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1 starsPython

Pre-execution verification for LLM-generated agentic workflows

by jaredwaxman·Mar 5, 2026·4 points·5 comments

AI Analysis

●●●BangerBig BrainSolve My Problem

Type-safe AST verification for AI workflows before they corrupt your CRM or delete production data.

Strengths
  • Closes a real gap: today AI agents generate code that executes immediately with zero pre-flight checks on types or side effects
  • AST-based approach (not runtime sandboxing) catches logical errors before transpilation, saving debugging cycles
  • Includes self-correction loop: verification failures can prompt the agent to fix itself and retry
Weaknesses
  • Ecosystem adoption unclear: agents need to generate to this AST spec, which isn't yet standard; library is only months old
  • No evidence of production usage or battle-tested against real agentic workflows (read-heavy examples only)
Target Audience

AI agent builders, workflow automation platforms, teams running LLM-generated code in production

Similar To

Anthropic Anthropic's structured outputs · Pydantic for runtime validation · Temporal Workflows for determinism

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