From 6a7500130ebb0d31eaf87c8903678f190766c73d Mon Sep 17 00:00:00 2001 From: Damien Date: Sat, 18 Jul 2026 15:53:24 +0200 Subject: [PATCH] Fix French colon spacing in blog post --- .../blog/aiops-observabilite-reseau-ia/index.en.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/blog/aiops-observabilite-reseau-ia/index.en.md b/content/blog/aiops-observabilite-reseau-ia/index.en.md index d4f2ef6..b715421 100644 --- a/content/blog/aiops-observabilite-reseau-ia/index.en.md +++ b/content/blog/aiops-observabilite-reseau-ia/index.en.md @@ -11,7 +11,7 @@ tags: - AI Agents --- -I just finished building a full observability stack on my EVPN/VXLAN lab: topology, telemetry, and near real-time visualization. While building it, I realized I wasn't just doing monitoring — I was preparing a network to one day be "seen" by an AI. This first article sets the frame for a five-part series: why observability is the mandatory prerequisite before plugging an agent into your infrastructure. +I just finished building a full observability stack on my EVPN/VXLAN lab: topology, telemetry, and near real-time visualization. While building it, I realized I wasn't just doing monitoring : I was preparing a network to one day be "seen" by an AI. This first article sets the frame for a five-part series: why observability is the mandatory prerequisite before plugging an agent into your infrastructure. @@ -26,7 +26,7 @@ The result is a stack combining four building blocks: - a **time-series database** for metric storage; - a **visualization layer** automatically regenerated by cross-referencing topology and metrics. -It works today. But it's not the stack itself that made me write this article: it's a realization I had while building it. I wasn't just setting up monitoring — I was laying the foundations of a pipeline that could, one day, allow an AI agent to reason about my network. And that has a name. +It works today. But it's not the stack itself that made me write this article: it's a realization I had while building it. I wasn't just setting up monitoring : I was laying the foundations of a pipeline that could, one day, allow an AI agent to reason about my network. And that has a name. ## AIOps, not MLOps @@ -59,7 +59,7 @@ Observability, then, is not an end in itself. It's **the condition of possibilit What helped me structure all this was thinking in stacked layers, and accepting that you don't skip a step: 1. **Infrastructure first.** Is the base data reliable, complete, up to date? Topology, metrics, logs. -2. **The model next.** What's built on top: a correlation rule, a model, an LLM — does it receive quality inputs, and can its relevance be measured over time? +2. **The model next.** What's built on top: a correlation rule, a model, an LLM : does it receive quality inputs, and can its relevance be measured over time? 3. **Agent behavior last.** Can its answers and actions be trusted, and can they be audited? The order isn't negotiable. An agent built on top of a shaky layer 1 inherits all its weaknesses in cascade, with the added bonus of the illusion of reliability that comes from an assertive answer by construction. You can't make up for a poorly instrumented infrastructure with a better prompt. @@ -101,10 +101,10 @@ That's why observability comes first, and the agent comes last: not because AI i ## Resources ### Concepts -- [What is AIOps?](https://www.ibm.com/think/topics/aiops) — definition and origin of the term (Gartner) -- [Model Context Protocol — official documentation](https://modelcontextprotocol.io/docs/getting-started/intro) -- [Introducing the Model Context Protocol](https://www.anthropic.com/news/model-context-protocol) — the original announcement, which frames well the problem MCP solves -- [Effective context engineering for AI agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) — the reference article on context engineering, including the phenomenon of context window saturation +- [What is AIOps?](https://www.ibm.com/think/topics/aiops) : definition and origin of the term (Gartner) +- [Model Context Protocol : official documentation](https://modelcontextprotocol.io/docs/getting-started/intro) +- [Introducing the Model Context Protocol](https://www.anthropic.com/news/model-context-protocol) : the original announcement, which frames well the problem MCP solves +- [Effective context engineering for AI agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) : the reference article on context engineering, including the phenomenon of context window saturation - *Observability Engineering* (O'Reilly, 2nd edition), Charity Majors, Liz Fong-Jones, George Miranda: the field's reference book, with a chapter dedicated to the rise of agents and LLMs ### My repo