Agentic AI in DevOps
Insights on applying agentic AI to DevOps and software development automation, by practitioners who build and operate agentic AI for DevOps at Sirob Technologies — the team behind B.O.R.I.S, an AI DevOps teammate.
Episodes
#22 — From Agent Traces to Better Evals with Nikita Kabardin
An agent can pass its evals and still disappoint customers. Production traces help only when someone turns real successes and corrected failures into tests worth trusting.Andrey Devyatkin, Vladimir Samoylov and Fernando Piviani join Langfuse engineer Nikita Kabardin to discuss golden datasets, human review of evals, reliable pull request workflows, context for autonomous agents, and permission bou
#21 — 8 DevOps Jobs to Try With Decision Models
An alert needs triage, a ticket needs a duplicate check, and a frontier LLM is generating answers for both. Decision models could make those routine checks cheaper, provided the thresholds and gates stay in code.Andrey Devyatkin, Vladimir Samoylov and Fernando Gonçalves explore Jev, eight DevOps use cases, agent tool hooks, probability thresholds and local alternatives, including where classifiers
#20 — Become an Agentic Engineer with Kaido Koort
Claude Code can produce more code than an engineer can comfortably review. Trusting that output takes a repeatable workflow, protected learning time and checks that catch a solution to the wrong problem.Andrey Devyatkin and Fernando Gonçalves join Kaido Koort to explore agentic engineering training, agent-led specs, validator sub-agents, session handoffs, verification versus validation, and what C
#19 — AI Doom Can Wait. Your Security Alerts Can’t
Security alerts keep piling up while AI doom dominates the headlines. LLMs can help sort the queue, but useful triage depends on evidence, limited access and spending controls.Andrey Devyatkin and Fernando Gonçalves discuss GuardDuty findings, live AWS policies, read-only permission traps and tool guards, explaining why assisted investigation and batch analysis should come before unattended agents
#18 — AI Writes Code. What's Your Job? with Julien Bisconti
AI writes the code, yet engineers end the day buried in reviews and unsure what shipped.Andrey Devyatkin, Vladimir Samoylov and Fernando Gonçalves talk with Julien Bisconti about reference implementations, review stopping rules, changing harnesses, token spend and the case for combining cloud expertise with industry knowledge.What you will learn:How reusable reference projects make complex agent i
#17 — DevOps Agents Need Context, Not More Tools
Your agent can call every tool you give it and still not know which account a service runs in, what depends on it, or what changed last week. So every session starts with the same discovery work.Andrey Devyatkin, Vladimir Samoylov and Fernando Gonçalves get into Grok 4.6 in Cursor, Anthropic's Claude Tag, cloud code review and AI-driven bug bounty noise, and why all four land on the same gap.W
#16 — How DevOps Makes AI Safe to Scale
AI adoption stalls when licenses and quotas replace trust, context, and verification. Fernando Gonçalves and Vladimir Samoylov explain how DevOps makes agentic coding safe to scale through checkable starter tasks, organization-specific context, independent tests, Terraform plans, and deterministic gates—while exposing why token metrics, AI-written tests, and advisory Markdown can create false conf
#15 — Can Origin and Entire Replace GitHub?
GitHub outages and agent-scale workloads are exposing the limits of centralized development platforms—learn where Cursor Origin and Entire could help, and which bottlenecks they cannot fix. Andrey Devyatkin, Vladimir Samoylov, and Fernando Gonçalves examine AI-native Git forges, distributed mirrors, API and Actions limits, repository sovereignty, and whether Git is even the real constraint. They w
#14 — Loop Engineering in DevOps
Loop engineering solves the coding-agent babysitting problem by giving DevOps teams a way to run larger tasks with evidence, constraints, and a clear definition of done. Andrey Devyatkin, Vladimir Samoylov, and Fernando Gonçalves unpack outer loops around agents, context-window limits, unattended runs, greenfield versus brownfield work, alert batching with SNS and SQS, model mixing, token costs, a
#13 — cmux vs iTerm with Viktor Vedmich
cmux terminal workflows: when iTerm slows down AI-agent work, Viktor Vedmich shows how workspaces, session restore, and socket APIs make Claude Code more practical. Andrey Devyatkin and Fernando Gonçalves talk with Viktor Vedmich about his terminal-first agentic stack, Obsidian vault setup, Claude Code on Bedrock, AWS Kiro, spec-driven workflows, MCP search tools, rising token bills, and why open-
#12 — Semantic Layers, Context Layers, and Agents That Stop Guessing
Two terms keep colliding in infrastructure AI: semantic layer and context layer. They're not the same thing, and conflating them is how teams end up with agents that understand vocabulary but have no idea what's actually running — or agents that know the environment but still guess wrong about what the data means.Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai
#11 — Base of Record for Intelligent Systems
The hosts of Agentic AI in DevOps make the case that the "second brain" idea — long a personal-productivity meme — is exactly what AI agents need to be useful inside a real engineering environment. Without a system of reference, the agent burns its context window rediscovering what is running where, hallucinates the gaps, and needs credentials it should not have. The episode also marks a p
#10 — What Changed in Our Daily AI Workflow
In a less-structured episode, the hosts of Agentic AI in DevOps compare day-to-day workflows: what they actually run, what they have stopped doing, and how their habits have shifted since the early autumn. Fernando Gonçalves keeps the classical engineering ritual — linting, tests, coverage targets — and bakes it into a skill so the AI cannot skip past quiet bugs. Vladimir Samoylov has rewired the
#9 — Code with Claude: Routines, Agents, and the AWS Catch
Anthropic's Code with Claude developer conference in San Francisco on May 6, 2026 dropped a wave of platform features aimed squarely at coding teams, and the hosts walk through what actually matters for builders. Andrey Devyatkin reads the SpaceX–Anthropic compute deal as one of the cleverest business moves of the year — xAI is sitting on underutilized GPUs, and "the enemy of my enemy"
#8 — DevOps Jobs Agentic AI Can Actually Do
After seven foundation-laying episodes, the hosts of Agentic AI in DevOps take the practitioner's tour: which DevOps jobs agentic AI actually does well, and which still fight back. Andrey Devyatkin reframes the "AI deleted my production database" headlines, arguing they are functionally identical to "my terminal deleted my database" — the human gave the credentials and confirme
#7 — Agent Memory: When It Helps, When It Hurts, and How to Manage It
Your AI agent just forgot everything. Again. You taught it your stack, your conventions, your preferences — and tomorrow it wakes up like it never happened. Memory is supposed to fix that. Here's what nobody tells you: it can also make things worse. This blog post breaks down exactly how agent memory works, when it helps, when it quietly poisons your sessions, and what to do about it.Maybe try
#6 — The Big AI Squeeze
LLM subsidies are drying up, subscription limits are tightening, and the astronomical data center CapEx has to be paid by someone — spoiler: it is you. In this episode, Fernando Gonçalves, Andrey Devyatkin, and Vladimir Samoylov tackle what they call "the big squeeze": the two-sided pressure of rising AI costs and emerging local-inference technologies that could reshape how teams budget fo
#5 — Stop Your Agent Before It Breaks Prod
Imagine your agent just deleted a production database — could you have stopped it? The hosts argue that yes, three lines of bash in a single hook could have prevented it, and yet most teams have never configured one. In this episode, Andrey Devyatkin, Vladimir Samoylov, and Fernando Gonçalves pull apart the agentic loop — the repeating cycle of reason, act, observe that makes coding agents appear
#4 — Harness Engineering: What Claude Code Accidentally Taught Everyone
A packaging mistake exposed Claude Code's full source tree to the world — and instead of scandal, the community got a masterclass in how agentic coding tools actually work under the hood. In this episode, Andrey Devyatkin, Vladimir Samoylov, and Fernando Gonçalves unpack what the disclosure revealed about the engineering behind coding agents, introduce the emerging discipline of "harness e
#3 — Skills, Powers, SOPs
What happens when your AI coding tool quietly starts billing like a cloud service — and your team burns through a thousand dollars in a week? Vladimir Samoylov returns as the hosts share their sticker-shock moment with Cursor's new pricing before diving into agent skills. From Claude Code skills to Kiro Powers to AWS Strands SOPs, the naming varies but the idea is the same — plugging structure
#2 — The Tool Layer: What Makes Agentic AI Possible
What happens when your AI coding assistant forgets what it was just working on? Andrey Devyatkin and Fernando Gonçalves break down context windows, how MCP servers can consume a large share of the session before the first message, and why over-specifying agent behavior in project rules often hurts output.Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.aiEpisode page, sho
#1 — AI in DevOps, 2022 to 2026: From Autocomplete to Action
What if most AI tools sold into DevOps fail because they only see part of the stack? In this first episode of Agentic AI in DevOps, Andrey Devyatkin and Fernando Gonçalves trace AI tooling from ChatGPT's launch in late 2022 through 2025: they treat context—not model hype—as the constraint, and explain why assistants cut off from source, logs, metrics, and docs tend to guess wrong under real op
