JetBrains + Weights & Biases: Establishing frameworks and best practices for enterprise AI agents

2026-01-30 · Source: Weights & Biases · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, quick

Summary

JetBrains, a company known for its professional developer IDEs, is actively engaged in AI development, including its flagship AI agent, Juni. The company utilizes Weights & Biases (W&B) for observability and evaluation of its AI agents. Specifically, W&B is employed to analyze agent behavior, monitor token consumption, and optimize performance. Through W&B's console and logging capabilities, JetBrains identified critical issues within a core AI algorithm, leading to necessary corrections. The partnership with Weights & Biases also emphasizes transparency and dedicated support, with direct access to support teams for immediate assistance and product setup. JetBrains is making multiple strategic investments in AI, aiming to cover the full spectrum of AI development possibilities.

Key takeaway

For AI Engineers developing and evaluating AI agents, integrating a robust observability platform like Weights & Biases is critical. Your team can proactively identify and resolve fundamental algorithmic flaws, optimize resource consumption like tokens, and ensure agent reliability. Leverage dedicated support channels to streamline setup and troubleshoot issues efficiently, accelerating your development cycles.

Key insights

Observability tools are crucial for identifying and correcting fundamental issues in AI agent algorithms.

Principles

• AI agent development requires robust observability.
• Dedicated support enhances AI tool integration.

Method

Utilize an observability platform like Weights & Biases to monitor AI agent behavior, track token usage, and analyze logs for performance optimization and error detection during evaluation runs.

In practice

• Integrate W&B for AI agent token monitoring.
• Review W&B logs to debug core AI algorithms.

Topics

• AI Agents
• Weights & Biases
• AI Observability
• Algorithm Evaluation
• JetBrains AI

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

Related on AIssential

• The Truth About Agents in Production — Effective AI agent development prioritizes type safety, robust evaluation, and a problem-first approach over complex multi-agent systems.
• Getting started with Weights & Biases for robotics — W&B streamlines VLA model fine-tuning for robotics by centralizing experiment tracking, artifact management, and team collaboration.
• True Positive Weekly #153 — The AI landscape is rapidly evolving across benchmarks, verification, agent skills, and economic integration.
• InsightFinder raises $15M to help companies figure out where AI agents go wrong — AI observability must encompass the entire tech stack, correlating data, models, and infrastructure for comprehensive diagnosis.
• [AINews] not much happened today — AI progress spans non-invasive BCIs, efficient inference, agent orchestration, and open-weight model commercialization, driven by infrastructure and evaluation.
• How agent o11y differs from traditional o11y — Phil Hetzel, Braintrust — Agent observability requires specialized approaches due to LLM non-determinism, complex trace data, and diverse stakeholder involvement.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Weights & Biases.