Collect first agent research and market sources

2026-07-08 12:08:24 +08:00
parent 36c2499d3c
commit 1cbc98e432
34 changed files with 3417 additions and 58 deletions
@@ -0,0 +1,68 @@
+# 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。