30 lines
2.7 KiB
Org Mode
30 lines
2.7 KiB
Org Mode
---
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title: One Single Agent
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type: reference
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tags: :passepartout:architecture:
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---
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* One Single Agent
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:PROPERTIES:
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:ID: 92e314a1-a650-4878-958a-9646aef4e032
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:ID: design-multi-agent-default
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:CREATED: [2026-05-07 Wed]
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:WEIGHT: 40
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:END:
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The AI industry has developed an intuition toward multi-agent systems as the default solution to hard problems. Multiple agents spawn, delegate, coordinate, debate, and consensus their way toward solutions. This pattern is compelling in demos and genuinely useful in specific contexts — but it has become a default assumption that warrants scrutiny.
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When context windows grew expensive and task complexity increased, the response was natural: split the problem across agents, each handling a slice. But this architectural choice carries hidden costs that are rarely acknowledged.
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*The synchronization tax* is the most immediate burden. Each agent operates with partial information, and maintaining coherence requires continuous state reconciliation. Tokens and processing cycles are spent not on the task itself, but on protocol overhead — who holds what, who decided what, who is correct when they disagree.
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*Fragmented context* is the deeper problem. When Agent A writes a function and Agent B modifies a type it depends on, neither has the full picture. Integration failures emerge not from individual incompetence but from systemic communication gaps. Single-agent systems avoid this entirely: one brain holds the complete model, every decision is made with full visibility.
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*Audit trails become complex* in multi-agent systems. A decision traced through a single-agent system has a clean, linear history. A decision traced through a multi-agent system branches and forks, with each agent's reasoning partially overlapping and partially conflicting.
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None of this is to say multi-agent systems are never appropriate. Embarrassingly parallel workloads benefit from parallelism regardless of context. When distinct expertises are required and cannot coexist in one model, delegation makes sense. In adversarial scenarios where conflicting goals are features, multi-agent architectures shine.
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But the default assumption that complex reasoning tasks are best solved by multiple agents is unproven and likely wrong for the engineering domain. Claude Code is a single-agent system. It handles 50-file refactors, debugs complex stack traces, writes tests, and navigates large codebases. The assumption that you need five agents to do what one well-designed agent can do is an industry habit, not a technical necessity.
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Passepartout is single-agent by default not from limitation but from conviction: for reasoning-heavy work where coherence matters, a unified memory space and single decision-making locus are architectural assets, not constraints.
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