Arize Phoenix

AI & ML

Neotask on OpenClaw automates your LLM observability pipeline through Arize Phoenix — monitoring traces, managing prompts, and running experiments so your AI systems stay reliable.

What You Can Do

Your AI agent turns Arize Phoenix into a fully automated LLM observability operation. It monitors your AI pipelines, manages prompt versions, and runs experiments — keeping your models reliable without constant manual oversight.

Pipeline Monitoring

Your agent continuously inspects traces and spans across projects. It identifies error patterns, reviews span annotations, and surfaces sessions where quality degraded. Schedule regular health checks and get alerted before issues reach users.

Prompt Lifecycle Management

Manage prompts as versioned, tagged assets. Your agent creates new versions, tags releases as production or staging, and tracks iteration history. When you need to roll back, it knows every version that ever existed.

Automated Experimentation

Build evaluation datasets from real production traces. Your agent adds examples from interesting spans, runs experiments against datasets, and compares results across prompt versions. Quantify improvements before deploying them.

| Area | What Your Agent Handles |

|------|------------------------|

| Prompts | Version management, tagging, upserts, iteration tracking |

| Traces & Spans | Inspection, annotation review, error detection |

| Datasets | Example management, experiment execution, regression testing |

| Projects | Multi-project monitoring, session tracking, health checks |

Every action runs autonomously or requires your approval — you decide.

Try Asking

  • "Check all traces from the last hour and flag any with error spans"
  • "Tag the latest version of our 'customer-support' prompt as 'production'"
  • "Add the 10 most recent failed traces as examples to our regression test dataset"
  • "Run an experiment comparing prompt v4 against v5 on the 'classification' dataset"
  • "What annotations exist for spans in the 'search-pipeline' project?"
  • "Show me all sessions from today with more than 3 turns"
  • "List every prompt version we've deployed to production in the last month"
  • "Create a new prompt called 'invoice-extractor' from this template"

    • Pro Tips

  • Schedule hourly trace checks during high-traffic periods — your agent catches regressions before they compound
  • Use approval gates for prompt version tagging — review changes before marking anything as production
  • Multi-agent teams excel here: one agent monitors traces, another manages prompts, a third runs experiments
  • Build regression datasets from real failures — they catch edge cases synthetic data misses
  • Session-level analysis reveals multi-turn conversation issues that single-trace inspection misses
  • Combine Phoenix with your alerting integration to get notified the moment trace quality drops

    • Works Well With