# agent observability Observability practices for AI agent applications and frameworks, using telemetry to monitor, troubleshoot, evaluate, and improve agent behavior. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building an Internal Developer Platform with Artificial Intelligence DevFeed: [Building an Internal Developer Platform with Artificial Intelligence]() Original publisher: [Read original article]() Author: Ben Linders Published: 2026-09-17T11:11:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [internal developer platform](), [AI Development](), [AI search](), [agent observability](), [OpenTelemetry](), [telemetry]() Tags: [agent](), [agents](), [artificial-intelligence](), [culture-methods](), [developer-experience](), [developer-platform](), [distributed-tracing](), [guardrails](), [internal-developer-platform](), [logging](), [logs](), [metrics](), [news](), [opentelemetry](), [platform-artificial-intelligence](), [platform-engineering](), [prometheus](), [traces]() ### AI overview This news article covers a KubeCon presentation about using AI agents as an internal developer platform. It discusses semantic search across sources such as Git, Slack, Jira, repositories, pull requests, and wiki pages; guardrails for controlling actions; and logs, metrics, and traces for understanding agent behavior. The speakers also describe OpenTelemetry conventions for GenAI and related observability tools. ### Source excerpt Agents are becoming the new developer platform, using semantic search with data from tools like Git, Slack, and Jira for context. Things to consider are setting guardrails to block or allow things, and using logs, metrics, and traces to understand agent behavior. By Ben Linders ## 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. ## The Future of Data Engineering in the Age of AI | Erfan Hesami DevFeed: [The Future of Data Engineering in the Age of AI | Erfan Hesami]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-09-16T13:21:19Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [data-engineering](), [AI Engineering](), [agent observability](), [coding](), [Security]() Tags: [agents](), [ai](), [ai-agents](), [ai-engineering](), [coding](), [data-engineering](), [fundamentals](), [governance](), [security]() ### AI overview An interview with Erfan Hesami examines how AI and agents may change data engineering, including the evolving role of data engineers, the overlap with AI engineering, the continuing importance of fundamentals, and the need to manage governance, security, costs, technical debt, and human judgment. ### Source excerpt AI Agents, Coding & Fundamentals ## Shared Selective Persistent Memory for Agentic LLM Systems DevFeed: [Shared Selective Persistent Memory for Agentic LLM Systems]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [agent observability](), [Framework](), [Code](), [configuration](), [Access Control](), [Git](), [Model Context Protocol (MCP)](), [CSV](), [SQL](), [Grafana]() Tags: [access-control](), [agentic](), [agents](), [code](), [configuration](), [csv](), [dashboards](), [git](), [llm](), [mcp](), [memory](), [platform](), [replication](), [sql]() ### AI overview This research introduces shared selective persistent memory for agentic LLM systems. The architecture retains reusable task specifications, data schemas, tool configurations, and output constraints while discarding session-specific reasoning traces. Shared workspaces support role-based collaborative reuse, and experiments report higher task completion than no memory or full-history persistence, along with zero-token data refresh and lower token costs. ### Source excerpt Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive. Naively persisting entire conversation histories is both token-inefficient and counterproductive--irrelevant context degrades generation quality. We introduce shared selective persistent memory, a memory architecture for agentic systems that identifies and retains four categories of reusable context--task specifications, data... ## Grab's LLM-Kit Framework Standardizes More Than 500 Internal Agent Services DevFeed: [Grab's LLM-Kit Framework Standardizes More Than 500 Internal Agent Services]() Original publisher: [Read original article]() Author: Hien Luu Published: 2026-09-15T09:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Framework](), [AI Agent](), [Deployment](), [Model Context Protocol](), [agent observability](), [FastAPI](), [OpenTelemetry](), [GitLab]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-ml-data-engineering](), [deployment](), [fastapi](), [framework](), [gitlab](), [grab-agent-platform](), [ml-data-engineering](), [model-context-protocol](), [news](), [opentelemetry](), [vault]() ### AI overview Grab's internal LLM-Kit framework standardizes more than 500 agent services by providing shared scaffolding for evaluation, tracing, secret handling, service discovery, and tool-server connections. The article reports that deploying a new agent service now takes about one hour instead of two weeks or more. ### Source excerpt Grab has implemented LLM-Kit, a framework that standardizes over 500 internal agent services. This system enhances service integration, evaluation, and secret handling, reducing the time to deploy new AI agents from two weeks to one hour. It centralizes infrastructure management, allowing runtime tool discovery and flexible model integration, while maintaining operational control. By Hien Luu ## Inside the LLM Call: GenAI Observability with OpenTelemetry DevFeed: [Inside the LLM Call: GenAI Observability with OpenTelemetry]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-09-14T16:56:42Z Content type: tutorial Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [agent observability](), [telemetry](), [AI Agent](), [Large Language Model]() Tags: [agent](), [ai-agent](), [claude-code](), [codex](), [observability](), [opentelemetry](), [token](), [tool](), [visibility]() ### AI overview This tutorial explains how OpenTelemetry Semantic Conventions for Generative AI record LLM calls, tool invocations, token counts, and related events. It demonstrates exporting telemetry from an LLM-powered application, viewing it with Aspire Dashboard, and considering sensitive-data implications of optional content capture. ### Source excerpt Your AI agent just took 45 seconds to answer a simple question. Was it the model? A slow tool call? A retry loop? Every time an application calls an LLM, a chain of model calls, tool invocations, and token exchanges happens behind the scenes -- and without observability, you are guessing. The OpenTelemetry Semantic Conventions for Generative AI give you that visibility. They standardize how GenAI operations are recorded -- the model being called, input and output token counts, and when opted in, the full content of prompts, completions, tool calls, and tool results. ## Datadog named the Company to Beat for observability platforms in 2026 Gartner® AI Vendor Race report DevFeed: [Datadog named the Company to Beat for observability platforms in 2026 Gartner® AI Vendor Race report]() Original publisher: [Read original article]() Author: Yanbing Li Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [observability](), [agent observability](), [observability ai agents](), [incident](), [MCP Server](), [observability pipelines](), [OpenTelemetry](), [Security](), [Artificial Intelligence]() Tags: [agent-observability](), [ai-agents](), [datadog](), [incident-response](), [mcp-server](), [observability](), [observability-pipelines](), [opentelemetry]() ### AI overview Datadog says it was named the Company to Beat for observability platforms in Gartner's August 2026 AI Vendor Race research and a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms. The article presents Datadog's unified observability and security platform, including autonomous incident investigation, AI agent and LLM application observability, an MCP Server for querying telemetry, and Observability Pipelines with OpenTelemetry support. ### Source excerpt Datadog has been recognized as the Company to Beat for observability platforms in the August 2026 Gartner® AI Vendor Race research. ## It passed CI. It passed your evals. The customer still got the wrong answer. DevFeed: [It passed CI. It passed your evals. The customer still got the wrong answer.]() Original publisher: [Read original article]() Author: Sean O'Dell Published: 2026-09-13T14:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [AI Agent](), [agent observability](), [observability](), [tracing](), [dynatrace](), [ci](), [coding]() Tags: [ai](), [ai-agent](), [ai-agents](), [ai-engineering](), [ci](), [coding](), [dynatrace](), [observability](), [post-contributed](), [sponsor-dynatrace](), [sponsored](), [sponsored-post-contributed](), [traces]() ### AI overview The article explains how AI-agent failures can pass CI and evaluations while still producing slow or incorrect customer-facing results. It presents distributed traces and agent trajectories--model calls, tool calls, arguments, and results--as evidence for debugging retrieval behavior, release context, and feature-flag state. ### Source excerpt A diff is not evidence. It's a statement of intent. The tests passed. The review's done. The change is live. The post It passed CI. It passed your evals. The customer still got the wrong answer. appeared first on The New Stack. ## The Grafana AI SDK for Go: a shared foundation for building AI applications DevFeed: [The Grafana AI SDK for Go: a shared foundation for building AI applications]() Original publisher: [Read original article]() Author: Luccas Quadros Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [vercel ai sdk](), [SDKs](), [Go Language](), [AI Development](), [Large Language Model](), [agent observability](), [Model Context Protocol](), [React]() Tags: [agent-observability](), [ai](), [analytics](), [backend](), [building](), [go](), [grafana](), [logs](), [metrics](), [monitoring](), [observability](), [sdk](), [streaming](), [tools](), [traces]() ### AI overview Grafana Labs introduces the Grafana AI SDK for Go, an open-source shared foundation for building AI applications. The SDK standardizes model calls, streaming, tool execution, structured output, multi-step agents, workflow controls, and operational features such as retries, logging, metrics, and Agent Observability. It also supports streaming Go backends to Vercel AI SDK frontend hooks. ### Source excerpt Starting an experiment with an LLM has never been easier. Keeping a growing collection of those experiments consistent is another matter. Earlier this year, as more teams began exploring AI features here at Grafana Labs, we repeatedly encountered the same pattern: a new experiment would start, move quickly, and build its own client for whichever model provider it needed. The next experiment would do the same, with a slightly different abstraction for streaming, tools, errors, or provider configuration. This was understandable, given the circumstances. Model providers were changing quickly, our teams were learning quickly, and coding agents made it possible to turn an idea into a working integration faster than ever. But that speed also made it easier for every integration to develop its own architecture. Eventually, we were maintaining a collection of solutions to what was essentially the same problem. And since most of our backend is written in Go, we built the Grafana AI SDK for Go to give our teams a shared foundation to work from. It provides common interfaces for calling models, streaming responses, executing tools, producing structured output, and running multi-step agents. It also speaks the protocol used by Vercel AI SDK frontend hooks, so a Go backend can stream directly to useChat, useCompletion, and useObject. We built it because we needed it inside Grafana Labs, but we open sourced it last month (alongside a broader collection of tools we released for building, operating, and understanding AI systems during our first Grafana Labs AI Week) because we think other teams building AI applications in Go are likely to encounter many of the same problems. We would like to build the next part together, so in this blog I'll tell you a bit more about the project, including how you can put it to use today, as well as how you can help us improve it. What teams can build with it today The SDK supports both simple model calls and larger application workflows: Generate ## Debugging our AI search assistant with agent tracing DevFeed: [Debugging our AI search assistant with agent tracing]() Original publisher: [Read original article]() Author: Dominik Buszowiecki; Shaun Kaasten Published: 2026-09-11T09:00:00Z Content type: article Language: en Sources: [Sentry Blog]() Topics: [AI search](), [agent observability](), [debugging](), [tracing](), [Large Language Model]() Tags: [agent](), [ai](), [api](), [debugging](), [errors](), [eval](), [llm](), [search](), [sentry](), [tracing]() ### AI overview Sentry engineers describe how they debugged the Search Query Assistant, which converts natural-language prompts into Sentry Syntax queries. They used evals for performance measurement and AI Conversation tracing to investigate failures, including a bug involving custom numerical attributes that caused queries to return no results. ### Source excerpt See how Sentry engineers used AI Conversations to debug a natural language search assistant and fix a tricky query generation bug. ## The agentic harness for Tenable Hexa AI: How Tenable prevents AI agents from going off the rails DevFeed: [The agentic harness for Tenable Hexa AI: How Tenable prevents AI agents from going off the rails]() Original publisher: [Read original article]() Author: Raj Agrawal Published: 2026-09-10T13:00:00Z Content type: article Language: en Sources: [Tenable Blog]() Topics: [Large Language Model](), [agent observability](), [Architecture & Design]() Tags: [agentic-ai](), [ai-agents](), [audit-trail](), [llms](), [security]() ### AI overview Tenable describes an agentic-AI harness that constrains model context and tool use, validates actions, requires human approval, and records activity to protect production security environments. ### Source excerpt Learn why Tenable treats agentic LLMs as untrusted insiders, and how we've made sure you can control and monitor the AI agents making changes in your production security environment Key takeaways AI models can quickly understand data, but not your business. While modern AI models are great at reasoning, they don't automatically understand your unique environment or who is allowed to do what. The "harness" is the custom-built layer that translates AI intelligence into safe, controlled actions specific to your organization. AI requires a supervisor. Tenable treats our AI agents like untrusted insiders. Instead of relying on the AI to police itself, the harness strictly limits what the AI can see and do, and ensures a human reviews and approves any changes before they happen in your environment. Trust requires proof. The harness ensures that every action AI proposes or takes is fully recorded, giving you an audit trail to confidently hand off real work to AI without losing control. Every security vendor has an AI agent. The demos are good. They are supposed to be good, because a demo runs against data that nobody minds breaking. The questions worth asking a vendor about their AI agents are the ones that come after the demo: What happens when the agent is wrong? What happens when someone feeds the agent a prompt designed to manipulate it? If the agent changes something in our environment, what evidence exists afterward about what it did and who authorized its action? When developing Tenable Hexa AI, the agentic AI engine of the Tenable One Exposure Management Platform, we tackled a difficult and critical problem that often gets overlooked: building the underlying infrastructure, the governance layer that safely turns the AI's decisions into actual changes without putting your production data at risk. We call this layer the harness: the runtime control environment in which the model operates. The harness decides: What context the model can see Which tools it can call Wha ## Your Agent Harness Needs Runtime Security DevFeed: [Your Agent Harness Needs Runtime Security]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-09T20:59:58Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [agent observability](), [Agent Harness](), [Security](), [Open Source](), [telemetry]() Tags: [agent](), [agent-harness](), [ai-agents](), [anthropic](), [claude](), [cybersecurity](), [github](), [guide](), [local](), [monitoring](), [open-source](), [security](), [telemetry]() ### AI overview This guide presents Agent Beacon, an open-source telemetry layer for AI agents. It records tool calls, shell commands, file changes, and approval decisions as structured runtime events across supported agent harnesses, providing a live record of agent behavior for security monitoring and investigation. ### Source excerpt A 100% local, open-source guide to recording what AI agents actually do at runtime. ## Data Engineering Weekly #286 DevFeed: [Data Engineering Weekly #286]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-09-07T00:18:06Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [agent observability](), [Large Language Model](), [data-modeling](), [Monitoring](), [Scalability](), [semantic-layer](), [parquet]() Tags: [ai](), [ai-agents](), [data-engineering](), [data-modeling](), [llm](), [monitoring](), [newsletter](), [observability](), [parquet](), [scalability](), [semantic-layer](), [weekly]() ### AI overview Data Engineering Weekly #286 is a curated newsletter covering data platform fundamentals, mathematics for machine learning, agentic machine learning at Instacart, Netflix's lifecycle for LLM-as-a-Judge systems, semantic layers and data modeling for AI analytics, and Apache Pinot scalability. ### Source excerpt The Weekly Data Engineering Newsletter ## Observing AI Agent Decisions with OpenTelemetry DevFeed: [Observing AI Agent Decisions with OpenTelemetry]() Original publisher: [Read original article]() Author: Raul Junco Published: 2026-08-22T12:06:38Z Content type: article Language: en Sources: [System Design Classroom]() Topics: [agent observability](), [OpenTelemetry](), [Artificial Intelligence](), [telemetry](), [Back end]() Tags: [agent](), [ai](), [ai-agent](), [opentelemetry](), [telemetry](), [traces]() ### AI overview A practical guide to observing AI agent decisions with OpenTelemetry. It explains why conventional logs, metrics, and distributed traces may not show whether agents chose the correct workflow, and argues that decision recording and observation should be in place before production. ### Source excerpt A practical guide to observing AI decisions with OpenTelemetry ## 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 ## Automated agent triage with Agent Tracing and Claude Routines DevFeed: [Automated agent triage with Agent Tracing and Claude Routines]() Original publisher: [Read original article]() Author: Trevor Elkins Published: 2026-08-13T09:00:00Z Content type: article Language: en Sources: [Sentry Blog]() Topics: [Claude](), [AI Agent](), [tracing](), [Model Context Protocol](), [agent observability]() Tags: [agent](), [ai-agent](), [claude](), [mcp](), [model-context-protocol-server](), [report](), [tool](), [tracing]() ### AI overview Sentry describes a Claude Routine that uses the Sentry MCP to query Agent Tracing data from roughly 800 overnight AI agent conversations. It aggregates error statistics, samples traces and conversations, reviews the Seer codebase, and files new Linear tickets for findings that are not already tracked. ### Source excerpt How Sentry uses a Claude Routine and the Sentry MCP to automatically triage 800 AI agent conversations overnight and file bugs. ## Building open-source observability together: The OpenSearch Observability TAG turns one DevFeed: [Building open-source observability together: The OpenSearch Observability TAG turns one]() Original publisher: [Read original article]() Author: Dotan Horovits Published: 2026-08-11T17:01:57Z Content type: article Language: en Sources: [OpenSearch]() Topics: [observability](), [Open Source](), [OpenTelemetry](), [Prometheus](), [agent observability]() Tags: [agent-observability](), [blog](), [cncf](), [community](), [metrics](), [observability](), [open-source](), [opensearch](), [opentelemetry](), [prometheus]() ### AI overview The OpenSearch Observability Technical Advisory Group marks its first anniversary after a year of community-driven guidance, RFC reviews, architecture decisions, and collaboration across organizations. The article highlights work involving OpenTelemetry, Prometheus, Perses, KEDA, Kubernetes, and the OpenSearch Observability Stack, and explains how contributors can participate in the group's second year. ### Source excerpt The OpenSearch Observability TAG's first year: 10+ orgs shipped OTLP ingestion, Prometheus metrics, an open-source Observability Stack, and AI agent observability. The post Building open-source observability together: The OpenSearch Observability TAG turns one appeared first on OpenSearch. ## Human in the loop: Do you need it at every step? DevFeed: [Human in the loop: Do you need it at every step?]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-08-10T11:35:04Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI-assisted coding](), [Pull Request](), [pull-requests](), [agent observability](), [sdlc](), [coding]() Tags: [agentic](), [agents](), [ai-coding-agents](), [coding-agents](), [production](), [pull-request](), [review](), [sdlc]() ### AI overview This article explains how human oversight should adapt as AI coding agents move from suggesting code to taking actions in production. It compares rule-based gates, which apply predefined conditions, with risk-based gates, which assess each action dynamically and involve a human when risk exceeds a threshold. ### Source excerpt Explore rule-based vs risk-based guardrails for AI coding agents and when human review is truly needed in the loop. ## The real cost of your observability stack DevFeed: [The real cost of your observability stack]() Original publisher: [Read original article]() Author: Shenoy Pratik Gurudatt Published: 2026-08-07T15:00:00Z Content type: article Language: en Sources: [OpenSearch]() Topics: [observability](), [telemetry](), [agent observability](), [Artificial Intelligence](), [Open Source]() Tags: [agent](), [agents](), [ai](), [ai-agents](), [blog](), [dashboards](), [infrastructure](), [latency](), [logging](), [logs](), [metrics](), [microservices](), [observability](), [open](), [open-source](), [platform](), [platform-engineering](), [technical](), [telemetry](), [traces](), [vendor-lock-in]() ### AI overview The article examines the operational and financial costs of fragmented observability for agentic workloads. It explains that humans and AI agents must move among separate metrics, logging, and tracing tools, which increases investigation time, token usage, latency, integration work, and the risk of losing correlation context. It presents an open-source OpenSearch Observability Stack as an alternative intended to unify logs, traces, and agent telemetry while avoiding per-GB penalties and vendor lock-in. ### Source excerpt AI agents generate 10x the telemetry, and proprietary per-GB pricing punishes you for it. See how the open-source OpenSearch Observability Stack unifies logs, traces, and agent telemetry without vendor lock-in. The post The real cost of your observability stack appeared first on OpenSearch. ## Elastic 9.5: Columnar, VectorDB index mode & auto-calibration, and AI-driven alert triage DevFeed: [Elastic 9.5: Columnar, VectorDB index mode & auto-calibration, and AI-driven alert triage]() Original publisher: [Read original article]() Author: Sarah Leslie Published: 2026-08-04T00:00:00Z Content type: release Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [elasticsearch](), [agent observability](), [Monitoring](), [Artificial Intelligence](), [Database]() Tags: [agent-observability](), [ai-agents](), [data](), [elasticsearch](), [monitoring](), [observability](), [observability-platform-search-security](), [performance](), [release]() ### AI overview Elastic 9.5 is generally available with Columnar Mode, VectorDB index mode and auto-calibration, native Prometheus and PromQL support, AI-driven alert triage, and enhancements to Elastic Agent Builder, including agent observability, monitoring, and human-in-the-loop approvals. ### Source excerpt Today, we are pleased to announce the GA of Elastic 9.5 as the latest version of the Elasticsearch Platform. This release includes a range of new features, including Columnar Mode, VectorDB index mode, and Agent Builder enhancements. Learn more. ## The AI Gateway Buyer's Guide: Beyond Routing and Tool Visibility DevFeed: [The AI Gateway Buyer's Guide: Beyond Routing and Tool Visibility]() Original publisher: [Read original article]() Author: Ritesh Patel Published: 2026-08-02T16:58:40Z Content type: opinion Language: en Sources: [Nirmata]() Topics: [Artificial Intelligence](), [Model Routing](), [ai security](), [agent observability](), [Model Context Protocol]() Tags: [ai](), [ai-gateway](), [ai-security](), [claude](), [cost-optimization](), [engineering](), [kubernetes](), [latency](), [mcp](), [routing]() ### AI overview This opinion article argues that AI gateways should not be treated as governance systems merely because they provide model routing and tool-call visibility. Routing can optimize cost and latency, while monitoring can show which tools or MCP servers were used, but governance requires deciding whether an agent action is permitted for a specific agent, with specific arguments, at a specific time. ### Source excerpt Over the past year, nearly every engineering org I talk to has reached the same milestone: AI agents are no longer a demo. They're calling real tools, against real systems, with real consequences. And nearly every one of those orgs has reached for the same... The post The AI Gateway Buyer's Guide: Beyond Routing and Tool Visibility first appeared on Nirmata. ## ClickHouse joins the Open Secure AI Alliance DevFeed: [ClickHouse joins the Open Secure AI Alliance]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-07-30T19:44:40Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [ai security](), [AI Agent](), [Securing AI](), [agent observability](), [clickhouse](), [Open Source](), [tracing](), [observability](), [Instrumentation](), [hugging face](), [Nvidia]() Tags: [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-security](), [clickhouse](), [hugging-face](), [instrumentation](), [nvidia](), [observability](), [open-source](), [security](), [tracing]() ### AI overview ClickHouse joins the Open Secure AI Alliance with NVIDIA and other industry leaders to develop open tools for securing AI agents. The article highlights Langfuse for agent tracing, evaluations, guardrail monitoring, and open audit trails, including air-gapped deployments that store traces in ClickHouse. ### Source excerpt ClickHouse is joining the Open Secure AI Alliance alongside NVIDIA and other industry leaders to help build open tools that keep AI agents secure. ## OpenTelemetry @ KubeCon + CloudNativeCon Japan 2026 DevFeed: [OpenTelemetry @ KubeCon + CloudNativeCon Japan 2026]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-07-23T17:40:46Z Content type: article Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [agent observability]() Tags: [2026](), [conference](), [japan](), [kubecon](), [opentelemetry]() ### AI overview A guide to OpenTelemetry-related sessions at KubeCon + CloudNativeCon Japan 2026, including Japan Community Day and the main conference program in Yokohama. The schedule includes sessions on agent observability, industrial IoT, metrics migration, traces as test data, trust, Wasm, and Prometheus exporters. ### Source excerpt KubeCon + CloudNativeCon Japan takes place July 28-30, 2026, in Yokohama, Japan. Register today to join us! Here are all the OpenTelemetry-related sessions, up to date at the time of writing. Session details can change, so confirm dates and times in the official schedule. Community Day Japan Community Day is on Tuesday, July 28, ahead of the main conference. It's a great way to engage with fellow attendees and with members of local meetup communities, special interest groups, and subgroups within Cloud Native Community Japan. ## Harness AgentTrace: An Observability and Guardrail Framework DevFeed: [Harness AgentTrace: An Observability and Guardrail Framework]() Original publisher: [Read original article]() Author: Sunil Gattupalle Sanjay Nagaraj Published: 2026-07-21T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [agent observability](), [ai observability](), [Framework](), [Testing](), [tracing]() Tags: [agents](), [ai](), [ai-observability](), [apache](), [ci](), [evaluation](), [observability](), [testing](), [tracing]() ### AI overview Harness describes AgentTrace, an internal framework for observing, evaluating, and governing AI agents in production. It connects production monitoring with evaluation, allows failures to become regression test cases, and includes open-source harness-sdk and harness-evals layers under Apache 2.0 that work with any OpenTelemetry backend. ### Source excerpt Harness AgentTrace unifies AI observability, evaluation, and guardrails to detect failures, improve quality, and secure AI agents in production. | Blog [Next page]()