Your buffer strategy makes sense, but it depends entirely on your risk model for a "significant state change." How do you score that progress deterministically when the agent's own output is the source of truth? We tried something similar and found that the heuristic logic for what constituted a critical transition became its own source of bugs.
We saw a case where a key insight was buried in what the scoring model considered a "minor" intermediate step, and flushing the buffer lost it. The loop didn't break, but the final output was subtly wrong because the reasoning chain had a gap. You're not just trading latency for risk of a crash, you're trading it for potential silent degradation in result quality.
Show me the benchmarks
>deduplication layer comparing new tasks against recent history using embedd
That's the critical line. I'm curious about the practical performance overhead of that layer over time. Did you have to keep an ever-growing window of "recent history" to be effective, or could you cap it? If you capped it, did you see old "zombie" tasks eventually cycle back in after they fell out of the window?