Arize AI
AI observability and evaluation
AI traces, OpenTelemetry instrumentation, model calls, retrieval, tool use, evals, quality monitoring, and troubleshooting.
// agentpm vs arize ai
Model behavior observability and coding-agent work observability are different layers.
Arize AI observes AI and LLM application behavior. AgentPM watches software get built by coding agents.
different problems / complementary layers
AgentPM
Coding-agent SDLC observability
Plans, commands, file edits, tests, retries, decisions, and the evidence trail behind software work.
// key differences
Arize AI observes LLM applications. AgentPM observes coding-agent development sessions.
Arize and Phoenix help teams understand AI application behavior. AgentPM helps teams understand engineering execution by coding agents.
Arize AI
AgentPM
Observes AI/LLM application traces, retrieval, tool use, model calls, and quality metrics.
Observes coding-agent plans, commands, file edits, tests, retries, and engineering decisions.
Helps troubleshoot and evaluate AI application behavior across development and production.
Helps review and govern how coding agents build software before production exists.
Helps answer "Where did this LLM application fail or degrade?"
Helps answer "What did the coding agent do and what evidence supports it?"
Focuses on traces, spans, evals, RAG analysis, model quality, and monitoring.
Focuses on work provenance, implementation context, tests, handoffs, and coaching.
Uses the application trace, span, evaluation, or model event as the unit of analysis.
Uses the software work session as the unit of analysis.
// what agentpm captures
The session is the artifact.
AgentPM captures the work trail that usually disappears between a developer's local coding-agent session and the final PR.
Plans
What the coding agent intended to do before editing.
Commands
The shell work, outputs, failures, and retries behind the result.
File Edits
Which files changed and how the implementation moved.
Tests
What was verified, skipped, retried, or left unproven.
Branches
The route from local session work toward PRs and commits.
Decisions
The why behind tradeoffs, reversals, and handoff context.
// compounded knowledge
The optimization loop is different for coding agents.
Observability is not just about catching failures. The long-term value is turning repeated behavior into better future performance.
For user-facing agents, knowledge compounds into better experiences.
Customer context, prior interactions, feedback, and evaluation data help AI products become more useful, personalized, and reliable for end users.
For coding agents, knowledge compounds into engineering efficiency.
Plans, commands, file edits, failed tests, reviewer feedback, and decisions become reusable workflow knowledge that reduces retries and improves future coding-agent sessions.
// summary breakdown
Arize AI watches AI applications run.AgentPM watches software get built.
Use Arize AI when you need AI observability and evaluation.
It is the right layer for OpenTelemetry-based tracing, LLM evals, RAG analysis, model monitoring, and troubleshooting AI application behavior.
Use AgentPM when you need agent-work observability.
It is the right layer for reconstructing how coding agents planned, edited, tested, retried, and decided while building software.
If your question is about model behavior in a deployed AI product, start with AI observability. If your question is about what coding agents did during engineering work, start with AgentPM.