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 AI agents. https://www.getboris.ai

  1. 2天前

    #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, Vladimir Samoylov 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 tuningWhy a CI/CD escalation finding needs live policy evidenceWhy ReadOnlyAccess can expose S3 objects and DynamoDB dataHow tool guards catch calls that IAM policies still allowWhy separate invocation limits cannot cap spending across repeated triggersMaybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links

  2. 9月11日

    #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 concreteHow a fixed review limit prevents endless rounds of findingsWhy local agent transcripts can become an overlooked store of credentialsWhy token spend alone cannot establish an engineer's productivityHow business knowledge helps engineers challenge the wrong technical requestMaybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links

  3. 8月31日

    #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 orientationWhat a CI reviewer misses when Terraform sits in another repoHow live configuration turns bug bounty noise into provable findingsWhy infrastructure state belongs in a database, not a skills fileWhat it costs to keep your context inside one vendor's memoryMaybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links

  4. 8月14日

    #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

  5. 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. Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links

  6. 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/

  7. 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/

  8. 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/

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关于

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

信息

  • 创作者
    Sirob Technologies
  • 活跃年份
    2026年
  • 单集
    19
  • 分级
    儿童适宜
  • 版权
    © 2026 Sirob Technologies OÜ
  • 节目网站
    Agentic AI in DevOps

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