Agentic AI in DevOps

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

Agentic AI in DevOps is a technology podcast hosted by Sirob Technologies, with 15 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Sirob Technologies hosts Agentic AI in DevOps, a technology show with 15 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #16

  #16 — How DevOps Makes AI Safe to Scale

  Aug 13, 2026—

  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. Episode page (show notes and links): https://getboris.ai/insights/016-how-devops-makes-ai-safe-to-scale/

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  Episode #15

  #15 — Can Origin and Entire Replace GitHub?

  Jul 17, 2026—

  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/

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  Episode #14

  #14 — Loop Engineering in DevOps

  Jul 8, 2026—

  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/

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  Episode #13

  #13 — cmux vs iTerm with Viktor Vedmich

  Jun 25, 2026—

  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/

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  Episode #12

  #12 — Semantic Layers, Context Layers, and Agents That Stop Guessing

  Jun 11, 2026—S0

  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...

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