Baseline Labs is building foundtional infrastructure for reliable AI agents.

Baseline Labs operates at the intersection of product engineering & applied research, focused on understanding how AI agents behave in real-world systems and translating those insights into practical, production-ready tools.

Our work centers on the core challenges of modern agent-based systems: observability, reliability, performance, and control. As autonomous agents grow more complex and interconnected, traditional software abstractions break down. Baseline Labs studies these systems at a structural level on how agents reason, execute, interact, and fail, then builds infrastructure that makes these behaviors measurable, understandable, and operable at scale.

Rather than treating agents as black boxes, we design systems that expose their internal execution, decision pathways, and system impact. This research-to-product loop allows us to build tools that are not only technically robust, but grounded in how agent systems actually function in practice.

AgentBasis

AgentBasis is the first product being developed by Baseline Labs. It is aimed to be the OS for reliable AI agents to provide deep visibility into AI agent behavior across development and production environments

AgentBasis enables teams to observe how agents execute tasks, interact with tools and models, and propagate decisions through complex workflows. By capturing execution traces, performance metrics, and system-level signals, AgentBasis helps engineers understand not just what an agent outputs, but how and why it arrived there. The platform serves as a core layer for operating agent-based systems—supporting debugging, performance analysis, cost and latency awareness, and long-term system reliability.

Beyond observability, AgentBasis is being designed as a broader execution and evaluation layer for agent systems. The platform’s roadmap includes capabilities such as controlled sandboxing environments, systematic agent evaluations, crash testing under failure and edge-case scenarios, and simulated real-world conditions that mirror production constraints. These primitives allow teams to stress-test agent behavior, measure robustness, and safely iterate before deployment. Together, these capabilities position AgentBasis as a foundational layer for building, validating, and operating reliable autonomous agents in real-world systems.