OpenAI's Codex developer experience lead sits down with a former comedian turned ML engineering lead for a conversation about what happens when computer use agents stop being a novelty and start actually running your day. The conversation moves through building an AI-powered morning brief that reads every email, Slack message, and tweet before you've even opened your laptop, letting pinned threads check in on themselves every 30 minutes, and a skills system built to mirror how a person actually writes and reviews code. There's a close look at the guardrails and permission layers that keep an autonomous agent from pushing to the wrong repo or replying to the wrong tweet, how a codebase merging thousands of pull requests a day survives thanks to self-healing review before anything hits CI, and the idea of AI deference - when an agent should push through a task alone versus stop and ask for help. The back half gets personal: why developing taste and vocabulary now matters more than working harder, what it actually takes to delegate to an agent the way you'd onboard a new employee, and why this might be the year voice-orchestrated computer use finally makes everyone feel like they're talking to Jarvis.

A deep dive into the "design-by-intuition" tax in AI engineering, comparing convergent and divergent agent loops. It introduces agent-evolutions, a genetic search framework that spawns variants in parallel, evaluates them against frozen binary gates and numeric rubrics, and relies on a robust file-based sub-agent contract rather than chat parsing.

Many coding agents fail because they improvise internal planning loops, leading to unchecked code or runaway sessions. This article details how externalizing a structured Plan-Do-Check-Act (PDCA) engineering loop into a disk-based workflow acts as a leverage multiplier. By forcing agents through an explicit, step-by-step verification process, developers can reliably guide agents to finish what they start with minimal human intervention.

In this episode, we're joined by Josh Collier, FinOps Lead at Superhuman (formerly Grammarly), to explore what it really costs to run AI at scale and why the rules of the game changed faster than anyone expected.

Rosie is iFood's AI support agent, serving millions of customers across Brazil. Rather than trying to eliminate latency, the team designed the system around it. In this conversation, César Gomes explains how iFood uses asynchronous workflows, specialized agents, rollback strategies, delayed memory consolidation, and a reviser agent to keep conversations responsive while reducing costly hallucinations. The result is a production-ready architecture focused on one outcome: shortening the path from customer problem to resolution.

Manish Dasaur is a Managing Director at PwC with over 20 years in data and AI, having helped 100+ clients navigate AI disruption and extract real business value from data, AI, and agentic AI initiatives. In this episode, he breaks down why most enterprise AI programs stall — and the playbook the winners are using instead.

Loop engineering is the shift from manually prompting AI agents turn-by-turn to designing autonomous systems that do the prompting for you. Instead of writing a prompt, reading output, then writing the next prompt, you build a small control system that discovers work, delegates tasks to agents (and sub-agents), verifies results, persists state, and decides what to do next on a schedule or until a goal is met. It sits one level above agent harness engineering: where a harness equips a single agent run, a loop keeps firing agents on a cadence, spawning helpers, and feeding itself. The framing is increasingly tool-agnostic, as both Claude Code and OpenAI Codex have converged on similar primitives. The people building at the frontier are already living it: Boris Cherny, head of Claude Code at Anthropic, has said his job is no longer writing prompts but writing the loops that do.

Letting coding agents search the web for documentation wastes tokens, increases latency, and risks importing outdated or incorrect API patterns. To resolve this, this article presents `agent-docs`, a system of local references stored directly within the repository. By instructing agents to search local Markdown documentation maps before attempting web queries, developers can ensure that agents always build on correct, version-aligned instructions.