Equation 16 · Reliable AI Agents Are Control Systems, Not Chatbots
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Symbol k
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superscript
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The empirical warning appears in agent benchmarks. SWE-bench constructs software tasks from real GitHub issues and associated code changes, forcing systems to coordinate edits across repositories rather than complete isolated snippets [ 9 ] . Its scores are informative, but they remain conditional on a particular harness, task filtering, test infrastructure, and contamination controls. The -bench work goes further by evaluating tool-using agents against domain rules and final database states. In its reported experiments, leading function-calling agents completed fewer than half of tasks, and consistency over repeated trials deteriorated sharply; the proposed perspective…
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The empirical warning appears in agent benchmarks. SWE-bench constructs software tasks from real GitHub issues and associated code changes, forcing systems to coordinate edits across repositories rather than complete isolated snippets [ 9 ] . Its scores are informative, but they remain conditional on a particular harness, task filtering, test infrastructure, and contamination controls. The -bench work goes further by evaluating tool-using agents against domain rules and final database states. In its reported experiments, leading function-calling agents completed fewer than half of tasks, and consistency over repeated trials deteriorated sharply; the proposed perspective makes repeated reliability visible rather than celebrating a lucky run [ 10 ] .
Sources cited in the surrounding passage
- [9] SWE-bench: Can Language Models Resolve Real-World GitHub Issues? ↗
- [10] tau-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains ↗
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