Insights on DevOps automation,
trust, and infrastructure AI
Practical thinking for teams building and running real systems
This is where BORIS shares what we learn from building context-aware DevOps AI.
No hype cycles. No vendor fluff. Just thoughtful perspectives on automation, reliability, and knowledge preservation.
#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.
If you work in data, "semantic layer" means one precise thing: the place where "active customer" or "recurring revenue" gets defined once so every dashboard agrees. If you build agents, the word starts doing two jobs at once — and a second term, "context layer," shows up claiming the same ground. This episode of Agentic AI in DevOps picks up directly where episode #11 left off, with Andrey Devyatkin, Vladimir Samoylov, and Fernando Gonçalves untangling the two. The short version: a semantic layer tells an agent what your data means; a context layer tells it what's actually running, where it lives, and what's connected to what. The hosts argue you usually want both — and along the way they get blunt about vendors who promise you can fire your analytics team, about agent memory that "comes stale" and quietly biases every session, and about a Kent Beck line that sums up the whole moment: typing got faster, thinking did not.
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 — When Agent Memory Helps and When It Hurts
Why persistent memory can make agents smarter, stranger, and easier to mislead
5 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 an AI DevOps teammate 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