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AI ObservabilityPhoenix at 10,000 stars on GitHub: How an open source AI observability project grew by following its community
Phoenix crossed 10,000 GitHub stars. Here is how the open-source AI observability project grew from a Jupyter notebook extension into a community-shaped platform for traces, evals, OpenInference, and… RL Nabors Nancy Chauhan June 7, 2026 10 min read -
AI ObservabilityBuilding the AI factory for self-improving agents: What’s new in Arize AX
Arize AX is adding managed agents, full-agent experimentation, expanded multimodal support, and Harness-as-a-Judge to help teams observe, evaluate, and improve production agents. Jason Lopatecki Aparna Dhinakaran June 4, 2026 8 min read -
AI ObservabilityMicrosoft’s open trust stack runs on OpenInference
Microsoft's open trust stack for AI agents puts ASSERT and Agent Control Specification on top of OpenInference, connecting evaluation, runtime controls, and observability through a shared trace contract. Jim Bennett June 3, 2026 6 min read -
AI ObservabilityAI benchmarks are breaking. Trace analysis is what comes next.
Models got smart enough to cheat their benchmarks, and outcome-only scores stopped measuring what we thought they measured. The fix, full trace analysis, is the same methodology production… Laurie Voss June 2, 2026 8 min read -
AI ObservabilityThe best eval harness for production AI and agents: A comparison
A practical comparison of production AI evaluation harnesses, including what to look for across instrumentation, evaluators, online evals, CI gates, and agent workflows. Laurie Voss June 1, 2026 10 min read -
AI ObservabilityHow to build a better agent harness with traces and evals
Agents are easy to prototype and hard to improve. A repeatable loop of traces, evals, failed-span inspection, and targeted harness changes makes agent behavior easier to debug and… Aaron Winston May 29, 2026 14 min read -
AI ObservabilityFrom production traces to better AI agents: Automating the LLMOps feedback loop
Production AI traces are the raw material for better evals, prompts, datasets, and fine-tuned models. This post shows how the Arize AX Airflow Provider turns that feedback loop… Jitendra Yadav Hakan Tekgul May 27, 2026 17 min read -
AI ObservabilityHow to build LLM-as-a-Judge evaluators that hold up in production
Learn how to design, calibrate, and run LLM-as-a-judge evaluators with fixed labels, human agreement checks, trace context, and Phoenix Evals. Aaron Winston May 21, 2026 22 min read -
AI ObservabilityCoding agent tracing and evaluation: An open source tool to improve AI coding workflows
Announcing coding harness tracing for observing, evaluating, and improving coding agent workflows across Claude Code, Cursor, Codex, GitHub Copilot, and Gemini CLI. Duncan McKinnon Chris Cooning Fuad Ali May 18, 2026 5 min read -
AI ObservabilityHow we use Alyx to build Alyx: How to build an AI agent feedback loop
How Arize uses Alyx to debug Alyx: searching dense traces, aggregating failures, triaging dogfooding issues, and closing the AI engineering feedback loop. Chris Cooning Sally-Ann DeLucia Priyan Jindal Jack Zhou May 13, 2026 10 min read -
AI ObservabilityFrom observability to context: What’s next for Arize Phoenix
As agents start changing software, they need a way to verify their work that includes traces, evals, feedback, and APIs. This is where Phoenix goes next — not… Mikyo King Elizabeth Hutton May 11, 2026 10 min read -
AI ObservabilityAI agent evaluation: How to test, debug, and improve agents in production
Lessons from building and shipping Alyx, our AI agent Sally-Ann DeLucia Chris Cooning Priyan Jindal Jack Zhou May 5, 2026 9 min read -
AI ObservabilityWhy agent telemetry needs standards
Enterprise agents are moving from demos into production workflows, which creates a basic problem: teams need to understand what those agents actually did. Richard Young May 1, 2026 6 min read -
AI ObservabilityUsing context graphs: build a data moat like Google’s using your enterprise data
Enterprise software is on the verge of its first compounding data loop, the same kind of self-reinforcing mechanism that built the most valuable consumer businesses of the last… Jim Bennett Laurie Voss April 29, 2026 8 min read -
AI ObservabilityData Fabric: Querying agent traces in BigQuery
How to join LLM traces with billing, infrastructure, and customer data using Iceberg and BigQuery If you run AI agents in production, you’ve probably run into a simple… Richard Young April 15, 2026 14 min read
Don’t ship vibes.
Arize gives AI teams observability and evals to understand and improve agent performance.
