The Emergence of Agentic AI in Network Observability: Architectural Patterns and Integration Strategies
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Authored by: Marc Buraczynski Publication Date: 2026-06-09
Blog posts
2026
Advancing Network Observability with Custom-Developed Machine Learning Models
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15 min read
Network Observability with GCN-LSTM
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DATE: 2026-05-24
Network Modeling in Cisco ThousandEyes
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A technical deep-dive into the graph-theoretic foundations, algorithms, and data structures that power ThousandEyes’ network intelligence platform.
Agent Skills: Architecture, Implementation, and the Future of Composable AI Agent Knowledge
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A deep technical analysis of the SKILL.md specification, progressive disclosure patterns, and how agent skills fundamentally reshape LLM-based agent architectures.
Your Customers Are Telling You How They Feel — Without Saying a Word. Are You Listening?
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Imagine walking into your favourite coffee shop. Before you even reach the counter, the barista notices the tension in your face, offers a warm smile, and says, “Rough morning? How about your usual — on the house today?” That small moment of emotional intelligence keeps you coming back for years.
From Pixels to Predictions: How CNNs Crushed ANNs in the Battle for Street-Level Recognition
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How choosing the right neural network architecture took digit recognition accuracy from 65% to 91% – and what business leaders should know about it.
Observability for LLMs: Understanding the Layers
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A practical guide to monitoring, debugging, and optimizing Large Language Model applications in production – with implementation examples for OpenTelemetry, AppDynamics APM, and Splunk Observability Cloud.
The Complete Guide to Fine-Tuning Large Language Models: From Theory to Production
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A Deep Technical Dive into LoRA, QLoRA, and Full Fine-Tuning with Modern Open-Source Models
Understanding Temperature in Large Language Models: A Deep Technical Guide
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A comprehensive exploration of temperature parameter mechanics, mathematical foundations, and practical implementation strategies for ML engineers and developers.
How Do LLMs Work: From Probabilistic Foundations to Intelligent User Segmentation
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A deep dive into the mechanics of Large Language Models and their transformative applications in recommendation systems
Understanding How Recommendation Systems Work-What We Found After 150,000 User Interactions
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A deep dive into user propensities, why your Netflix recommendations aren’t as smart as you think, and what the first 10 clicks reveal about someone’s entire journey.
What Your RTO Data Isn’t Telling You—And Why It Matters for Your People
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Let’s talk about something that’s probably been nagging at you if you’re in HR: those badge swipe reports everyone’s using to justify return-to-office decisions? They might be telling you a story that isn’t quite true.