Neotask övervakar dina ML-modeller med Arize Phoenix - Neotask-agenter spårar prestanda, identifierar drift och utvärderar modellkvalitet automatiskt.
Övervaka modellprestanda, spåra prediktioner och identifiera datadrift genom konversation
Utvärdera LLM-svar, analysera spårningsdata och felsök modellbeteende
Generera insiktsrapporter, jämför modellversioner och optimera ML-pipelines direkt i chatten
Vad du kan göra
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.
Pipelineövervakning
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-livscykelhantering
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.
Automatiserad experimentering
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.
Område
Vad din agent hanterar
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
Varje åtgärd körs autonomt eller kräver ditt godkännande - du bestämmer.
Prova att fråga
"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"
Proffstips
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
Multiple workspaces and capacity for larger teams.
Works Well With
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