Agentic AI in DevOps, without the hype
Weekly perspectives from engineers building and running AI on real infrastructure.
#17 — DevOps Agents Need Context, Not More Tools
Infrastructure context turns blind agents into evidence-backed reviewers and security triage partners.
DevOps agents can call every tool and still miss what is running in production; a portable context layer gives reviews, security triage, and automation evidence they can trust. Andrey Devyatkin, Vladimir Samoylov, and Fernando Gonçalves connect Grok 4.6 in Cursor, Claude Tag, cloud code review, and AI-driven bug bounty noise to the same orientation tax. They explain why live infrastructure state belongs in an automatically refreshed database rather than skills or vendor memory, and how owning that context improves accuracy, cost control, and portability across agent platforms.
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#16 — How DevOps Makes AI Safe to Scale
AI adoption needs trusted context and hard gates—not licenses, quotas, or coverage theater.
25 min read
#15 — Can Origin and Entire Replace GitHub?
GitHub at agent scale: distributed mirrors may help, but APIs, CI, and human review remain the real bottlenecks.
13 min read
#14 — Loop Engineering in DevOps
Loop engineering turns coding agents from supervised helpers into bounded DevOps workers
12 min read
#13 — cmux vs iTerm with Viktor Vedmich
cmux workflows for faster Claude Code sessions, Kiro tradeoffs, and AI token cost reality
14 min read
#12 — Semantic Layers, Context Layers, and Agents That Stop Guessing
Why agents need live system context and shared meaning before they can act with confidence.
13 min read
#11 — Base of Record for Intelligent Systems
Why AI agents need a second brain before they can safely understand engineering systems.
10 min read
#10 — What Changed in Our Daily AI Workflow
Three engineers compare the habits, guardrails, and timing shifts reshaping everyday AI work
9 min read
#9 — Code with Claude: Routines, Agents, and the AWS Catch
Anthropic’s coding push gets real, but its new AWS route hides a compliance trap.
9 min read
#8 — DevOps Jobs Agentic AI Can Actually Do
Where agentic AI helps DevOps today, and where state, cost, and production risk still block it.
8 min read
#7 — Agent Memory: When It Helps, When It Hurts, and How to Manage It
Persistent memory can make AI agents smarter — or poison every session. Here's how the memory lifecycle works and when to skip it entirely.
8 min read
#6 — The Big AI Squeeze
As AI discounts fade, teams face the real economics of subscriptions, local models, and sustainable automation.
12 min read
#5 — Stop Your Agent Before It Breaks Prod
How hooks turn unpredictable coding agents into controlled, auditable workflows
11 min read
#4 — Harness Engineering: What Claude Code Accidentally Taught Everyone
Why the tool layer, not the model alone, decides whether coding agents deliver or derail
7 min read
From Frustration to Product: The Story of B.O.R.I.S
How a broken experience with AI CLI tools led us to build a context layer that actually knows your infrastructure.
5 min read
#3 — Skills, Powers, SOPs
Agent skills sound like magic until someone uploads malware to the public hub.
7 min read
#2 — The Tool Layer: What Makes Agentic AI Possible
Context windows, MCP overhead, and why micromanaging your AI agent makes it worse.
7 min read
#1 — AI in DevOps, 2022 to 2026: From Autocomplete to Action
How AI went from clever autocomplete to agents that can act on your infrastructure — and why context is the missing piece.
7 min read