A primer on building successful AI agents
AI agents can dramatically boost productivity and innovation, but getting them into the real world takes a lot of iteration. Quality, governance, and security need to be baked into your development process from the start. Having the right tools and development workflows makes all these iterations possible.
Download this whitepaper to learn:
- What defines agentic applications and why observability is critical
- A state-of-the-art workflow for building agentic AI applications
- How pioneering companies are building and deploying AI agents today
Whether you’re exploring agents for the first time or refining your current approach, this primer delivers actionable insights to help your team succeed and thrive in the AI era.

Download now

Square accelerates the development and evaluation of new LLM candidates to power the Square Assistant, bringing conversational AI to businesses of all sizes.

Canva optimizes MLOps using Weights & Biases, leveraging the Model Registry to seamlessly transition from experimentation to deployment. This empowers Canva’s ML team to enhance user experiences for over 150 million monthly active users through advanced AI capabilities in design and publishing.

Leonardo.ai leverages AWS and Weights & Biases to scale their GenAI platform, enabling creators to produce high-quality, customizable art assets for various industries. This collaboration accelerates the development and deployment of cutting-edge AI models, democratizing access to advanced GenAI tools.
Scalable and Secure
With Weights & Biases you can:
Overview
- Company size: 300+
- Industry: Autonomous vehicles


Problem

Solution
Instead of tinkering with brittle internal tools and ad-hoc solutions for experiment tracking and prediction visualizations, the ML team was able to standardize with Weights & Biases’ lightweight experiment tracking and visualization solutions.
The Weights & Biases dashboard gave machine learning practitioners a command center to compare across dataset and model versions, maintaining a reliable record of every experiment and result. ML engineers are now free to focus on the valuable work of model development, accelerating project progress.

