Lessons Learned
Anonymized, pattern-based reflections from running long-lived agents and tool chains. These are not how-tos. They are the postmortems: what went wrong, what the operator did about it, and what to keep in your back pocket for the next time it happens. Read these when you want to avoid a mistake someone else already paid for — or when you want to know which mistakes are inevitable and which are avoidable.
Lessons Learned
Anonymized, pattern-based reflections from running long-lived agents and tool chains. These are not how-tos. They are the postmortems: what went wrong, what the operator did about it, and what to keep in your back pocket for the next time it happens. Read these when you want to avoid a mistake someone else already paid for — or when you want to know which mistakes are inevitable and which are avoidable.
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Desk Architecture Failure Modes: What Breaks First and How to Fix It
After running desk-based architectures across dozens of agent systems, the same failure patterns show up in the same order: context bleeds, memory饱和, handoff drops, and authority drift. Here is what each one looks like in practice, why it happens, and how to detect it before it causes real damage.
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Desk Architecture Failure Modes: The Six Ways Operators Get It Wrong
Adding desks does not automatically make a system better. It can make it worse — slower, more confused, and harder to debug. This lesson catalogs the six failure modes operators hit when they move from one generalist agent to a desk-based architecture, with the diagnostic signals and the fix for each.
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Where AI Helps Blender, and Where It Should Stop
AI helps with the tedious parts of 3D — base meshes, texture cleanup, denoising, rig auto-weighting. It does not help with the parts that need judgment. The line between is worth drawing clearly.
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Lessons Learned: Memory Design Mistakes in Early Agent Work
Three memory mistakes show up in nearly every first attempt at long-running agents: writing too much, writing too little, and writing in the wrong place. Each looks like a different kind of failure, but the underlying cause is the same — the agent is writing notes without writing knowledge. The fixes are architectural, not behavioral.
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Lessons Learned: Why Agents Stall Without Clear Termination
Termination is the most under-engineered part of the agent loop. Most agents stop on the wrong signal — or no signal at all — because the operator treated stop conditions as something the model would figure out. This piece walks through the four failure modes, the four parts of a testable stop condition, and the concrete patterns that make termination reliable.
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Lessons Learned: Why Context Explodes Without Bounding
The most common silent failure in long-running agents is unbounded context growth. The runtime decides to keep accumulating context without bound, and the model — responding to the unbounded transcript — eventually degrades. The fix is at the runtime layer: cap tool result sizes, cap retry counts, summarize resolved turns, and offload old context to long-term memory at intervals.
LESSONS