FROM CHAT TO DELEGATION: CODEX DATA SHOWS AGENTS ARE BECOMING WORKFLOWS, NOT ANSWERS
OpenAI’s Codex data shows engineers are delegating multi-step work to agents, not chatting for answers. In [The Shift to Agentic AI: Evidence from Codex](https...
OpenAI’s Codex data shows engineers are delegating multi-step work to agents, not chatting for answers.
In The Shift to Agentic AI: Evidence from Codex, usage shifts from Q&A to longer runs, concurrency, workflow reuse, and saved skills. This explainer frames it as delegation, not conversation.
Practice is catching up: handoffs matter more than chats and loops need guardrails. See Open Engine, a clear loop primer from Daily Dose of DS, and community asks for a shared ChatGPT–Codex workspace plus automatic topic-based context retrieval.
KPIs move from message counts to run outcomes, reuse, and safe delegation.
Platform choices now affect handoffs, shared context, and recovery from partial failures.
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terminal
Add run-level telemetry for agents: run_id, start/stop, tool steps, artifacts, and review checkpoints; compare against prior chat-based metrics.
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Prototype an Open Engine–style handoff record between two agents and run it in CI to measure failure modes and reuse.
Legacy codebase integration strategies...
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Retrofit job runners/queues to supervise long-running agent runs with timeouts, budgets, and resumable checkpoints.
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Centralize agent outputs and saved skills in versioned storage for reuse and audits across teams.
Fresh architecture paradigms...
- 01.
Design agents as first-class services with clear interfaces, idempotent steps, and explicit context assembly.
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Pick an orchestrator that supports concurrency controls, pause/resume, and human-in-the-loop review.
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