Recognize the problem
Separate the visible request from the business outcome. Define value, ownership, constraints, and what a meaningful result changes.
Understand the work before you scale it.
A six-stage system for converting uncertainty into a testable operating artifact.
Forsolving turns an ambiguous business problem into a verifiable operating skill for a person, AI agent, or human-AI team.
Find the causes
Inspect how the work happens today. Surface bottlenecks, assumptions, missing evidence, failure modes, and the knowledge people use without documenting it.
Formulate the task
Write the task as a specific goal achieved through a repeatable algorithm under known conditions. State what is in scope and what is not.
Design the skill
Package inputs, sources, logic, tools, output formats, checkpoints, stop rules, and escalation into a reusable operating unit.
Test real cases
Build a representative evaluation set. Measure factuality, consistency, policy compliance, exception handling, and human review requirements.
Choose the outcome
Use evidence to decide whether the process should stay manual, be documented, use AI assistance, become an agentic workflow, or be implemented as software.
Decision checkpoint
Automation is one possible answer.
The method deliberately delays implementation decisions. This prevents teams from hardening a false assumption into software, or asking an agent to make decisions the business itself has not defined.