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Why long-horizon AI agents need trajectory-level governance Long-running AI agents create a governance problem that single-action approval cannot solve on its own: the risk often emerges from the sequence, intent, and cumulative effect of actions rather than from any one tool call in isolation. A file read, a shell command,…
What changed on September 9: Deep Research moved beyond ordinary Chat into Work and Codex OpenAI’s September 9 release notes state that Deep Research is now available in ChatGPT Work and Codex, expanding the feature from a research report experience in ordinary Chat into longer-running work surfaces where users may…
Opening position: treat 0.154 alpha as evidence to evaluate, not a migration instruction As of September 9, 2026, the official GitHub release feed for OpenAI Codex shows version 0.153.4 marked as Latest, while 0.154.0-alpha.7, 0.154.0-alpha.8, 0.154.0-alpha.10.2, and 0.154.0-alpha.11 appear as prereleases. The alpha entries are labeled generically and include commit…
What changed on September 8: Prompt Cache Diagnostics is now generally available OpenAI’s September 8, 2026 API changelog states that Prompt Cache Diagnostics is now generally available in the Responses API for GPT-5.6 and later supported models. The feature is designed for a narrow but operationally important task: compare cache…
Start with entitlement, not with troubleshooting folklore When a user says “I do not see Astra,” the fastest mistake is to start by reinstalling apps, clearing browser state, or buying more credits. OpenAI’s documented operating model for ChatGPT Work and Codex makes this a rollout and entitlement problem first: plan…
What changed on September 1, and why this needs an operations plan OpenAI’s September 1 product release notes and API changelog state a narrow but important connectivity change: connections to api.openai.com can now use IPv6. For teams that call the OpenAI API from application servers, CI runners, worker queues, desktop…
What This Playbook Is For: Supervised Research Operations, Not an Autonomous Lab This playbook treats Codex-style coding agents as participants in a supervised research-operations system: people choose the research portfolio, approve resource use, define task boundaries, inspect evidence, decide whether results are credible, and make the final call to scale,…
What This Tutorial Builds—and What It Deliberately Does Not Move This tutorial explains the AWS reference pattern for placing a customer-operated LiteLLM gateway between OpenAI Codex and Amazon Bedrock. The important boundary is that Codex remains the software-development agent running its task loop on the user’s workstation, while LiteLLM governs…
Start with the right mental model: the agent loop is yours A long-running Claude Code workflow is an application-owned agent loop with durable state, explicit permissions, resumable steps, bounded tools, and human checkpoints. It is not a model independently running infrastructure in the background. Claude Fable 5.1 can reason across…
Migration impact in one sentence Most Claude Fable 5 applications can start the Fable 5.1 migration by changing the model identifier to claude-fable-5-1, but direct Messages API integrations, custom agent harnesses, tool routers, and systems that persist Claude thinking blocks need a real code review because Fable 5.1 changes how…
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