AI workflow instructions often fail not because the model is wrong, but because the instructions are incomplete, vague, or structurally unsound. Workflow Doctor is a diagnostic tool inside TryPromptFlow that checks your instructions across five dimensions: completeness, specificity, executability, constraint fidelity, and test coverage alignment. Each stage must pass before the next runs. It returns a corrected artifact, paste-ready operator rules, and a release confidence note — a go/no-go signal based on deterministic rules, not AI opinion. Workflow Doctor does not run live AI agents. It diagnoses instructions before they are used.
For pre-launch diagnosis of bot-driven and AI-executed workflows, Agentic Workflow Doctor reviews the full workflow design — approval gates, action boundaries, recovery risks, and runtime controls.
Here is what the Completeness and Specificity gates catch in a real intake. The input below is a typical team SOP for a customer refund approval process.
When a customer asks for a refund, check their order and decide if it qualifies. If it does, process it and let them know.
Purpose: Determine refund eligibility and process approved refunds within one business day. Eligibility criteria (all must be met): — Order placed within the last 30 days — Item is unopened or defective as documented — Customer account is in good standing (no prior fraud flags) Steps: 1. Retrieve order record from system. Confirm date, item status, and account flag. 2. Apply eligibility criteria. If all pass, mark APPROVED. If any fail, mark DECLINED with reason. 3. If APPROVED: initiate refund in payments system, set status to "refund_processing", send confirmation email using template REF-01. 4. If DECLINED: send decline notice using template REF-02 with specific reason. Do not offer alternatives unless customer replies. Acceptance criteria: — Refund decision recorded within 4 hours of request — No refund issued for orders older than 30 days (no exceptions without manager override) — Email confirmation sent within 1 hour of approval
Gates triggered: Completeness (missing eligibility criteria, missing step sequence), Specificity (vague "decide if it qualifies"), Test Coverage (no acceptance criteria defined). All three failed on the original. The corrected artifact passes all five.
Workflow Doctor runs a five-stage diagnostic on your AI workflow instructions — checking completeness, specificity, executability, constraint fidelity, and test coverage alignment. Each stage must pass before the next runs. It returns a corrected artifact, paste-ready operator rules, and a release confidence note.
A release confidence note is a deterministic, rules-based go/no-go signal — not an AI opinion. It tells you whether the repaired workflow instruction is ready for controlled use, needs human review, or should not be released yet.
No. Workflow Doctor diagnoses and repairs workflow instructions. It does not run live AI agents, execute workflows, or manage any running AI system. It operates before deployment, not during it.
Workflow Doctor works on prompts, SOPs, checklists, and workflow instruction sets used to guide AI systems. It is not specific to any one AI model or platform.
Asking an AI to rewrite a prompt produces a different prompt — it does not diagnose why the original failed. Workflow Doctor identifies specific structural failure points and produces a corrected artifact with explicit requirements and operator acceptance tests.