agent/industry/items/2026-07-08-microsoft-agentrx.md

Industry: Microsoft Research - AgentRx

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type: industry company: Microsoft team: Microsoft Research title: "Systematic debugging for AI agents: Introducing the AgentRx framework" url: https://www.microsoft.com/en-us/research/blog/systematic-debugging-for-ai-agents-introducing-the-agentrx-framework/ source_name: Microsoft Research source_type: research-blog source_quality: official published_at: collected_at: 2026-07-08 status: analyzed topics:

• agent-evaluation
• observability
• debugging implementation_signals:
• trajectory-normalization
• constraint-synthesis
• constraint-checking
• failure-taxonomy product_area:
• agent-debugging models:

tools:

• AgentRx benchmarks:
• AgentRx Benchmark related_papers:
• 2025-vijayvargiya-openagentsafety related_jobs:
• 2026-07-08-tencent-cloud-ai-agent-test-engineer related_experiments:

related_projects:

evidence_level: high relevance: high

One-line Takeaway

Agent 调试需要从“最终失败”定位到 trajectory 中第一个不可恢复错误步骤。

What They Built or Claimed

Microsoft Research 发布 AgentRx 和 115 条人工标注失败轨迹 benchmark，用于定位 Agent trajectory 的 critical failure step。

Technical Signals

• architecture: trajectory normalization -> constraint synthesis -> guarded evaluation -> LLM judge。
• tool use: 根据 tool schema 和 domain policy 生成可执行约束。
• memory:
• evaluation: 逐步检查约束，输出可审计验证日志。
• safety: 区分计划偏离、幻觉、新信息发明等失败类型。
• deployment: domain-agnostic debugging framework。
• data: tau-bench、Flash、Magentic-One 失败轨迹。

Evidence Quality

官方研究博客，包含框架、benchmark 规模、失败分类，证据强。

Gaps for Us

• 本知识库需要设计统一 Agent trace schema 和失败 taxonomy。