# Agent Seer: Synthesizing Scenarios from Specification Understanding

DevFeed: [Agent Seer: Synthesizing Scenarios from Specification Understanding](<https://devfeed.tech/articles/agent-seer-synthesizing-scenarios-from-specification-understanding-6727.md>)

Original publisher: [Read original article](<https://machinelearning.apple.com/research/agent-seer-synthesizing-scenarios>)

Published: 2026-08-28T00:00:00Z

Content type: article

Language: en

Sources: [Apple Machine Learning Research](<https://devfeed.tech/sources/apple-machine-learning-research.md>)

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>), [ai observability](<https://devfeed.tech/topics/ai-observability.md>)

Tags: [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [model-context-protocol](<https://devfeed.tech/tags/model-context-protocol.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>), [tool](<https://devfeed.tech/tags/tool.md>), [tools](<https://devfeed.tech/tags/tools.md>)

## AI overview

Agent Seer generates realistic, multi-turn evaluation scenarios for tool-using AI agents from a single MCP specification, without examples, live tool access, or domain-specific tuning. It enriches schemas, creates synthetic tool outputs, and evaluates tool-calling correctness and conversational coherence.

## Source excerpt

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 that cannot track evolving APIs. We observe that tool specifications--function names, natural-language descriptions, and typed parameter schemas--already encode sufficient semantic information to synthesize realistic evaluation scenarios without manual curation or live tool execution. Agent Seer...