Original publisher: [Read original article]() Author: Achuth Narayan Rajagopal Published: 2026-09-17T01:25:27.608736Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [agent observability](), [OpenTelemetry](), [Security](), [Traces](), [API]() Tags: [agent](), [anomaly-detection](), [api](), [autonomous-agents](), [gemini](), [opentelemetry](), [security]() ### AI overview Google announces Agent Anomaly Detection in private preview on the Gemini Enterprise Agent Platform. The feature analyzes agents' reasoning traces, tool calls, logs, and execution flows to identify behavioral anomalies, suspicious intent, and policy violations. ### Source excerpt Agent Anomaly Detection is a new, out-of-band oversight layer for the Gemini Enterprise Agent Platform that analyzes OpenTelemetry traces and tool calls to catch behavioral risks without adding runtime latency to live requests. It utilizes a multi-tiered detection pipeline--combining lightweight statistical scanning with deep LLM-based reasoning--to identify logical anomalies and policy violations grounded in the OWASP Agentic Top 10. Developers can triage these automated findings within Security Command Center or leverage the exposed API to programmatically block subsequent tool calls when an agent breaches defined risk thresholds. ## Optimizing agent system prompts with Amazon Bedrock AgentCore DevFeed: [Optimizing agent system prompts with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Han Ding Published: 2026-09-16T15:47:39Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [Prompt optimization](), [Prompt Engineering](), [Traces](), [Benchmark](), [A/B Testing](), [Automated prompt engineering]() Tags: [a-b-testing](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [benchmark](), [best-practices](), [evaluation](), [intermediate-200](), [open-source](), [prompt-optimization](), [system-prompts](), [traces]() ### AI overview This technical companion explains how Amazon Bedrock AgentCore's system prompt optimizer uses production agent traces and reward signals to propose revised system prompts. It describes the reflector engine, recommendation explanations, offline batch evaluation, online A/B testing, and promotion workflow, and reports benchmark results for Single Agent Reflector and the experimental open source Sub-Agent Reflector. ### Source excerpt AgentCore optimization turns production traces into proposed configuration changes, then validates them before promotion. This technical companion to the launch post explains how the system prompt optimizer's reflector engine works and shares benchmark results for the Single Agent and Sub-Agent Reflectors. ## Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time DevFeed: [Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time]() Original publisher: [Read original article]() Author: Egor Klimov Published: 2026-09-16T12:34:47Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [OpenTelemetry](), [Microservices](), [observability](), [telemetry](), [Traces](), [ide]() Tags: [all-things-web](), [backstage](), [environment-variables](), [goland](), [ide](), [idea](), [intellij-idea](), [intellij-platform](), [jetbrains](), [microservices](), [observability](), [opentelemetry](), [performance-optimization](), [plugin-development](), [plugin-highlights](), [plugins](), [pycharm](), [research](), [rider](), [telemetry](), [traces](), [webstorm]() ### AI overview This article explains how the JetBrains OpenTelemetry Plugin generates a service map from runtime telemetry. It describes using logs, metrics, and especially standardized trace spans to visualize how microservices communicate, along with the plugin's lightweight local OpenTelemetry backend. ### Source excerpt We've all been there: you join a new project, and the first thing you ask for is the architecture diagram. You're handed a diagram that looks great, but after a week of debugging, you realize it's six months out of date. Service A hasn't talked to Service B since the spring, and there's a new [...] ## 4 Speculative Decoding Variants DevFeed: [4 Speculative Decoding Variants]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-12T20:03:46Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [dynatrace](), [MCP](), [MCP Server](), [Traces](), [Latency]() Tags: [coding-agents](), [dynatrace](), [latency](), [mcp](), [mcp-server](), [traces]() ### AI overview This article explains how Dynatrace's open-source MCP server and related skills bring production traces and logs into coding agents for performance investigations. It also introduces speculative decoding, in which a cheaper drafter proposes tokens and a target model verifies them in parallel, then outlines variants based on how drafts are produced. ### Source excerpt ...covered with tradeoffs in production. ## How to add Sentry monitoring to a Webflow Cloud app DevFeed: [How to add Sentry monitoring to a Webflow Cloud app]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [error tracking](), [Next.js](), [Workers](), [session replay](), [debugging](), [Traces](), [configuration](), [Instrumentation](), [Command-line interface]() Tags: [cloudflare-workers](), [debugging](), [error-tracking](), [guides](), [how-to](), [instrumentation](), [monitoring](), [next-js](), [session-replay](), [traces]() ### AI overview This tutorial explains how to manually add Sentry monitoring to a Webflow Cloud app running Next.js. It covers SDK installation, initialization files, environment variables, a Cloudflare-specific tunnel fix, verification, alerts, and common silent failure modes. The setup enables error grouping, stack traces, performance traces, and session replay. ### Source excerpt Learn how to add Sentry monitoring to a Webflow Cloud Next.js app. Get error grouping, stack traces, and session replay working. ## Give Your Coding Agents a Memory You Own DevFeed: [Give Your Coding Agents a Memory You Own]() Original publisher: [Read original article]() Author: David Corvoysier Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI-assisted coding](), [Traces](), [Inference](), [dataset]() Tags: [agents](), [announcement](), [coding](), [datasets](), [embedding](), [guide](), [inference](), [local](), [memory](), [open-source](), [retrieval](), [traces]() ### AI overview funes is a local, durable memory layer for coding agents that indexes prior session traces so agents can retrieve past decisions with provenance. It uses a deterministic pipeline with vector and BM25 search, reranking, recency weighting, and local storage. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Monitor prompt caching to optimize your token usage DevFeed: [Monitor prompt caching to optimize your token usage]() Original publisher: [Read original article]() Author: Thomas Sobolik Published: 2026-09-02T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [Caching](), [Large Language Model](), [Latency](), [Traces](), [SIEM, Security, Observability](), [anthropic]() Tags: [agent-observability](), [agents](), [ai](), [ai-engineering](), [ai-observability](), [anthropic](), [caching](), [cost](), [how-to](), [latency](), [llm](), [models](), [monitor](), [openai](), [traces]() ### AI overview A tutorial on prompt caching for LLM and agent workloads, covering cache behavior, provider differences, and monitoring token use and latency. ### Source excerpt Learn how to use prompt caching effectively and monitor your models and agents to troubleshoot cache invalidations. ## How Uken Games reduces observability costs by 87% with ClickHouse DevFeed: [How Uken Games reduces observability costs by 87% with ClickHouse]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-09-01T15:28:24Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [telemetry](), [Traces](), [Grafana]() Tags: [clickhouse](), [cost](), [observability](), [opentelemetry](), [traces]() ### AI overview Uken Games replaced Datadog with an open-source observability stack centered on ClickHouse, cutting observability costs by 87%. The stack stores traces in ClickHouse and uses OpenTelemetry collectors with SigNoz, Prometheus, and Grafana. ### Source excerpt Uken Games replaced Datadog with an open-source observability stack built on ClickHouse, cutting costs by 87% while storing every trace on a single node. ## Making Rust observability reliable at scale with OpenTelemetry DevFeed: [Making Rust observability reliable at scale with OpenTelemetry]() Original publisher: [Read original article]() Author: Björn Antonsson; Paul Le Grand des Cloizeaux; Scott Gerring Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [tracing](), [telemetry](), [Traces](), [Instrumentation]() Tags: [apm](), [instrumentation](), [latency](), [observability](), [opentelemetry](), [production](), [rust](), [scale](), [traces](), [tracing]() ### AI overview Datadog describes building an opinionated Rust tracer on OpenTelemetry to improve trace propagation, sampling consistency, and trace quality in production services. ### Source excerpt Learn how Datadog improved Rust tracing by building an opinionated OpenTelemetry-based library to help ensure consistent sampling, propagation, and trace quality at scale. ## Agents and Xanalogical Computing: Versioned, Traceable References DevFeed: [Agents and Xanalogical Computing: Versioned, Traceable References]() Original publisher: [Read original article]() Author: Nathan Sobo Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [Zed Industries - Blog]() Topics: [Code](), [Web](), [Traces]() Tags: [agent](), [agents](), [code](), [traces](), [web]() ### AI overview The article revisits Ted Nelson's vision of Xanadu and argues that agents make its ideas more practical. Agents can follow source and discussion layers, preserve references to exact text spans, and cite those connections so humans can audit their work. ### Source excerpt Ted Nelson specified the docuverse sixty years ago. The technology finally arrived, and so did its native users. ## Monitor Azure Functions across every hosting plan with Datadog DevFeed: [Monitor Azure Functions across every hosting plan with Datadog]() Original publisher: [Read original article]() Author: Duncan Harvey; Kathie Huang; Piyali Banerjee Published: 2026-08-24T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Azure](), [serverless monitoring](), [Serverless](), [telemetry](), [tracing](), [Traces](), [Application Performance Management (APM)](), [cpu](), [Node.js](), [Python](), [.NET]() Tags: [apm](), [azure](), [azure-functions](), [cpu](), [digital-experience-monitoring](), [metrics](), [monitor](), [monitoring](), [net](), [node-js](), [observability](), [performance](), [python](), [serverless](), [serverless-monitoring](), [telemetry](), [traces](), [tracing]() ### AI overview Datadog Serverless Monitoring expands observability for Azure Functions across Linux and Windows hosting plans, with telemetry collection, distributed tracing, enhanced CPU metrics, and continuous profiling. ### Source excerpt Trace requests, get enhanced CPU metrics, and profile code across Azure Functions hosting plans with Datadog Serverless Monitoring. ## How Ora benchmarks every major AI agent on Vercel DevFeed: [How Ora benchmarks every major AI agent on Vercel]() Original publisher: [Read original article]() Author: Kevin Sundstrom Published: 2026-08-21T21:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [AI Agent](), [benchmarking](), [Vercel](), [agent observability](), [Traces](), [Claude Code](), [Latency](), [ChatGPT](), [OpenClaw](), [backends](), [Front end]() Tags: [ai](), [ai-agent](), [authentication](), [benchmarks](), [chatgpt](), [claude-code](), [deployment](), [latency](), [openclaw](), [software](), [tools](), [traces](), [vercel](), [web]() ### AI overview Ora benchmarks major AI agents by running them against live customer websites and recording the cost, latency, steps, and traces required to complete workflows. Built on Vercel, the platform provides the front end, back end, and agent runtime through one deployment path. ### Source excerpt Ora on Vercel Front end, back end, and agent runtime on one platform Every major agent tested side by side on live sites Hundreds of commits a day from a 16-person engineering team Ora sends agents onto live websites with instructions to sign up for a product, integrate with it, and pay for it. Agents often fail, and by Ora's estimate, 99% of the web isn't agent-ready. The platform shows customers where and why agents fail, and what to change. Assaf Elovic, co-founder of Ora, spent years helping agents discover the web. His previous company, Tavily, built a web search engine for AI agents and was acquired by Nebius earlier this year. Search solved half the problem, but an agent that finds a product still has to actually use it. He and co-founder Liad Yosef started Ora to measure how ready the web is for agents, and to fix the parts that aren't. Today that means spawning agents against live customer sites from journey.ora.ai, where Ora runs a journey and records the cost, latency, and steps an agent needs to finish a task. The platform runs on Vercel, including the agent runtime. Benchmarking every major agent side by sideEvery harness expects its own infrastructure Agents decompose into two parts: a model, which does the reasoning, and a harness, the software that gives the model its tools and drives it from step to step. Ora's lineup covers the agents customers use most: Claude Code, ChatGPT, Gemini, Hermes, OpenClaw, and eve, Vercel's agent framework. Ora runs each agent on a customer's website and watches how it handles common workflows. No two harnesses want the same infrastructure. Each expects its own environment and exposes its steps differently, so Ora runs a separate runtime for every harness and traces every step. Ido Finder, who leads engineering at Ora, calls that side-by-side coverage one of the most valuable things ora brings to its customers. When an agent stalls in a signup flow, the customer sees which step and what it tried. Without the trace, the ## Always-on tracing for production and preview traffic DevFeed: [Always-on tracing for production and preview traffic]() Original publisher: [Read original article]() Author: wits Published: 2026-08-21T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [tracing](), [Traces](), [Vercel](), [Command-line interface](), [telemetry]() Tags: [cli](), [debug](), [infrastructure](), [logs](), [traces](), [tracing](), [vercel]() ### AI overview Vercel introduces always-on tracing for production and preview traffic, allowing teams to debug real user requests through continuously collected, sampled traces. Sampling rules control collection by environment and path, with CLI management, automatic infrastructure and outbound fetch spans, optional framework and custom instrumentation through @vercel/otel, and trace viewing in Logs or via the CLI. ### Source excerpt With always-on tracing, you can now debug your real user requests without reproducing them. Always-on tracing continuously collects traces from your production and preview traffic. Unlike session tracing, which only captures requests from your own browser, it samples your live traffic. You control what's collected with sampling rules. Each rule sets a trace rate for an environment (All, Production, or Preview), optionally scoped to a path prefix like /checkout. Nothing is collected until you add a rule, so you pay only for what you choose to trace. You can also manage sampling rules from the CLI. vercel traces config set adds a rule, or changes the rate of an existing one when you run it again with the same environment and path prefix: Infrastructure and outbound fetch spans are captured automatically. To add framework and custom spans, instrument your app with @vercel/otel. To view a trace, open the Logs or run vercel traces get . Always-on tracing is is now in beta and available to teams on all plans. Tracing is priced at $0.50 per 1M span units. Traces follow your runtime log retention: 1 hour on Hobby, 1 day on Pro and 3 days on Enterprise. Get started by adding a rule in Settings -> Tracing or from the settings icon in Logs. Learn more in the documentation. Read more ## Control trace volume with OpenTelemetry tail-based sampling DevFeed: [Control trace volume with OpenTelemetry tail-based sampling]() Original publisher: [Read original article]() Author: Bill Meyer; Eddie Cai Published: 2026-08-21T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [OpenTelemetry](), [Traces](), [tracing](), [observability](), [Application Performance Management (APM)](), [configuration](), [Latency]() Tags: [apm](), [architecture](), [distributed-systems](), [how-to](), [latency](), [learn](), [metrics](), [observability](), [opentelemetry](), [rideshare](), [synthetic](), [traces]() ### AI overview A guide to using OpenTelemetry tail-based sampling to reduce exported trace volume while retaining errors, slow requests, and other diagnostically valuable traces. It explains the difference between head- and tail-based sampling, the role of Span Metrics, and the collector architecture required to evaluate complete traces. ### Source excerpt Learn how to configure tail-based sampling in the OpenTelemetry Collector to drop noisy traces, keep the ones that matter, and control APM costs. ## Centralize human and agentic work with Datadog Work Management DevFeed: [Centralize human and agentic work with Datadog Work Management]() Original publisher: [Read original article]() Author: Roxanne Moslehi Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Agent](), [incident](), [site-reliability-engineering](), [Slack](), [SRE](), [SIEM, Security](), [GitHub](), [error tracking](), [dashboards](), [Traces]() Tags: [agentic](), [ai](), [ai-agent](), [cloud-siem](), [dashboards](), [devops](), [devsecops](), [error-tracking](), [github](), [incident](), [management](), [slack](), [sre](), [traces](), [work-management](), [workflow-automation]() ### AI overview Datadog Work Management centralizes work created by people, automations, and Datadog AI agents. It preserves context from logs, traces, monitors, alerts, ownership, assignments, approvals, artifacts, and activity while integrating with Datadog and external collaboration systems. ### Source excerpt Learn how Datadog Work Management helps you coordinate human and AI agent-driven work while preserving context, ownership, and activity across tools. ## Set up coding agents in one command with AI Gateway DevFeed: [Set up coding agents in one command with AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-08-12T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI-assisted coding](), [Vercel](), [Command-line interface](), [API keys](), [dashboards](), [observability](), [Traces](), [API]() Tags: [api](), [api-keys](), [cli](), [coding](), [coding-agents](), [observability](), [traces](), [vercel]() ### AI overview Vercel's AI Gateway adds one-command setup for coding agents, centralizing model access, API keys, observability, billing, budgets, and team-wide provider policies. It supports more than 200 models with automatic fallbacks and works with agents including Claude Code, Codex, and OpenCode. ### Source excerpt Using coding agents means setting up multiple accounts, provisioning API keys, and scattering observability and billing. Now, you can route them through AI Gateway to centralize all of this and add controls, with set up in one command: Any of 200+ models in any agent, including models the agent has no native support for. The gateway routes the request to whichever model you picked, with automatic fallbacks. One dashboard for agent traffic: spend, performance, tokens and more, across the models your agents used. Budgets, resets, and expiry on the keys agents use to control spend. Team-wide policy your agents can't route around: turn on Zero Data Retention or restrict which providers may serve requests, and it holds for every agent request without editing an agent's config. Request traces, with the cost, tokens, and model behind each request. To start: vercel ai-gateway coding-agents setup connects Claude Code, Codex, OpenCode, and more in one command. It finds the agents on your machine, creates the API key, and writes the gateway URL and your credentials into each agent's own config format. Agent Selecting a model Claude Code, CLI and desktop /model or /model , as in /model anthropic/claude-opus-5 Codex, CLI and desktop /model or codex --model OpenCode /models, then pick vercel// Pi /model, or pi --list-models Cline /model in CLI, or go to settings to change models in IDE extension Cursor Add gateway slugs under Settings, then Models Hermes /model custom:vercel-ai-gateway: Kilo Code /models, then pick openai-compatible// OpenClaw Add slugs to the starter list in openclaw.json Files are edited in place with their formatting preserved, and setup does not pin to a model. This command supports 9 coding agents to utilize the models in AI Gateway. You can also use the /model in-line command for a selector with all models: refer to the models list for the full catalog. Pass --yes to take the defaults, or set the key up fro ## AI SRE: Cut MTTR in Half with Autonomous Incident Resolution DevFeed: [AI SRE: Cut MTTR in Half with Autonomous Incident Resolution]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-08-10T11:34:38Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [site-reliability-engineering](), [incident](), [Artificial Intelligence](), [monitor](), [dashboards](), [Traces](), [archive search](), [Pull Request](), [codex](), [cursor]() Tags: [agent](), [ai](), [codex](), [cursor](), [dashboards](), [datadog](), [incident](), [monitor](), [monitoring](), [on-call](), [sre](), [traces](), [workflow]() ### AI overview This developer article describes an autonomous incident-resolution workflow built in Port. It argues that incident response time is largely spent reconstructing context from alerts, logs, dashboards, traces, ownership data, recent changes, and runbooks. The proposed agent-based workflow aims to use that full context to triage, diagnose, and fix production incidents, with a reported 50% reduction in MTTR. The supplied text ends during a discussion of why simply routing alerts to coding agents can fail. ### Source excerpt See how an SRE agent workflow cuts MTTR in half, using full context to triage, diagnose, and fix incidents autonomously, all the way to a RCA. ## Centralize cross-account Amazon ECS telemetry with an ADOT gateway DevFeed: [Centralize cross-account Amazon ECS telemetry with an ADOT gateway]() Original publisher: [Read original article]() Author: Rahul Kumar Published: 2026-08-06T16:13:46Z Content type: tutorial Language: en Sources: [Containers]() Topics: [Amazon Elastic Container Service](), [telemetry](), [OpenTelemetry](), [SIEM, Security, Observability](), [Amazon CloudWatch](), [Amazon Web Services](), [Windows](), [.NET Framework](), [Traces](), [log management](), [Amazon Elastic Kubernetes Service]() Tags: [amazon-cloudwatch](), [amazon-eks](), [amazon-elastic-container-service](), [amazon-elastic-kubernetes-service](), [aws](), [intermediate-200](), [logs](), [metrics](), [net-framework](), [observability](), [opentelemetry](), [technical-how-to](), [telemetry](), [traces](), [windows]() ### AI overview This tutorial presents a centralized AWS Distro for OpenTelemetry gateway for collecting telemetry from Amazon ECS workloads across multiple AWS accounts. It replaces per-task sidecars, supports Windows .NET Framework tasks, receives OTLP over private connectivity, and exports traces to AWS X-Ray plus metrics and logs to Amazon CloudWatch. ### Source excerpt Running an OpenTelemetry collector as a sidecar in every Amazon ECS task does not scale across a multi-account estate, and it cannot run at all on Windows. Learn how to replace per-task sidecars with a single centralized ADOT gateway that ingests OTLP from workloads across accounts and exports traces to AWS X-Ray and metrics and logs to Amazon CloudWatch. ## Export AI Gateway traces with Vercel Drains DevFeed: [Export AI Gateway traces with Vercel Drains]() Original publisher: [Read original article]() Author: Chloe Tedder Published: 2026-08-05T05:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [OpenTelemetry](), [Traces](), [Vercel]() Tags: [ai](), [integrations](), [opentelemetry](), [traces](), [vercel]() ### AI overview Vercel AI Gateway now creates an OpenTelemetry trace for every request. Pro and Enterprise teams can export these traces through Vercel Drains to OTLP/HTTP endpoints and supported integrations, with details such as routing, retries, token usage, costs, latency, status, and attribution. ### Source excerpt AI Gateway now produces an OpenTelemetry trace for every request. Pro and Enterprise teams can send these traces through Vercel Drains to any OTLP/HTTP-compatible endpoint, including native integrations for Braintrust, Dash0, Kubiks, Sentry, and Statsig. Each trace shows the full request lifecycle, including: Model and provider routing Fallback and retry attempts Token usage and cost Time to first token, request duration, and response status Project, deployment, API key, environment, and custom tag attribution Trace Drains do not include prompt or completion content. Sampling controls let you choose how much traffic to export to each drain. AI Gateway traces cost $0.05 per 1,000 traces delivered to each drain, plus the standard Drains rate of $0.50 per GB of data transferred. A request counts once per successful drain delivery, even when AI Gateway makes multiple provider attempts. Failed deliveries do not incur a trace charge. Set up a Trace Drain from your team's Drains settings or learn more in the AI Gateway documentation. Read more ## OpenTelemetry: The platform engineer's path to standardized observability DevFeed: [OpenTelemetry: The platform engineer's path to standardized observability]() Original publisher: [Read original article]() Author: Sam Barlien Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [OpenTelemetry](), [observability](), [telemetry](), [Instrumentation](), [Traces](), [Kubernetes]() Tags: [cncf](), [incident](), [instrumentation](), [kubernetes](), [logs](), [metrics](), [observability](), [opentelemetry](), [platform](), [sdks](), [telemetry](), [traces](), [vendor-lock-in]() ### AI overview This article explains how OpenTelemetry standardizes observability across platform services by providing vendor-neutral APIs, SDKs, semantic conventions, and a Collector for routing and processing telemetry. It connects logs, metrics, and traces while allowing teams to choose analysis backends later, reducing fragmentation and vendor lock-in. ### Source excerpt Standardize observability across your platform with OpenTelemetry. Eliminate fragmented telemetry, reduce incident resolution time, and avoid vendor lock-in. ## RAG debugging guide: fast ways to reduce retrieval errors DevFeed: [RAG debugging guide: fast ways to reduce retrieval errors]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-07-21T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [debug](), [Monitoring](), [AI search](), [Latency](), [Traces]() Tags: [debugging](), [docs](), [embedding](), [errors](), [guide](), [latency](), [logs](), [monitoring](), [rag](), [retrieval](), [tech-de](), [traces](), [vector]() ### AI overview A practical guide to diagnosing five common production RAG failures across retrieval, ranking, grounding, freshness, and latency. It explains how chunking, vocabulary mismatch, monitoring, logs, and traces can reveal why a system returns confidently incorrect answers. ### Source excerpt Your RAG-backed support assistant just told a customer the refund window is 30 days. It's 14. The retrieval logs look clean: chunks came back, latency was normal, nothing errored. That's what makes RAG failures slippery. The pipeline still returns an ... ## OpenTelemetry Has Graduated... Now what? DevFeed: [OpenTelemetry Has Graduated... Now what?]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-07-15T18:59:02Z Content type: opinion Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [telemetry](), [Open Source](), [vendor lock-in](), [Instrumentation](), [Traces]() Tags: [cncf](), [instrumentation](), [open-source](), [opentelemetry](), [telemetry](), [traces](), [tracing](), [vendor-lock-in]() ### AI overview This article discusses OpenTelemetry's CNCF graduated status, its history as a unified standard for traces, logs, and metrics, and the role of standardized APIs, language implementations, and the Collector in reducing vendor lock-in. ### Source excerpt In case you missed it: OpenTelemetry (OTel) has officially achieved CNCF graduated status! It now stands proudly alongside amazing open source projects such as Kubernetes and Prometheus, to name just a few. It's been a long journey, and we're very excited... But, now what? To understand where we're going, it's important to understand where we came from. History In the not-so-distant past, telemetry signals were not standardized. This meant telemetry formats differed from tool to tool, with each telemetry vendor creating and maintaining its own instrumentation libraries. Vendor lock-in was a huge problem: If you wanted to switch vendors, you had to strip out the previous vendor's libraries from your code and replace them with the new vendor's libraries. As a result, switching vendors was a nontrivial task. ## Endform joins the Vercel Marketplace DevFeed: [Endform joins the Vercel Marketplace]() Original publisher: [Read original article]() Author: Sam Halstead Published: 2026-07-14T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Playwright](), [Command-line interface](), [Traces](), [Deployment](), [dashboards]() Tags: [cli](), [config](), [deployment](), [flaky](), [traces](), [vercel]() ### AI overview Endform is now available through the Vercel Marketplace and Vercel CLI. It runs Playwright tests in parallel on isolated machines, integrates with existing tests without configuration changes, links deployment checks to traces in its dashboard, and identifies flaky tests from historical results. ### Source excerpt Endform is now available on the Vercel Marketplace. It runs your Playwright tests in parallel, so your suite finishes in the time of your slowest test. Key capabilities: Run every test on its own isolated machine Bring your existing Playwright tests with no config changes Get a pass/fail check on every production and preview deployment, linked to full traces in the Endform dashboard Spot flaky tests by tracking results across historical runs Pay only for the test runtime you use Install Endform on the Vercel Marketplace or with the Vercel CLI: vc i endform. Read more ## Monitor watchOS and visionOS apps with Datadog RUM DevFeed: [Monitor watchOS and visionOS apps with Datadog RUM]() Original publisher: [Read original article]() Author: Jessica Manheimer; Maël Lilensten Published: 2026-07-06T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [real user monitoring](), [visionOS](), [watchOS](), [error tracking](), [Traces](), [SDKs](), [observability](), [User Experience](), [SwiftUI](), [iOS]() Tags: [apple](), [apple-watch](), [digital-experience-monitoring](), [error-tracking](), [net-11-preview-7](), [observability](), [real-user-monitoring](), [traces](), [user-experience](), [vision]() ### AI overview Datadog RUM extends crash reporting, error tracking, deobfuscated stack traces, and session-level observability to watchOS and visionOS apps. The article explains how teams can investigate failures and understand user interactions without a separate SDK. ### Source excerpt Monitor crashes, errors, and user sessions on watchOS and visionOS by using Datadog RUM with fully deobfuscated stack traces and no separate SDK required. [Next page]()