Directory AI & ML PlatformsAI Governance & SafetyWeights & Biases Weave

Weights & Biases Weave

About Weights & Biases Weave

Weave is an observability product for production agents. It helps production agents learn and improve from real-world experience. It provides end-to-end observability to monitor agent behavior. It includes out-of-the-box signals to surface failure modes and a flexible evaluation framework to prevent regressions. Coding agents like Claude Code can connect to Weave to iterate on agents continuously.

Products

Traces: Used to debug agents and AI applications.
Evaluations: Performs rigorous evaluations of agentic AI systems.
Playground: Used to explore prompts and models.
Guardrails: Blocks prompt attacks and harmful outputs.
Monitors: Used to continuously improve agents in production.
Leaderboards: Aggregates evaluations into leaderboards featuring top performers that can be shared across an organization.

Used for

Monitoring agent behavior using built-in and custom signals.
Tracing behavior across multi-agent systems organized into sessions and turns.
Measuring improvements to harnesses and models with an evaluation API.
Testing new LLMs and custom models against production traces.
Routing alerts through Slack notifications and webhook automations.

From Weights & Biases Weave's own website, read 2026-10-23. How we build listings.

How to evaluate AI Governance & Safety

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Weights & Biases Weave. It comes from our AI Governance & Responsible AI buyer guide.

25%

AI Inventory & Lifecycle Governance

Automated discovery and a registry of every model, LLM/prompt, agent, and embedded-AI feature (including shadow and third-party AI); risk tiering by use case; intake-to-retirement workflow with approvals, deployment gates, and named owners; versioning and change control

25%

Regulatory Mapping & Compliance Evidence

Out-of-the-box, maintained policy packs for the EU AI Act, NIST AI RMF, ISO 42001, SR 11-7, and sector rules (e.g. NYC LL144); control mapping and gap analysis; one underlying assessment that satisfies many frameworks; audit-ready, exportable evidence and model cards

20%

Model Monitoring & Explainability

Production monitoring for drift, performance decay, and data quality; bias and fairness testing across protected groups; explainability (e.g. SHAP / feature attribution) for tabular and NLP models; the depth of this domain is what separates observability tools from pure GRC

15%

GenAI & Agentic Oversight

LLM evaluation and guardrails (hallucination, toxicity, PII leakage, prompt-injection and jailbreak defense); agent discovery, runtime policy enforcement, and a logged trail of agent actions and the authority behind them; red-teaming and continuous evaluation of agent traces

10%

Integration & ML-Stack Fit

Connectors to your model platforms (SageMaker, Vertex AI, Databricks, Dataiku, MLflow, Bedrock), CI/CD and registries; API and policy-as-code coverage; identity (SSO/RBAC); fit with existing GRC and data-security tooling rather than yet another silo

Human-in-the-Loop & Accountability

Cross-functional workflows that reach risk, legal, and business owners (not just data scientists); reviewer sign-off and attestations; issue and exception tracking; reporting that a board or regulator can read, with a defensible audit trail

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Quick Facts

wandb.ai/site/weave

CategoryAI & ML Platforms

SubcategoryAI Governance & Safety

PricingFree tier

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