Baseline Studio vs Poolside

Baseline Studio is a New York-based entity focused on developing agent-native tools and systems designed to enhance the capabilities of AI agents. The core philosophy revolves around making the implicit explicit for agents, ensuring they have access to well-documented rules and instructions within their operational environment. This approach aims to maximize agent performance, particularly during the discovery and operational phases, by providing a structured system and readily accessible knowledge. The studio emphasizes designing the system architecture around the agent, rather than solely focusing on the agent model itself. This includes meticulous organization of context, clear documentation of rules, and efficient surfacing of knowledge precisely when an agent requires it. Baseline Studio offers open-source tools and frameworks to support these principles, enabling developers and creators to build more robust and effective AI-driven applications. Each project undertaken by Baseline Studio represents a unique implementation of this central idea: empowering agents by providing them with a navigable system. The ultimate goal is to enable agents to perform real work by ensuring they are equipped with the necessary information and contextual understanding. The studio is actively developing new projects and invites collaboration from those who find value in its current offerings or have ideas for future endeavors.

Poolside delivers open-weight foundation models, including the Laguna series (Laguna s 2.1 and Laguna xs 2.1), optimized for coding tasks. Its platform blends model research with a powerful coding agent that can plan work, edit repositories, execute operations, and integrate with codebases, documentation, and agent server workflows. Designed for developers, software teams, and organizations—including government and enterprise users—Poolside provides web, CLI, API, and desktop access, enabling seamless AI‑assisted development across any environment.

• Focuses on agent-native tools and systems.
• Emphasizes making implicit information explicit for AI agents.
• Develops open-source tools and frameworks.
• Designs systems around the agent's needs for context and knowledge.

• Open‑weight models enable customization and privacy
• Available on Web, CLI, API, and Desktop
• Integrated coding agent handles planning, editing, and execution
• Tailored for enterprise and government scale

• Limited number of completed projects publicly showcased.
• The website provides high-level concepts with fewer detailed technical specifications for immediate use.
• Some aspects of the 'agent-native' concept may require a deeper understanding of AI agent architecture.

• User interface is functional but not highly polished
• Initial setup may require technical expertise
• Community and ecosystem are still maturing