# observability ai agents A software observability concept concerning telemetry for generative AI agent operations. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## Knowledge Graph as context for LLMs: demonstrating decisive RCA and faster production performance DevFeed: [Knowledge Graph as context for LLMs: demonstrating decisive RCA and faster production performance]() Original publisher: [Read original article]() Author: Sarah Constant Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Large Language Model](), [Grafana Cloud](), [debugging](), [incident](), [telemetry](), [observability ai agents]() Tags: [ai](), [ai-agents](), [analytics](), [debugging](), [grafana](), [grafana-cloud](), [incident](), [knowledge-graph](), [llms](), [logs](), [metrics](), [monitoring](), [observability](), [telemetry](), [traces]() ### AI overview The article reports Grafana Labs experiments testing whether providing AI agents with Grafana Cloud's Knowledge Graph improves incident debugging compared with raw telemetry alone. In one incident replayed 16 times per approach, Knowledge Graph context led to the correct root cause 15 times, versus once with raw telemetry. The article also describes challenges including misleading signals, unsupported confident answers, and inconsistent investigations, arguing that well-structured context matters more than context-window size alone. ### Source excerpt On the product team here at Grafana Labs, we consider AI agents our users, too. That's why we set out to test how well agents can debug incidents across the full stack, and how much better they perform with Grafana Cloud's Knowledge Graph vs. using raw telemetry alone. Our early results are promising. In one real incident we replayed 16 times each way, an agent with Knowledge Graph context found the correct root cause 15 times, compared with just once using raw telemetry alone. Along the way, we also uncovered some of the challenges that still stand in the way of reliable AI-assisted debugging, from chasing the wrong signals to confidently making things up and producing inconsistent answers. We're still early, but our findings point to an important idea. The industry's shorthand right now is that a bigger context window will lead to better outputs. Our findings suggest it's not just about more context; it's about structuring your data well enough to serve the right context. Here's a look at what we've learned so far, as we continue to experiment out in the open and bring you along, the Grafana Labs way. Giving an agent access to telemetry is just the beginning Give a current-generation model like Opus 4.8 access to your raw telemetry during a live incident, and it genuinely starts to figure things out: querying metrics and logs, forming a hypothesis, and checking it. We have watched it work on our own incidents, and it does it affordably. But if you run software at scale, where uptime is business-critical and large teams share the responsibility, a better model alone doesn't get you all the way there for debugging. From analyzing how LLMs do root-cause analysis on our own infrastructure, and from speaking to our customers, we've uncovered three problems that get in the way: The further the cause is from the alert, the more likely the model is to get it wrong. An agent may confidently make things up when it doesn't have the evidence it needs. The same investigation c ## Agent Night demo recap: Brian Douglas's self-healing Pokémon agent DevFeed: [Agent Night demo recap: Brian Douglas's self-healing Pokémon agent]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-17T18:34:12Z Content type: news Language: en Sources: [WorkOS Blog]() Topics: [Demo](), [observability ai agents](), [telemetry](), [Claude Code](), [Python](), [DuckDB](), [Kafka](), [Code](), [Open Source]() Tags: [agent](), [claude-code](), [code](), [coding](), [demo](), [duckdb](), [kafka](), [open-source](), [python](), [self-healing](), [telemetry](), [traces]() ### AI overview A recap of Brian Douglas's Agent Night demo shows how Paper Compute ran Pokémon as a self-healing agent for six months. The setup uses Claude Code, a headless Python emulator, and traces from each session and turn. The article explains how labeled traces and per-session reflections can make the agent's history searchable and useful for improvement. ### Source excerpt Brian Douglas of Paper Compute closed Agent Night with six months of running Pokémon as a self-healing agent: traces, anomalies, dreams, and inceptions. ## Eric Schwartz on what it takes to run an AI SRE at petabyte scale DevFeed: [Eric Schwartz on what it takes to run an AI SRE at petabyte scale]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-07T00:00:00Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [site-reliability-engineering](), [data-platforms](), [telemetry](), [observability ai agents]() Tags: [ai](), [data](), [data-platforms](), [on-call](), [production](), [root-cause-analysis](), [sre](), [telemetry]() ### AI overview An interview with Traversal product manager Eric Schwartz examines how the company operates an AI site reliability engineer for large enterprises. The article explains that petabyte-scale telemetry requires continuously analyzing, compressing, and indexing data ahead of runtime, with an SRE-focused tool and prompt harness. Traversal reports that deployments are generally running in production within a week with minimal tuning, supported by forward-deployed engineering for last-mile optimization. ### Source excerpt Traversal PM Eric Schwartz on data platforms, routing models by severity, and the permission ladder toward self-driving production, from AI Engineer 2026. ## How Trigger.dev is using ClickHouse to scale observability for long-running AI workflows DevFeed: [How Trigger.dev is using ClickHouse to scale observability for long-running AI workflows]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-07-09T13:07:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [telemetry](), [Serverless](), [observability ai agents](), [data-processing](), [Databases](), [Open Source]() Tags: [ai](), [ai-agent](), [clickhouse](), [logs](), [observability](), [open-source](), [serverless](), [telemetry](), [traces](), [video]() ### AI overview Trigger.dev migrated observability workloads for its serverless asynchronous workflow platform from Postgres to ClickHouse. The move addressed scaling bottlenecks while enabling fast analysis of logs, traces, and execution telemetry, with P95 queries around 200 ms and substantially lower telemetry storage requirements. ### Source excerpt Trigger.dev migrated from Postgres to ClickHouse to scale observability for its serverless AI workflow platform, delivering ~200 ms P95 query performance and a dramatically smaller telemetry storage footprint. ## Announcing the Managed ClickStack MCP Server DevFeed: [Announcing the Managed ClickStack MCP Server]() Original publisher: [Read original article]() Author: The ClickStack Team Published: 2026-06-24T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [MCP Server](), [observability](), [log management](), [observability ai agents](), [Cloud](), [configuration]() Tags: [ai-agents](), [architecture](), [clickhouse](), [cloud](), [guide](), [mcp](), [mcp-server](), [observability](), [telemetry]() ### AI overview ClickHouse is bringing the ClickStack MCP Server to ClickHouse Cloud, giving AI agents specialized tools for investigating logs, metrics, and traces. Managed ClickStack adds full-fidelity telemetry, longer historical context, dedicated compute, and existing ClickStack investigation features. ### Source excerpt Announcing the Managed ClickStack MCP Server: purpose-built observability tools, full-fidelity telemetry, and dedicated compute for AI-driven investigations at scale. ## Towards a science of scaling agent systems: When and why agent systems work DevFeed: [Towards a science of scaling agent systems: When and why agent systems work]() Original publisher: [Read original article]() Published: 2026-01-28T11:00:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [AI Agent](), [observability ai agents](), [systems](), [Generative AI](), [Google](), [AI Chat]() Tags: [agent](), [agentic](), [agents](), [ai-agents](), [architecture](), [benchmarks](), [evaluation](), [generative-ai](), [google](), [machine-intelligence](), [research](), [systems]() ### AI overview Google Research reports a controlled evaluation of 180 agent configurations examining how multi-agent systems scale. Multi-agent coordination improves performance on parallelizable tasks but can reduce performance on sequential tasks, and a predictive model identifies the optimal architecture for 87% of unseen tasks. ### Source excerpt Generative AI ## Your AI SRE needs better observability, not bigger models. DevFeed: [Your AI SRE needs better observability, not bigger models.]() Original publisher: [Read original article]() Author: Manveer Chawla Published: 2026-01-01T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [Artificial Intelligence](), [observability](), [SRE](), [observability ai agents](), [telemetry](), [clickhouse](), [incident](), [SQL]() Tags: [ai](), [dashboards](), [data](), [incident](), [logs](), [metrics](), [models](), [observability](), [site-reliability](), [sql](), [sre](), [telemetry]() ### AI overview The article argues that effective AI SRE copilots depend on rich, long-retention, high-cardinality observability data and fast queries rather than larger language models. It presents an architecture combining an LLM, SQL, an observability and context layer, and a scalable OLAP database such as ClickHouse to support incident investigation and root-cause analysis. ### Source excerpt Most AI SRE tools fail because the observability stack underneath can't keep up. Learn how to build an SRE observability architecture with the retention, cardinality, and query speed that AI copilots actually need. ## This Week at Zed Industries: #6 DevFeed: [This Week at Zed Industries: #6]() Original publisher: [Read original article]() Author: Antonio Scandurra Published: 2023-06-09T00:00:00Z Content type: article Language: en Sources: [Zed Industries - Blog]() Topics: [Development](), [Elixir](), [Tree-sitter](), [telemetry](), [Syntax Highlighting](), [observability ai agents](), [OpenAI](), [toolchain](), [configuration]() Tags: [ai-assistants](), [configuration](), [development](), [elixir](), [openai](), [syntax](), [telemetry](), [toolchain]() ### AI overview This weekly Zed development update covers Zed 0.89, including language server protocol support and improvements to the Assistant Panel, panic telemetry, feedback analysis, build configuration, Elixir support, syntax highlighting, and Tree-sitter query reloading. ### Source excerpt We're working on language servers, AI assistants, and plenty more!