The Strongest Agent Control Is Reversibility
Approval protects the moment of action. Reversibility protects what happens after. Classify agent work by whether you can undo it, and review earns a real target.
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Articles about reliability and its practical role in production agent work.
How Punk Governs the OpenAI Responses API for Coding Agents
Coding agents need more than text streaming. They need a runtime that preserves typed tools, reasoning summaries, terminal states, and evidence end to end.
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Replay Is Not Shadow: How Adaptive Agent Runtimes Earn Trust
Historical agreement, current agreement, and authority to serve are separate states. A trustworthy runtime refuses to collapse them.
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How Successful AI Agent Work Becomes Reusable
The real value of a good agent run is not the answer it produced once. It is the chance to make similar work more reliable the next time.
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How to Roll Out an AI Agent by Observing First
The safest useful pilot is not a sandbox nobody depends on. It is real work observed carefully, with the live path preserved and success defined in advance.
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When an AI Agent Should Keep Using the Expensive Model
The most trustworthy agent runtime is not the one that avoids the expensive model most often. It is the one that knows when the work still deserves it.
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Why AI Agent Cost Is a Workflow Problem
A cheaper model cannot rescue an expensive operating pattern. Start by finding where the agent repeats, retries, waits, and recomputes.
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Why Every AI Agent Shortcut Needs an Explanation
If a runtime cannot tell you why it took the cheaper path, the savings number is not a reason to trust it.
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