Trustworthy AI as an Engineering Path
What is often described today as “Trustworthy AI” is not a single technology, nor a regulatory slogan.
It is an engineering trajectory that unfolds in stages.
What we currently see in most products represents only the first third of that path.
Stage I — SaaS 2.0: Service-as-Agent-Service
The first stage corresponds to what can be called SaaS 2.0:
software services wrapped as agents.
At this stage, agents automate tasks, coordinate workflows, and replace portions of human operations.
They improve efficiency, but they do not yet resolve responsibility.
This layer maps closely to early policy discussions of trustworthy AI:
useful, assistive, but still dependent on external human accountability.
It is a necessary foundation — and only that.
Stage II — SaaS 3.0: Service-as-Agent-Contract-Service
The second stage introduces a structural shift.
Here, services are no longer just agents.
They become contract-bearing agents.
This layer handles:
- Responsibility attribution
- Risk mapping
- Automated fulfillment and breach conditions
Only at this stage does trust become an internal property of the system, rather than an external promise.
Without agent-level contract logic, liability remains ambiguous, and AI systems remain uninsurable.
This completes the second third of the trustworthy AI path.
Stage III — Regulation as a Closing Layer
Only after the first two stages are in place does regulation become meaningful.
Regulation cannot substitute for missing responsibility logic.
It can only operate once systems are:
- Contract-aware
- Risk-expressive
- Auditable by design
At that point, regulation closes the loop rather than attempting to enforce trust from the outside.
This is the final third of the path.
Taiwan’s Structural Position
This staged path aligns unusually well with Taiwan’s industrial strengths.
Taiwan does not need to become a model-centric AI power.
Its advantage lies elsewhere:
- OEM / ODM capability
- Process standardization
- System integration under constraint
Agent systems built on contractual responsibility extend naturally from these strengths.
This is a structural opportunity, not a catch-up race.
SaaS Is Fragmenting, Agents Are Coming Online
As systems move toward risk-oriented deployment:
- Model size becomes less decisive
- Data volume becomes less central
- Traditional SaaS scale effects begin to fragment
Agent architectures reorganize services around responsibility, not features.
This is not a question of technical feasibility, but of recognition.
From Manufacturing Economy to Entrepreneurial Economy
When SaaS can be packaged as agents, the limiting factor is no longer headcount.
The bottleneck becomes intent.
With agent-native tooling, individuals can deploy AI workers directly.
Entrepreneurship becomes the default interface to production.
This shift offers an alternative response to demographic decline.
What Industry Actually Asks
In practice, legacy industries ask only three questions when adopting AI:
- Will this cause incidents?
- Who is responsible?
- Can it be insured?
Technology alone does not answer these.
What is being sold is not intelligence —
but risk transfer capability.
Closing Note
Trustworthy AI is not achieved by declaring trust.
It emerges when agents can act, commit, fail, and be held accountable —
within systems designed for that reality.