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Is a Multi-Agent System a Failure If It Fails to Reach Consensus? — Designing “Non-Agreement” as an Outcome, and the Value of Conflict Logs —

When people hear “multi-agent,”many expect the following: Agents discuss with one another.They reconcile their differen...

2026.02.26

ICT技術:ICT Technology人工知能:Artificial Intelligence

Treat Cold-Start as a Contract — Transform Data Scarcity into a Design Problem —

Cold-Start Is Considered a Technical Problem The cold-start problem is usually treated as a technical issue. There ...

2026.02.25

ICT技術:ICT Technology人工知能:Artificial Intelligence

Where Should We Write the Condition to Stop Judgment? — Externalizing Stop Conditions and the Importance of Designing “Return to Human” —

AI does not stop. Optimization continues.Probabilities update.Scores improve. This is not a defect.Computation is desi...

2026.02.24

ICT技術:ICT Technology人工知能:Artificial Intelligence

How to Build Ontology, DSL, and Behavior Trees Efficiently and Accurately — A Practical Method for Designing Judgment Structures —

In previous articles, I explained why judgment must be externalized. We introduced three structural components: ...

2026.02.23

ICT技術:ICT Technology人工知能:Artificial Intelligence

Externalizing Judgment in AI Systems — A Decision-Oriented Signal Platform Built on Ontology, DSL, and Behavior Trees

Many AI platforms are designed with a single dominant focus: How can we make the model smarter? Improve accuracy. ...

2026.02.22

ICT技術:ICT Technology人工知能:Artificial Intelligence

Designing Progressive Trust Scores as Contracts — Treating Trust Not as a Learned Outcome, but as a Revocable Boundary —

Trust scores are convenient.When there is a number, people feel reassured. But there is a fatal misunderstanding about ...

2026.02.21

ICT技術:ICT Technology人工知能:Artificial Intelligence

Externalizing Judgment in Machine Learning Systems — The Design Philosophy Behind the Decision-Oriented Signal Platform —

Most AI platforms are designed around a single goal: building smarter models. Improve accuracy.Increase inference speed...

2026.02.20

Uncategorized

Exceptions Are Not Failures — How to Integrate Exception Handling into Design, and Why More Exceptions Can Mean a Healthier System —

A system with many exceptions is often seen as“unfinished,”“unstable,”or “poorly designed.” That is why many AI and bus...

2026.02.19

ICT技術:ICT Technology人工知能:Artificial Intelligence

Who Owns the Objective Function? — The Implicit Judgments Embedded in Scores and the Problem of the Values Humans Did Not Write Down —

AI is often believed to be fair.It evaluates through numbers, remains unaffected by emotion, and produces consistent jud...

2026.02.18

ICT技術:ICT Technology人工知能:Artificial Intelligence

Why AI Cannot Possess “Common Sense” — Common Sense as a Collection of Discontinuities, and Common Sense as Ontology —

AI knows an enormous amount. Wider than an encyclopedia.Faster than a specialist. And yet, at a certain moment, we fee...

2026.02.17

ICT技術:ICT Technology人工知能:Artificial Intelligence

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Is a Multi-Agent System a Failure If It Fails to Reach Consensus? — Designing “Non-Agreement” as an Outcome, and the Value of Conflict Logs —

Treat Cold-Start as a Contract — Transform Data Scarcity into a Design Problem —

Where Should We Write the Condition to Stop Judgment? — Externalizing Stop Conditions and the Importance of Designing “Return to Human” —

How to Build Ontology, DSL, and Behavior Trees Efficiently and Accurately — A Practical Method for Designing Judgment Structures —

Externalizing Judgment in AI Systems — A Decision-Oriented Signal Platform Built on Ontology, DSL, and Behavior Trees

Designing Progressive Trust Scores as Contracts — Treating Trust Not as a Learned Outcome, but as a Revocable Boundary —

Externalizing Judgment in Machine Learning Systems — The Design Philosophy Behind the Decision-Oriented Signal Platform —

Exceptions Are Not Failures — How to Integrate Exception Handling into Design, and Why More Exceptions Can Mean a Healthier System —

Who Owns the Objective Function? — The Implicit Judgments Embedded in Scores and the Problem of the Values Humans Did Not Write Down —

Why AI Cannot Possess “Common Sense” — Common Sense as a Collection of Discontinuities, and Common Sense as Ontology —

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