Originally published on AI Tech Connect.
Why single-agent architectures hit a ceiling A single agent running in a long context window can handle a surprising range of tasks, but three constraints reliably force builders toward multi-agent designs once workloads grow beyond toy complexity. The first is context limits. Even with models offering 128k or 1M-token windows, shoving an entire workflow's history, tool outputs, and instructions into one context is expensive and fragile. Retrieval quality degrades as context grows, and the cost-per-task scales linearly with context size. A multi-agent design where each specialist receives only the context it needs is both cheaper and more accurate. The second is specialisation. A general-purpose agent prompted to "research, then draft, then critique, then execute" performs worse than a…
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