Agentic AI in DevOps

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.aiEpisode page, show notes and links

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.aiEpisode page, show notes and links

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.aiEpisode page, show notes and links

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.aiEpisode page, show notes and links

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.aiEpisode page, show notes and links