No Way to Compose Multiple AI Tools Into One Reliable, Verifiable Workflow Without Engineering Skills

Summary

Real work rarely fits one tool: it needs research → extraction → cross-checking → spreadsheet writing. Today non-technical users are told to 'just use the API' or to stitch together automation platforms, but chatbots 'stop midway', return 'inaccurate or too generic' answers, and silently corrupt data across 18,327 rows — forcing hours of manual re-verification. Users with zero Python experience have no way to know which tool should do which step, where a verification pass belongs, or what failure rate to expect, and automation stacks like n8n/Relay/Lindy produce no measurable ROI for them. This is unsolved: no recommendation layer outputs a concrete multi-tool workflow (tool A → tool B → validation step C) with expected reliability, per-unit cost, and guardrails for non-technical operators. The result is wasted budget, missed client deliverables, and people abandoning AI automation entirely.