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OpenAI's Codex harness — a full observability stack wired into the agent so it can query, correlate, and reason about its own output (source: OpenAI)
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Agentic workflows are driven by “supervising” agents and operated by “worker” agents, the latter of which have specialized instructions that are optimized for specific tasks (e.g. a “SQL Generation Agent” or “Request App Agent”). The tradeoff here is the complexity of state management: how do you store and route the necessary context between agents without it leaking into other workers, potentially leading to bad/noisy output?