agentsop-code-execution-decisionDecision rubric for when an LM agent should write-and-run code (Program-of-Thought / code interpreter) versus reason in natural language: classify each step...
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Potentially destructive shell commands in tool definitions
exec (Audited Jun 2, 2026 · audit v1.0
Generated Sep 28, 2026
An AI finance assistant must compute compound interest, tax percentages, and multi-step loan amortization for user queries. Instead of letting the model do arithmetic in prose (which hallucinates plausible wrong numbers), the agent detects deterministic-computable steps and emits Python to a sandbox, then feeds the exact result back into the narrative.
A RAG/eval builder is designing retrieval pipeline steps that deduplicate, join, sort, and aggregate structured records. The decision rubric classifies these steps as emit-code, while keeping query reformulation and relevance ranking in prose. This prevents silent data-manipulation errors that break downstream evaluations.
A tool-use harness designer needs to decide, per step, whether the agent should call a code interpreter or answer directly. By embedding the emit-code-vs-reason gate, the harness avoids wasting sandbox round-trips on judgment tasks like tone review, and avoids prose arithmetic hallucinations on parsing and hashing tasks.
A support analytics agent computes CSAT deltas, date-range metrics, and regex-parsed ticket categorizations. The rubric routes these to code execution while keeping empathy scoring and summarization in natural language. The result is deterministic reporting with human-quality narrative summaries.
A research assistant parses CSV experiment data, performs unit conversions, and solves systems of equations for lab workflows. The skill flags these as deterministic-computable and emits executable code, while literature synthesis and hypothesis critique stay as prose reasoning for open-ended judgment.
Distribute the decision rubric as a free, composable overlay alongside DSPy and other agent frameworks. Adoption drives downstream demand for paid tooling, hosted sandboxes, and enterprise support around reliable code-execution agents.
Package the rubric with evaluation tooling that detects under-coding (prose math hallucinations) and over-coding (wasted sandbox calls) across agent traces. Enterprises buy the suite to audit and harden production agents.
Build domain-specific agents (finance, healthcare, logistics) that internally apply the emit-code-vs-reason gate to guarantee computational accuracy. Customers pay for correct, auditable outputs rather than raw model access.
💬 Integration Tip
Wire the gate as a pre-step classifier that tags each signature step as deterministic-computable or judgment, then route only the former to ProgramOfThought or a sandbox; log both under-coding and over-coding incidents to tune the threshold over time.
Scored Sep 28, 2026
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