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Doing Less with More: A First-Principles Exploration of the Suitability of Agentic Computing over Alternative Architectural Choices

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 20/10/2026, 04:03:24 UTC.

Briefings

A controlled experiment compared seven computing methods for 1,000 arithmetic expressions, revealing that deterministic local methods achieve 100% accuracy with negligible latency, while agentic systems using LLMs and MCP tools fail to meet this benchmark due to excessive latency. The study concludes that LLM-based approaches are unsuitable for exact arithmetic evaluation and recommends an architectural decision framework based on specific task requirements rather than a one-size-fits-all solution.

Key points

• Seven architectural methods were tested on a benchmark of 1,000 arithmetic expressions requiring preserved operator precedence.
• Deterministic local methods achieved 100% accuracy with negligible latency, whereas agentic systems using LLMs and MCP tools failed to meet this accuracy threshold due to high latency.

Context

The research investigates whether agentic computing is preferable to established alternatives like REST, SOAP, MCP, and local LLMs for business activities, specifically when development speed, performance, and operational cost are factors. The study uses a controlled mathematical task to compare seven methods on a benchmark of 1,000 arithmetic expressions where semantics of operator precedence must be preserved.

Limitations

The evidence is limited to a single controlled mathematical task with 1,000 arithmetic expressions. Results are based on a preprint (arXiv:2610.23921v1) and include significant uncertainty regarding real-world operational costs and performance metrics beyond the benchmark.

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