When code gets cheaper to produce, feedback becomes the limiting factor - CI, reviews, and the handoffs between tools can quietly slow everything down.

Rob Zuber breaks down what platform engineers are seeing as teams adopt AI-assisted development: more branch builds, new failure modes, and growing pressure to shorten the loop between “change made” and “change validated.” He focuses on how CI can evolve from a human-first dashboard into a system that agents can interact with directly through APIs, CLIs, and MCP-style interfaces - so fixes can happen faster and with less waiting on manual triage.

Along the way, Rob and Cory dig into practical questions engineering leaders are wrestling with: how PR review becomes the next major bottleneck, what “agent experience” means in a delivery pipeline, why speed isn’t only about faster compute (it’s also about doing less unnecessary work), and how teams can share learnings so “agentic velocity” doesn’t only benefit a few power users.

If you’re building or running the systems that ship software, this is a clear look at where CI fits in an AI-accelerated workflow, and what needs to change to keep delivery safe, fast, and sustainable.

Guest: Rob Zuber, Chief Technology Officer at CircleCI

Rob Zuber is a 20-year veteran of software startups, a four-time founder, and three-time CTO. Since joining CircleCI, Rob has seen the company through its Series F funding and delivered on product innovation at scale while leading a team of 300+ engineers who are distributed around the globe.

CircleCI, Website

CircleCI, LinkedIn

CircleCI, GitHub

Links to interesting things from this episode:

• “The Confident Commit” podcast
• Wardley Mapping
• “How one programmer broke the internet by deleting a tiny piece of code.”

A lot of infrastructure and automation fails for ordinary reasons: rate limits, flaky networks, partial permissions, long-running jobs, and retries that vanish when the process restarts. Durable execution is a way to design systems that keep going anyway - without rebuilding a maze of queues, cron jobs, and manual cleanup.

Cornelia Davis breaks down how durable execution works in practice: writing “normal” code while the runtime provides durable retries, state management, and the ability to pause work, wait for a human or external change (like a quota increase), and resume right where things left off. The conversation connects these ideas to platform engineering realities - Terraform workflows, long provisioning times, and “orphan” resources - and explains how Temporal workflows and activities help teams model failure handling as a first-class part of the system.

You’ll also hear why this approach is showing up in AI engineering: long-running agent workflows, frequent rate limiting, and the need to avoid re-running expensive LLM calls when something breaks near the end.

Guest: Cornelia Davis, Developer Advocate at Temporal Technologies and author of “Cloud Native Patterns”

Cornelia Davis is a Developer Advocate at Temporal, where she brings more than three decades of experience as a software technologist to help engineers build resilient, scalable systems. Known for her pragmatic blend of hands-on coding, technical strategy, and customer collaboration, Cornelia is passionate about helping developers unlock the full potential of modern cloud-native architectures. Previously, she served as VP of Technology at Pivotal, where she played a key role in shaping Cloud Foundry and enabling enterprise cloud transformations. Whether she’s writing code, presenting at conferences, or whiteboarding with teams, Cornelia is driven by a singular goal: empowering developers to build better software. Outside of tech, she recharges on the yoga mat or in the kitchen, where she brings the same creativity and focus to her practice.

Temporal, Website

Temporal, GitHub

Temporal Community, GitHub

Temporal’s AI-assisted development tools

Links to interesting things from this episode:

• Temporal Developer Skill
• “Cloud Native Patterns” by Cornelia Davis

What happens when a non-deterministic AI system is asked to touch production telemetry or generate changes for an SRE pipeline? The cost of being “close enough” can be lost data, downtime, or a security incident.

Cribl’s Nikhil Mungel joins Cory to break down what it takes to build AI that sysadmins can actually trust. The conversation digs into harness engineering and the practical guardrails that turn probabilistic models into repeatable, verifiable outcomes. They cover why breaking work into small chunks matters, how validation and testing become the real leverage point for AI-native development, and what “code factories” mean for review, CI, and platform reliability when teams can generate a thousand PRs an hour.

Platform engineers will also hear a pragmatic take on the future of the job. The focus shifts away from typing code and toward building systems for verification, simulation, and safe deployment at scale, plus clearer ways to decide what needs human scrutiny and what can ship automatically.

Guest: Nikhil Mungel, Head of AI R&D at Cribl

Nikhil Mungel is the Head of AI R&D at Cribl, where he's building LLM-powered systems for IT and Security data transformation and analysis. Before Cribl, he spent over a decade developing distributed systems across the observability and consumer social tech landscape. He lives in San Francisco with his wife and two kids. His current focus is applying AI to make complex infrastructure more intuitive and explainable.

Nikhil Mungel, Website

Nikhil Mungel, X

Cribl, Website

Cribl, LinkedIn

Links to interesting things from this episode:

• Cribl Guard
• “Open source died in March. It just doesn't know it yet.” by Dan Lorenc, CEO of Chainguard