agent observability

agent observability, Agent tracing, agent workflows, agent-memory, AI Agents, AI debugging, AI Engineering, AI Infrastructure, Arize AI, autonomous agents, context graphs, developer-tools, graph databases, llm-applications, Machine Learning, observability, Phoenix OSS, RAG, reasoning systems, retrieval augmented generation, Self-improving agent

Building a self-improving agent on a context graph of human disagreement

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You can build a measurably better agent from data you already have, without retraining a thing. The data is what your experienced humans do when they correct the AI. Capture…

The post Building a self-improving agent on a context graph of human disagreement appeared first on Arize AI.

agent observability, AI agent harness, AI Agents, Arize AX, claude-code, Codex, coding-agents, cursor, Evals, gemini-cli, github-copilot, harness tracing, harness-engineering, LLM observability, MCP, Open Source, OpenTelemetry, phoenix

Coding agent tracing and evaluation: An open source tool to improve AI coding workflows

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Announcing coding harness tracing for observing, evaluating, and improving coding agent workflows across Claude Code, Cursor, Codex, GitHub Copilot, and Gemini CLI.

The post Coding agent tracing and evaluation: An open source tool to improve AI coding workflows appeared first on Arize AI.

agent observability, agent traces, Agents, AI Agents, AI Engineering, Alyx, dogfooding, Evals, LLM agents, LLM observability, trace debugging

How we use Alyx to build Alyx: How to build an AI agent feedback loop

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How Arize uses Alyx to debug Alyx: searching dense traces, aggregating failures, triaging dogfooding issues, and closing the AI engineering feedback loop.

The post How we use Alyx to build Alyx: How to build an AI agent feedback loop appeared first on Arize AI.

agent evaluation, agent observability, ai-reliability, autonomous agents, coding-agents, context platform, debugging agents, feedback loops, Observability & tracing, OpenInference, phoenix, self-improving agents, tracing

From observability to context: What’s next for Arize Phoenix

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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 the next release, but what this product becomes.

The post From observability to context: What’s next for Arize Phoenix appeared first on Arize AI.

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