# Industry: Microsoft Research - AgentRx

---
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。
