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The infra layer for AI agent experience.
Scope is the system companies use to measure and improve how agents discover, interact and use their product. As more products gets used through AI agents like Claude Code, Codex, Cursor, and similar agents, agents are starting to influence which tools get chosen, how they get set up, and whether they keep getting used. Most companies still cannot see that process clearly. We run real workflows across agents and show teams when the agent picks them versus a competitor, where it breaks, where docs or product surfaces confuse the agent, and what to change to get better results and a better agent experience. I started Scope after working on interpretability research for closed-source models at Princeton and later as an ML engineer in GEO/AEO. I kept seeing the same pattern: these systems were shaping real product discovery and usage, but companies had very little visibility into what the model was actually doing. We are starting with products that agents can directly interact with, especially APIs, infra products, CLIs, and MCP servers.
Scope is an agent experience platform that helps make products discoverable and usable by AI agents. It provides full-stack AI search, monitoring, analytics, and actionable guidance to optimize how agents interact with your product.
Scope evaluates and monitors how AI agents discover, understand, and complete tasks across real user workflows. It analyzes agent behavior, usage friction, and revenue impact, captures tool calls and errors with latency and reasoning data, and presents concrete, actionable actions to improve the agent experience, including done-for-you solutions.
Who it’s for: Product teams and companies building AI agent integrations who want to improve agent discovery, usability, and conversion
Backed by Y Combinator; launch-focused content and product messaging
Anand is the founder of Scope, which helps companies understand and improve how AI agents discover, choose, and use their product. Before Scope, he did interpretability research on closed-source models at Princeton and later worked as an ML engineer in GEO/AEO. He started Scope after seeing that model behavior was becoming commercially important, while companies still had almost no visibility into how those systems were influencing product discovery and usability.
We help companies understand and improve how AI agents discover, choose, and use their product
Scope runs real workflows across AI agents and shows teams where agents choose competitors, get stuck, or encounter unclear documentation. The platform traces failures and highlights what product or documentation changes would improve agent selection and task completion.
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