Forsolving glossary

Language for turning uncertainty into a skill.

One foundational meta-skill and twenty practical terms for describing the goal, designing the work, keeping people in control, and testing what was built.

Clear language is part of the architecture.

A shared vocabulary helps teams challenge the first request, expose hidden decisions, and agree on what evidence is needed before work is automated.

Meta-Skill / FoundationUmbrella Concept

Agentic Thinking

The capacity to turn an ambiguous business goal into a governed, autonomous, and self-correcting process — producing a tested operating artifact rather than a passive LLM response.

01Goal & Decomposition

Converts vague requests into true business outcomes (Terms 01–05).

02Skill Architecture

Transforms tacit know-how into inspectable blueprints (Terms 06–10).

03Governance & Control

Enforces stop rules, exceptions, and human checkpoints (Terms 11–15).

04Verification Loop

Proves reliability on real cases before automation (Terms 16–20).

Glossary shorts

Watch the concepts.

Three short explanations of the language behind Forsolving.

Automation readinessOpen on YouTube
01

Core method

From an ambiguous request to work that can be designed.

00
Agentic thinking (Агентное мышление)
The capacity to turn an ambiguous business goal into a governed, multi-agent process with explicit roles, trusted sources, human checkpoints, and verifiable outputs.
01
Forsolving
A method for recognizing a problem, identifying its causes, turning the real goal into tasks, and designing a verifiable way of working.
02
Forsolver
The person who separates the visible request from the goal, evidence, constraints, and decisions behind it.
03
False request
The first requested output when it hides or oversimplifies the outcome that actually matters.
04
Actual goal
The business outcome that should be achieved, independent of the tool or format first requested.
05
Problem decomposition
Breaking ambiguous work into causes, tasks, decisions, evidence, exceptions, and ownership.
02

Skill design

The parts that turn tacit know-how into an operating artifact.

06
Task
A bounded piece of work derived from the actual goal, with a clear result and conditions for completion.
07
Operating skill
A repeatable way to perform work, including its inputs, sources, logic, outputs, controls, and tests.
08
Skill Blueprint
The inspectable specification of a skill: goals, inputs, sources, decision logic, exceptions, outputs, checkpoints, and evaluation cases.
09
Trusted source
Material that the skill is allowed to rely on, such as an approved policy, contract, process record, or expert decision.
10
Decision logic
The explicit rules and reasoning that connect evidence to an action or conclusion.
03

Control

Where automation stops and accountable judgment begins.

11
Exception
A case that cannot safely follow the normal path because evidence, policy, risk, or context is different.
12
Stop rule
A defined condition that prevents the skill from continuing automatically.
13
Human checkpoint
A moment when a person reviews evidence, approves a decision, or takes ownership of the next step.
14
Escalation
Routing a case to the right person with enough context and evidence to decide what happens next.
15
Automation readiness
The degree to which goals, logic, inputs, exceptions, controls, and ownership are stable enough to implement.
04

Evidence & testing

How the skill proves that it works beyond a polished example.

16
Representative case
A real or realistic example that covers the normal path or a meaningful variation of it.
17
Acceptance criterion
A measurable condition used to decide whether an output or decision is good enough.
18
Demonstrator case
A transparent example used to explain or test the method without presenting it as a customer result.
19
Human-AI team
A working arrangement in which people and AI have explicit roles, controls, handoffs, and shared evidence.
20
Forsolving loop
A practical cycle: Problem → Cause → Task → Skill → Test → Outcome. Test results can send the work back to any earlier step.
Use the language on real work

Bring one process. Name the real goal first.