New in Opik: Native OpenClaw Observability, Custom Dashboards, Optimization UI Upgrades, & More!

2026-03-26 · Source: Comet · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

Opik has released significant updates designed to enhance visibility into AI development performance, cost, and quality. Key additions include the new Opik-OpenClaw plugin, which provides native observability for OpenClaw agents by capturing LLM calls, tool execution, memory steps, and agent handoffs, alongside tracking token usage, costs, and output quality. The Optimization Studio now offers improved tracking and comparison for prompt optimization runs, enabling users to identify strong results based on metrics like accuracy, latency, and cost. Furthermore, custom dashboards have been enhanced with multi-project and experiment-specific views, separate data sources for widgets, and auto-save functionality. The platform also expanded support for models like Gemini 3.1 and Claude Sonnet 4.6, added OpenAI TTS tracing, and introduced full-text search for traces.

Key takeaway

For NLP Engineers building and deploying AI agents, these Opik updates offer critical tools to diagnose agent failures, optimize prompt performance, and monitor system health. You should integrate the Opik-OpenClaw plugin to gain deep visibility into agent reasoning and costs, and leverage the Optimization Studio to systematically compare and refine your prompt engineering efforts, ensuring more robust and cost-effective AI applications.

Key insights

Opik's updates enhance AI development visibility through agent observability, prompt optimization, and flexible dashboards.

Principles

• Agent workflow visibility is crucial for debugging.
• Prompt optimization requires detailed metric comparison.
• Customizable dashboards improve AI system monitoring.

Method

The Opik-OpenClaw plugin captures LLM calls, tool execution, memory steps, and agent handoffs to provide agent behavior insights, tracking token usage, costs, and output quality.

In practice

• Use `opik_openclaw` for agent workflow visibility.
• Track prompt optimization runs in Optimization Studio.
• Create multi-project dashboards for AI system monitoring.

Topics

• OpenClaw Observability
• Prompt Optimization
• Custom AI Dashboards
• LLM Agent Workflows
• Model Provider Support

Best for: NLP Engineer, MLOps Engineer, AI Engineer, Machine Learning Engineer

Related on AIssential

• Native Observability & Alerts for Your OpenClaw with Opik — Native observability for AI agents is crucial for understanding behavior, optimizing costs, and ensuring reliability in production.
• Introducing Opik Test Suites: Straightforward Unit & Regression Testing for AI Agents — Agent development benefits from software testing principles for consistent, predictable, and safe production deployment.
• AI agent observability: what enterprises need to know — AI agent observability is a distinct discipline providing deep visibility into agent reasoning, behavior, and coordination.
• AI Observability Starter Kit for Microsoft Foundry agents — Production-grade AI observability requires instrumented traces, automated quality and safety evaluations, and proactive alerting.
• OpenClaw Architecture - Part 6: Reliability, Observability, and Evaluation — Production-ready agents require robust control planes, durable evidence, and continuous evaluation beyond simple demos.
• Mind the Gap (In your Agent Observability) — Amy Boyd & Nitya Narasimhan, Microsoft — Effective AI agent observability requires continuous evaluation, monitoring, and automated optimization to manage non-determinism and ensure reliability.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Comet.