responses API

The Golden Age of AI Engineering — Alexander Embiricos & Romain Huet & Peter Steinberger, OpenAI

Jul 9, 2026 · 25:13

OpenAI's Alexander Embiricos, Romain Huet, and Peter Steinberger reveal how AI engineering is entering its golden age, with model releases accelerating from every 15 months to roughly every 6 weeks and frontier-level intelligence now available at $1 per million input tokens and $6 per million output tokens. They demonstrate Codex's evolution from a model that couldn't test its own code to one generating 750 tokens per second, enabling a 10-second pull request, and emphasize that the product is built on the same open-source stack—responses API, AGENTS.md format, and harness—that ships to developers. The trio argue that the future is not about automating engineers but empowering them via value maxing over token maxing, with agents moving from local/cloud distinctions to persistent, long-running managers that orchestrate work and only require human steering at key decision points, as illustrated by an automated open-source issue resolution workflow.

OpenAI on Securing Code-Executing AI Agents — Fouad Matin (Codex, Agent Robustness)

Jul 30, 2025 · 14:00

Fouad Matin, an OpenAI engineer working on agent robustness, argues that code-executing agents like Codex CLI require layered security safeguards as they become capable of autonomously writing and running code. He emphasizes three main defenses: sandboxing agents via containerization (e.g., ChatGPT spins up fully isolated containers) or OS-level sandboxing using SeatBelt on macOS or Seccomp and Landlock on Linux; disabling or limiting internet access to prevent prompt injection and data exfiltration, with configurable allowlists for approved endpoints; and requiring human review through PR approvals and monitoring tasks to catch unintended actions like installing malicious packages. Matin also details new API tools—local shell and apply patch—that enable agents to execute code and apply diffs securely, and recommends using remote containers (available in the Agents SDK and Responses API) as the safest deployment option. He concludes by acknowledging that while LLM-based monitors are valuable, deterministic system-level controls remain essential, and invites applications to OpenAI's new Agent Robustness team.