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ANTHROPIC JUST EXPOSED WHY MOST MULTI AGENT CODING SYSTEMS FAIL BEFORE WRITING A SINGLE LINE OF CODE the model is not the problem the plan is one bad dependency cut gets copied across six agents then three builders produce the wrong result in parallel spec → planner → three... show more
34,491 görüntüleme • 1 ay önce •via X (Twitter)
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Plan quality is the real multiplier

agre w u

bad planning parallelizes failure

yeah

thx for ur explanations

🫡🫡

The biggest insight here isn't even the number of agents. It's the dependency graph. Give each builder a clearly defined surface, let them operate independently, keep failures contained, then bring everything together only after verification.

One bad plan breaks everything downstream

Basically supply chain optimization. I work with upstream dependencies daily. One bad plan stalls the whole line. Same principle, different stack.

A bad assumption early in the pipeline is the quiet killer. My production app tokenized with NLTK while my training pipeline lemmatized with spaCy, and that mismatch alone created more out-of-vocabulary words at inference than any downstream fix could catch.

straight facts the plan really makes or breaks everything

Right - the model isn't the problem, the plan is. Which is another way of saying the failure lives in how agents are organized and hand off, not in any single agent. Multi-agent coding breaks at the seams, not the nodes.

The success of a multi-agent coding system rests on a well-thought-out plan and effective communication among agents, not just the model itself

bro this is so real, planning is literally everything

Really interesting approach. Keeping agents isolated while feeding failures back into the spec makes a lot of sense

The dependency graph is part of the safety boundary. I’d want each parallel task to carry the assumptions it depends on, then get rechecked before merge. Otherwise the system can be internally consistent and still build the wrong thing.
