The question sounds almost absurd until you realize how real it's become: What happens when an AI agent is looking for your product—and can't find it?
For companies selling APIs, developer tools, and infrastructure software, this isn't hypothetical anymore. As AI agents increasingly act as gatekeepers between users and software, a new species of anxiety has taken root. You might have built the best database, the fastest CLI, the most elegant API. But if Claude or ChatGPT doesn't surface it when a developer asks for help? You might as well not exist.
Scope, a San Francisco startup that emerged from Y Combinator's Spring 2026 batch, thinks it has an answer. The company launched what it calls an "Agent Experience Platform" around mid-May, designed to solve a problem that barely registered two years ago: testing whether AI agents can actually discover, recommend, and use your product when it matters.
"We make your product discoverable and usable by any AI agent," the company states on its YC profile. The pitch is straightforward, perhaps deceptively so. What Scope is really offering is something closer to quality assurance for the age of agentic software—a way to know if you're visible in a world where the search bar is disappearing.
Testing in the Dark
Traditional monitoring tools tell you when your API breaks. Scope operates a layer above that, running real workflows across AI agents daily to see whether those agents even think to try your product in the first place.
The platform's architecture rests on three pieces: monitoring, analytics, and what the company calls "action." The monitoring layer simulates actual use cases—developers asking agents for help, agents attempting to complete tasks with specific tools. Analytics tracks what happens during each run: which tool calls get made, where errors crop up, friction points, latency spikes, and crucially, the agent's reasoning process as it decides what to use.
It's the third component where things get interesting. Rather than generating reports and leaving teams to figure out what to do next, Scope suggests specific changes—tweaks to documentation, adjustments to product surfaces—and models the expected impact before anything ships. Founder Anand-Arnaud Pajaniradjane described this in a LinkedIn post as the ability to "A/B test changes before going live with expected impact" on metrics like agent mentions, citations, and traffic referrals.
According to company materials, Pajaniradjane's background feels almost tailored for this moment. He spent time on interpretability research for closed-source models at Princeton before shifting into machine learning engineering with a focus on what's now called generative engine optimization. He emphasizes that Scope doesn't scrape AI outputs after the fact—it replicates the search engine itself, running the same queries and workflows agents would encounter in the wild.
Early Signals
Scope claims to be working with Blaxel, a YC-backed platform for persistent agent sandboxes, and a "decacorn" customer in its launch materials, though it hasn't named names publicly. The company is in early access with demo-led onboarding. Pricing hasn't been disclosed.
The timing isn't accidental. Industry chatter around "agent discoverability" and "agentic search optimization" has intensified in recent months. TechRadar Pro ran a piece in April arguing that visibility no longer means ranking on a results page—it means being selected in AI-generated answers. Google released updated guidance on AI search optimization in May. Companies like Honeycomb and Glean announced agent observability features around the same period, reading the same tea leaves.
A Nascent Category Gets Crowded
Scope is hardly alone. At least half a dozen startups now chase some version of agent visibility or interaction tracking. Surfex audits the machine-readable channels agents rely on. Elba translates APIs into executable interfaces agents can parse. Astrant focuses on attribution reporting across ChatGPT, Claude, and Perplexity.
What sets Scope apart—or at least what it claims sets it apart—is the emphasis on continuous testing paired with pre-deployment impact modeling. The promise isn't just insight; it's prediction. Will this documentation change actually move the needle? Scope wants to tell you before you waste engineering time finding out.
The company remains small. YC lists the team size as one, though LinkedIn suggests somewhere between two and ten employees—startup opacity at its finest. Plenty of specifics remain murky. Which agents does the platform actually support? How does pricing work beyond "talk to us"? What does "full-stack AI search platform" mean under the hood, technically speaking?
Those answers will presumably arrive as Scope transitions from early access to broader availability. In the meantime, the product represents something stranger than it first appears: a tool for optimizing not for search engines or human users, but for the AI intermediaries now mediating between the two.
Whether this becomes a category or a footnote depends partly on whether agent-driven discovery becomes as dominant as Scope's thesis assumes. But for now, at least, the company is building for a future where being technically excellent isn't enough. You also have to be machine-readable—and not just readable, but preferred. That's a different game entirely.
