Agentic AI in DevOps

Sirob Technologies
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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 infrastructure. https://getboris.ai

  1. 7月18日

    #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. Episode page (show notes and links): https://getboris.ai/insights/015-can-origin-and-entire-replace-github/

  2. 7月9日

    #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. Episode page (show notes and links): https://getboris.ai/insights/014-loop-engineering-in-devops/

  3. 6月26日

    #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. Episode page (show notes and links): https://getboris.ai/insights/013-cmux-vs-iterm-with-viktor-vedmich/

  4. 6月12日

    #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. Episode page (show notes and links): https://getboris.ai/insights/012-semantic-layers-context-layers-and-agents-that-stop-guessing/

  5. 6月5日

    #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. Episode page (show notes and links): https://getboris.ai/insights/011-base-of-record-for-intelligent-systems/

  6. 5月31日

    #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. Episode page (show notes and links): https://getboris.ai/insights/010-what-changed-in-our-daily-ai-workflow/

  7. 5月15日

    #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. Episode page (show notes and links): https://getboris.ai/insights/009-code-with-claude-routines-agents-and-the-aws-catch/

  8. 5月8日

    #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. Episode page (show notes and links): https://getboris.ai/insights/008-devops-jobs-agentic-ai-can-actually-do/

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簡介

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 infrastructure. https://getboris.ai

資訊

  • 創作者
    Sirob Technologies
  • 活躍年代
    2026年
  • 集數
    15
  • 年齡分級
    兒少適宜
  • 版權
    © 2026 Sirob Technologies OÜ
  • 節目網站
    Agentic AI in DevOps

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