#10 — What Changed in Our Daily AI Workflow

#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. Gonçalves keeps the classical engineering ritual — linting, tests, coverage targets — and bakes it into a skill so the AI cannot skip past quiet bugs. 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. 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/

#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. 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 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 — 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. 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 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/

#7 — When Agent Memory Helps and When It Hurts

Every session, your agent wakes up with amnesia — the same mistakes, the same rediscovery, the same wasted tokens. Memory is how teams solve this, but as the hosts of Agentic AI in DevOps argue, it is both a superpower and a liability. Andrey goes so far as to say that semantic memory makes an already non-deterministic LLM "even less deterministic," while Fernando warns that a poisoned memory file is trusted implicitly — just like a human trusts their own recollections. In this final episode of the foundations series, the hosts unpack the full memory lifecycle — capture, management, retrieval — and share hard-won lessons from building B.O.R.I.S, their agentic DevOps teammate where memory plays a central role. Episode page (show notes and links): https://getboris.ai/insights/007-when-agent-memory-helps-and-when-it-hurts/

#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, Andrey, and Vladimir 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. Episode page (show notes and links): https://getboris.ai/insights/006-the-big-ai-squeeze/