Agentic AI in DevOps

Sirob Technologies

Insights on applying agentic AI to DevOps and software development automation, by practitioners at Sirob Technologies — the team behind B.O.R.I.S, the context layer for AI agents. https://www.getboris.ai

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25 episodes
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English

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#25
Saturday · length unknown
#25 — 5 Building Blocks for Agents That Run on Their Own
Deploy checks, failing pipelines and overnight alerts keep arriving after the laptop closes. Giving those jobs to an unattended agent takes shared context, visibility into its actions and boundaries it cannot ignore. Andrey Devyatkin and Fernando Piviani unpack five building blocks for autonomous DevOps agents, covering live-data tools, tracing, existing harnesses, GitHub Actions and AWS runtimes, plus the sandbox and network controls that contain each run. What you will learn: Which repetitive engineering jobs offer a clear first trigger How shared context and live reads serve different purposes What to record before an agent's first unattended run How triggers, run length and network placement shape runtime choices Why prompts cannot enforce credential and network boundaries Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#24
October 23 · length unknown
#24 — Applying AI Agents to Infra Security with Mohit Bansal
A malicious package lands, and production scans only answer part of the question. Developer laptops may already hold it, while security teams spend hours finding out where they're exposed. Andrey Devyatkin and Vladimir Samoylov join guest Mohit Bansal to explore Webflow's reduction of supply chain triage from hours to minutes, endpoint inventories, MCP credential risks, repository maturity gates and AWS account boundaries for AI agents. What you will learn: How Webflow reduced supply chain triage from hours to minutes Why developer laptops belong in every malicious-package investigation Where MCP configs and agent transcripts leave credentials exposed How repository graduation gates focus remediation on production Why patching a base image still requires rebuilding and redeploying Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#23
October 16 · length unknown
#23 — Git Hosting Has a Scale Problem. Do You?
Agents can produce pull requests faster than teams can review them, even while Git stays fast. Hosting platforms face real scaling pressure, but most teams hit other limits first. Andrey Devyatkin, Vladimir Samoylov and Fernando Piviani examine GitHub, GitLab and Cursor Origin's storage changes, Anthropic's Scion, file APIs that avoid unnecessary clones, and Entire's transcript checkpoints as a way to preserve intent without overlooking secrets and access controls. What you will learn: Why Git hosting scale problems rarely require ordinary teams to migrate Which agent lookup jobs can use file APIs instead of clones Why short-lived credentials and separate transcript access boundaries matter How intent blocks help reviewers assess agent pull requests Why faster pushes do not eliminate coordination around a shared ref Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#22
October 9 · length unknown
#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 boundaries between customer data and public fixes. What you will learn: Build a golden dataset from successful traces and corrected failures Recognize when an agent improves its scores by changing the grading Move pull request state transitions from prompts into deterministic code Understand why unattended agents need context beyond local Markdown files Keep private customer traces out of public development workflows Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#21
October 2 · length unknown
#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 fall short. What you will learn: How decision models differ from LLMs with structured output Why uncertain probabilities belong in a review band around 0.5 How pre-tool and post-tool hooks can filter expensive agent work Where alert triage, duplicate detection and GraphRAG could benefit Why classifier checks cannot replace permissions or deployment policy Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#20
September 25 · length unknown
#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 Claude certifications actually demonstrate. What you will learn: How six Fridays progress from brownfield orientation to parallel agents How competing validator sub-agents expose trade-offs before implementation starts How transcript-based wrap-ups turn forgotten decisions into actionable issues Why verification and validation need separate gates for agent output How autonomy, parallelism and skill usage reveal different learning gaps Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#19
September 18 · length unknown
#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. What you will learn: How batching a week of alerts reveals rules worth tuning Why a CI/CD escalation finding needs live policy evidence Why ReadOnlyAccess can expose S3 objects and DynamoDB data How tool guards catch calls that IAM policies still allow Why separate invocation limits cannot cap spending across repeated triggers Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#18
September 11 · length unknown
#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 instructions concrete How a fixed review limit prevents endless rounds of findings Why local agent transcripts can become an overlooked store of credentials Why token spend alone cannot establish an engineer's productivity How business knowledge helps engineers challenge the wrong technical request Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#17
August 31 · length unknown
#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. What you will learn: Why AWS Agent Toolkit gives an agent access, but not orientation What a CI reviewer misses when Terraform sits in another repo How live configuration turns bug bounty noise into provable findings Why infrastructure state belongs in a database, not a skills file What it costs to keep your context inside one vendor's memory Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#16
August 14 · length unknown
#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 confidence. Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#15
July 18 · length unknown
#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 weigh agent-scale throughput claims against human review, CI, internet bandwidth, and the token cost of best-of-N agent fleets, asking whether teams need to leave GitHub or simply reduce their dependence on it. Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#14
July 9 · length unknown
#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, and why bad code still gets worse when you automate it faster. Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#13
June 26 · length unknown
#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-weight model competition matters for DevOps teams. Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#12
June 12 · length unknown
#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 Episode page, show notes and links
#11
June 5 · length unknown
#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 pivot: B.O.R.I.S is no longer pitched as a replacement DevOps engineer, but as a context layer for engineering systems — and the acronym now stands for Base of Record for Intelligent Systems. Fernando Gonçalves rejects pure RAG as enough on its own (the relationships are what matter), Andrey Devyatkin turns "do you take notes?" into a hiring question, and the half-hour closes with a token-maxing post-mortem and the reminder that you can outsource the legwork to agents but not the decisions. Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#10
May 31 · length unknown
#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 harness with hooks for audit logs, command-failure journals, and a daily "dead code" sweep, and tracks a new personal metric: average working hours without a human. Andrey Devyatkin argues that the second shift at 9 PM is the wrong time to drive an agent — the sharpest decisions belong to a fresh 5 AM mind — and shells Claude Code out to Codex to cross-review its own plans. The closing exchange wanders into whether AI is "the last thing humans will invent," and the hosts disagree: imagination, accidents, and champagne all live outside the training data. Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#9
May 15 · length unknown
#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" gets to raise everyone's usage limits. The episode dissects routines, outcomes/goals, multi-agent orchestration, the advisor pattern, dreaming, and the new Anthropic-on-AWS path that is *not* Bedrock, with Fernando Gonçalves flagging an easy-to-miss compliance gotcha: on that path, data still leaves AWS for Anthropic. The closing read on OpenAI is sharp — a two-month free Codex trial for new Codex users on eligible enterprise accounts, which the hosts call "a little bit of a desperate attempt" after Anthropic's enterprise adoption crossed OpenAI's. Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#8
May 8 · length unknown
#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 confirmed the action — and walks through why infrastructure-as-code is harder for agents than application code (one word: state). The hosts dig into the gap between C-suite adoption claims and practitioner reality, with Fernando Gonçalves noting that "use AI" is now a manager KPI, and they land on documentation, runbooks, and postmortems as the place where agents quietly earn their keep right now. Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#7
April 29 · length unknown
#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 B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links
#6
April 22 · length unknown
#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 for and deploy AI. Along the way, they debate whether buying a Mac Mini is a rational investment or just hype, reveal the mental gymnastics required to get meaningful work out of a twenty-dollar subscription, and argue that if your AI vendor has no pricing page, that tells you everything you need to know about their business model. Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links

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