Speeding up agentic workflows with WebSockets in the Responses API
OpenAI published a technical deep dive into the Codex agent loop, detailing how WebSockets and connection-scoped caching were used to reduce API overhead and improve model latency. The post focuses on infrastructure optimizations within the Responses API for agentic workflows. These changes are relevant to developers building multi-step agent pipelines that rely on repeated API calls.
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Unrolling the Codex Agent Loop
OpenAI published a technical deep dive into the Codex CLI agent loop, detailing how it orchestrates models, tools, and prompts via the Responses API. The post explains the internal architecture of the agentic coding system, including how the loop manages state, tool calls, and performance. This provides concrete implementation detail on how OpenAI structures production agent workflows on top of its API primitives.
From model to agent: Equipping the Responses API with a computer environment
OpenAI describes how it built an agent runtime by combining the Responses API with a shell tool and hosted containers, enabling agents to operate with persistent files, tools, and state. The architecture supports secure, scalable execution of agentic workflows. This represents a concrete infrastructure layer for deploying agents in production environments.
Unlocking the Codex Harness: How OpenAI Built the App Server
OpenAI published a technical deep-dive on the Codex App Server, a bidirectional JSON-RPC API designed to embed the Codex coding agent into external applications. The server supports streaming progress updates, tool use, human-in-the-loop approvals, and diff outputs. The post explains the architectural choices enabling developers to integrate Codex agent capabilities programmatically.
Harness Engineering: Leveraging Codex in an Agent-First World
OpenAI published a technical post by Ryan Lopopolo describing how Codex is being used in an agent-first engineering workflow. The piece appears to cover practical patterns for integrating Codex into software development pipelines where AI agents take a more central role. As a Tier 1 source announcement, it likely details real-world engineering practices and lessons from deploying Codex at scale.
Introducing upgrades to Codex
OpenAI has announced upgrades to Codex, its AI coding agent, improving speed, reliability, and real-time collaboration capabilities. The updates extend Codex's reach across multiple development environments including terminal, IDE, web, and mobile. The announcement emphasizes both interactive collaboration and autonomous task execution.
Wasmer used OpenAI Codex with GPT-5.5 to build a Node.js edge runtime 10-20x faster
Wasmer used OpenAI's Codex powered by GPT-5.5 to build a Node.js runtime for edge computing, reporting 10x to 20x development acceleration and shipping in weeks instead of months. The case study is published on the OpenAI blog as a deployment showcase. It provides concrete evidence of agentic coding tools compressing development timelines for systems-level infrastructure work.
Running Codex Safely at OpenAI
OpenAI published a blog post describing the security architecture used to run Codex as a coding agent internally, covering sandboxing, human approval workflows, network policies, and agent-native telemetry. The post is aimed at supporting enterprise adoption of coding agents by demonstrating safe and compliant deployment patterns. It provides operational detail on how OpenAI itself governs agentic code execution in production.
The Age of Async Agents — Cognition's Walden Yan & OpenInspect's Cole Murray
A Latent Space podcast episode featuring Cognition's Walden Yan and OpenInspect's Cole Murray discussing the current state of autonomous software engineering agents. Topics include Devin's reported 80% commit rate, spec-to-PR workflows, full VM environments for agents, agent memory, and the emerging pattern of product managers shipping code directly. The conversation covers practical deployment patterns and tooling for async agentic coding workflows.