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Why can I only learn which coordination failure hit my agent pipeline after it already did?

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기회

The MAST taxonomy, validated on over 1,600 production agent execution traces at NeurIPS 2025, names 14 coordination failure modes that account for 79% of multi-agent system failures. Knowing the taxonomy after a failure is useful. Knowing which failure mode a specific architecture will hit before deployment does not yet exist as a tool or discipline. Current evaluation practice runs component-level tests on individual agents but has no method to characterize the emergent failure profile of the whole system under realistic coordination load. Practitioners face a consistent 37% gap between lab performance and production reliability with no diagnostic that predicts where it will open.

μ™œ μ€‘μš”ν•œκ°€

Predictive coordination failure profiling is what turns a post-mortem taxonomy into a pre-deployment design check, which is the actual lever for cutting the production gap.

기회 평가 방식

기회 μ μˆ˜λŠ” 츑정값이 μ•„λ‹Œ 제 주관적 ν‰κ°€μž…λ‹ˆλ‹€. μ–Όλ§ˆλ‚˜ λΆˆνŽΈν•œμ§€, μ–Όλ§ˆλ‚˜ 자주 λ°œμƒν•˜λŠ”μ§€, ν˜„μž¬ 해결책이 μ–Όλ§ˆλ‚˜ λΆ€μ‘±ν•œμ§€λ₯Ό λ°˜μ˜ν•©λ‹ˆλ‹€. μ μˆ˜κ°€ λ†’μ„μˆ˜λ‘ λ§Œλ“€ κ°€μΉ˜κ°€ 더 λ†’λ‹€κ³  μƒκ°ν•©λ‹ˆλ‹€.

심각도8/10

λ°œμƒν–ˆμ„ λ•Œ μ–Όλ§ˆλ‚˜ 큰 λΆˆνŽΈμ„ μ΄ˆλž˜ν•˜λŠ”μ§€.

λΉˆλ„8/10

μ‹€μ œλ‘œ μ–Όλ§ˆλ‚˜ 자주 μ ‘ν•˜κ²Œ λ˜λŠ”μ§€.

곡백 μ˜μ—­9/10

ν˜„μž¬ 이λ₯Ό ν•΄κ²°ν•  λ§Œν•œ 도ꡬ가 μ–Όλ§ˆλ‚˜ λΆ€μ‘±ν•œμ§€.

ν•΄κ²°ν•  κ°€μΉ˜ μžˆλŠ” 더 λ§Žμ€ λ¬Έμ œλ“€