---
title: "Root Cause Analysis"
description: "2 posts about Root Cause Analysis, summarised, each linking to the original."
---

# Root Cause Analysis
> 2 posts about Root Cause Analysis, summarised, each linking to the original.

## Articles

### [Seer, the Sentry MCP and CLI, or your own coding agent: where each one fits](https://yomu.fyi/post/seer-the-sentry-mcp-and-cli-or-your-own-coding-agent-where-each-one-fi.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Paul Jaffre
- Published: Oct 2, 2026
- Type: Explainer

Seer is Sentry’s agent debugger that reasons across errors, traces, logs, profiles, and linked repositories to identify root causes, not just surface-level stack traces. The Sentry MCP, set up via the agent plugin with a single command, bridges Sentry’s data into coding agents like Claude Code and Cursor, enabling them to invoke Seer’s full analysis. The new Sentry CLI provides natural-language access to Seer’s diagnosis and issue management directly from the terminal, using selectors like @latest and @most\_frequent. These tools are complementary: Seer provides deep diagnosis, while the MCP and CLI deliver that context into the environments developers already use. Teams can automate the entire workflow so Seer diagnoses, an agent fixes, and a draft PR awaits review without human involvement until approval, with Seer triggering on issues meeting event count and fixability thresholds.


### [Graph-centric agentic intelligence](https://yomu.fyi/post/graph-centric-agentic-intelligence.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Imen Grida Ben Yahya, Nameet Dutia
- Published: Oct 1, 2026
- Type: Problem & solution

Graphs encode relationships and provide scaffolding for causal inference and compositional reasoning, which is essential for AI agents reasoning over complex networks. The post traces the evolution of graph intelligence from passive topology models to active agentic reasoning, culminating in a three-pillar approach for root cause analysis. The approach combines a continuously synchronized network digital twin graph with a cascaded pipeline of graph algorithms—decomposition, clustering, and centrality ranking—that narrow the search space. An agentic orchestration layer selects and composes these algorithms adaptively based on failure characteristics, performing complexity triage and consulting an incident knowledge base. The system achieved root cause analysis in minutes on commercial networks, demonstrated with NTT DOCOMO at MWC.