Update documentation for clarity and consistency

Remove emojis and em dash separators
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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 what I was actually doing: 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.
 
 <!--more-->
 
@@ -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

content/blog/aiops-observabilite-reseau-ia/index.fr.md

@@ -11,7 +11,7 @@ tags:
   - AI Agents
 ---
 
-Je viens de terminer une stack d'observabilité complète sur mon lab EVPN/VXLAN : topologie, télémétrie et visualisation en temps quasi réel. En la construisant, j'ai compris que je ne faisais pas juste du monitoring — je préparais un réseau à être un jour « vu » par une IA. Ce premier article pose le cadre d'une série de cinq : pourquoi l'observabilité est le préalable obligé avant de brancher un agent sur son infrastructure.
+Je viens de terminer une stack d'observabilité complète sur mon lab EVPN/VXLAN : topologie, télémétrie et visualisation en temps quasi réel. En la construisant, j'ai compris ce que je faisais réellement : je préparais un réseau à être un jour « vu » par une IA. Ce premier article pose le cadre d'une série de cinq : pourquoi l'observabilité est le préalable obligé avant de brancher un agent sur son infrastructure.
 
 <!--more-->
 
@@ -26,7 +26,7 @@ Le résultat est une stack qui combine quatre briques :
 - une **base de séries temporelles** pour le stockage des métriques ;
 - une **couche de visualisation** régénérée automatiquement en croisant la topologie et les métriques.
 
-C'est fonctionnel aujourd'hui. Mais ce n'est pas la stack elle-même qui m'a fait écrire cet article : c'est une prise de conscience en la construisant. Je ne posais pas seulement de la supervision : je posais les fondations d'un pipeline qui, un jour, pourrait permettre à un agent IA de raisonner sur mon réseau. Et ça, ça a un nom.
+C'est fonctionnel aujourd'hui. Mais ce n'est pas la stack elle-même qui m'a fait écrire cet article : c'est une prise de conscience en la construisant. Je ne posais pas seulement de la supervision, je posais les fondations d'un pipeline qui, un jour, pourrait permettre à un agent IA de raisonner sur mon réseau. Et ça, ça a un nom.
 
 ## AIOps, pas MLOps