Weights & Biases for Agent Development: An Overview

Weights & Biases is well known in machine learning for experiment tracking, but its tooling has expanded to cover the development of applications built on large language models and AI agents. The company's Weave toolkit focuses specifically on tracing, evaluating, and monitoring these systems. This overview explains how Weights & Biases supports agent development and what Weave brings to the workflow.

From Experiment Tracking to Agent Tooling

Weights & Biases built its reputation as a platform for tracking machine-learning experiments, logging metrics, and comparing model runs. As teams shifted toward language-model and agent applications, the company introduced Weave, a toolkit aimed at the distinct challenges of building with these models. Where traditional experiment tracking centers on training runs, Weave centers on the runtime behavior of applications that call models, use tools, and chain steps together.

This means agent developers can work within a familiar vendor ecosystem while gaining tools designed for the non-deterministic, multi-step nature of agent systems.

Tracing Agent Behavior

A core capability of Weave is tracing. It automatically logs the inputs, outputs, code, and metadata involved in your application, capturing language-model calls and the surrounding logic. A common pattern is decorating functions so that each call is recorded without extensive manual setup, which keeps instrumentation lightweight.

Weave organizes these logs into a trace tree that mirrors the structure of an agent run. Metrics such as latency and cost are aggregated at every level of the tree, so you can move from a high-level view of a run down to an individual model call to find the root cause of a problem. For agents that branch and loop, this hierarchical view makes otherwise opaque behavior easier to follow.

Evaluation and Iteration

Beyond observing runs, Weave supports systematic evaluation. You can run scoring functions over datasets to measure output quality, using either custom scorers or pre-built ones, and compare results across different prompts, models, or versions through comparison dashboards. This turns informal trial-and-error into a more disciplined process of measuring whether a change actually improved the system.

The toolkit also includes a playground for experimenting interactively with different models and prompts, which is useful during the early iteration phase before changes are committed to an evaluation suite.

Feedback, Guardrails, and Production Use

Weave can capture user and expert feedback alongside automated scores, giving a fuller picture of how an application performs in real use rather than relying on metrics alone. It also offers guardrail features intended to help protect applications, such as content moderation and prompt-safety checks.

In production, the same tracing and monitoring tools that support development continue to provide visibility into deployed agents. This continuity means teams do not need to switch tools as they move from building an agent to operating it.