AgentsAugust 28, 2026via Apple Machine Learning
Agent Seer: Synthesizing Scenarios from Specification Understanding
Why it matters
Agent Seer addresses a critical gap in agent evaluation: scaling realistic, multi-turn tool-use benchmarks without manual effort or live API dependencies. This matters for practitioners deploying agents across evolving tool ecosystems and for building reliable agent evals that track real-world complexity.
Key signals
- Tool specification-driven scenario synthesis (function names, descriptions, typed schemas)
- Multi-turn conversation iteration modeling without manual curation
- API-agnostic approach—avoids live tool execution and static benchmark decay
- Targets practitioner pain: tool composition and agent-tool interaction evaluation
- Published by Apple ML Research
- Apple research release: Agent Seer framework for synthetic agent evaluation
- Generates test scenarios from tool specifications alone (function names, descriptions, schemas)
- Eliminates manual curation and live tool execution dependency
- Addresses static benchmark drift as APIs evolve
- Targets multi-turn tool composition—the real practitioner use case
- Published via Apple's ML research group (credible source, likely to influence enterprise evaluation)
The hook
Apple's new framework generates realistic agent test scenarios from tool specs alone—no manual curation, no live APIs required.
Evaluating AI agents that use external tools requires realistic test scenarios that capture how practitioners compose tools and iterate across conversation turns. Constructing such scenarios by hand demands deep domain expertise, does not scale across tool ecosystems, and produces static benchmarks …