Human, procedural, and mixed workflows fail when instructions are vague or context is missing. Identify the exact breakdown point before bad instructions compound into operational rework.
AI workflow instructions often fail not because the model is wrong, but because the instructions are incomplete, vague, or structurally unsound. Workflow Doctor is an operational diagnostic tool inside TryPromptFlow that checks your standard operating procedures, checklists, and manual prompts across five strict dimensions. Each stage must pass before the next runs.
To run a diagnosis, provide the symptomatic instruction exactly as it is currently deployed. Include any context given to operators, the expected criteria for a successful run, and ideally one example of a failed or rejected output.
Is all required operational context present? We flag missing assumptions and hidden dependencies.
Are the instructions precise enough to execute consistently without human interpretation?
Can this procedural instruction be followed as written by the assigned operator or model?
Are the limits, guardrails, and boundaries explicit and enforceable?
Are acceptance criteria defined to prove the output met the standard?
Workflow Doctor identifies structural failure points and produces an artifact with explicit requirements.
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 1 business day. Eligibility criteria (all must be met): — Order placed within 30 days — Item unopened/defective — Account in good standing Steps: 1. Retrieve order. Confirm date, item status, flag. 2. Apply criteria. Mark APPROVED or DECLINED. 3. If APPROVED: initiate refund, set status "refund_processing", send REF-01. 4. If DECLINED: send REF-02. Acceptance: Refund decision recorded < 4h. No exceptions without override.
TryPromptFlow works from the workflow information and evidence you provide. It does not automatically verify a fix or gate a release, and it does not continuously monitor, connect to, modify, or deploy changes to your live systems.
Workflow Doctor focuses strictly on static instructions. It does not map tool access, memory, recovery pathways, or cross-functional systems. For agentic workflows, use Agentic Workflow Doctor. For systemic breakdowns across teams, use Workflow OS Doctor.
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 (missing context, unclear constraints, no output specification) and produces a corrected artifact with explicit requirements and operator acceptance tests.