Apple Researchers Built a Tool That Writes Its Own AI Tests

Key points

• Agent Seer, developed by Apple ML Research, automatically generates evaluation scenarios for AI agents by reading tool specifications rather than by hand-writing tests.
• Current AI agent benchmarks are static, meaning they go out of date every time an API or software tool changes its behaviour.
• The system works without running any live tools, cutting the cost and complexity of building test environments.
• Agent Seer targets a gap that grows as AI agents, software that can carry out multi-step tasks on its own, become more common in real workplaces.

Testing an AI agent is harder than it sounds.

An AI agent, software that can carry out multi-step tasks on its own, does not just answer a question. It calls external tools, checks the results, and tries again across several conversation turns. To test whether it is doing that well, you need realistic, believable scenarios that reflect how real users actually combine those tools.

Until now, building those scenarios meant hiring domain experts to write them by hand. That is slow, expensive, and breaks the moment an API, the connection point between software services, updates its behaviour.

What does Agent Seer actually do?

It reads the tool specification itself and writes the test scenarios from that.

Every software tool comes with a specification: a function name, a plain-English description of what the tool does, and a typed schema that lists the parameters it accepts. Agent Seer, detailed by Apple ML Research, treats those specifications as a source of meaning. The system extracts enough information from them to generate realistic multi-turn conversation scenarios automatically, without ever running the tool live.

Think of it as a teacher who can write a credible exam question simply by reading the course syllabus, without sitting through every lecture.

That matters for two reasons. First, it scales. A team can cover hundreds of tools without writing a single test by hand. Second, when an API changes its specification, Agent Seer can regenerate the scenarios to match, keeping the benchmark current.

Why does this matter for ordinary people?

Most people will not interact with Agent Seer directly. But the quality of AI agent testing shapes how reliably those agents behave in products that real people use.

If you use an AI assistant to book a calendar appointment, summarise your email, or pull data from a business system, that assistant is an agent calling external tools. Poor testing means those agents fail quietly, booking the wrong time or returning stale data without flagging an error.

Better automated evaluation is one way the industry can catch those failures before they reach users.

What happens next?

Agent Seer addresses a structural problem: the more tools an AI agent can use, the harder it is to keep evaluation benchmarks up to date. The research suggests that tool specifications already contain enough information to close that gap without manual effort.

The practical question is adoption. Benchmark frameworks only improve safety and reliability if developers actually use them. Whether Agent Seer becomes a standard part of the agent-development pipeline will depend on how well it integrates with the tool ecosystems practitioners already use.

Common questions

Do I need to understand AI development to care about this?

No. This research is plumbing, the kind of infrastructure work that improves AI products behind the scenes. Better testing means the AI tools you already use are less likely to behave unexpectedly.

Does Agent Seer test tools in a live environment?

No. It generates test scenarios by reading tool specifications only, without executing any real tools. That is part of what makes it practical to scale.

Is Agent Seer available to developers now?

The research describes the system and its approach, but no public release date or open-source repository was announced in the published work.