Glages: The Model Reveals the Automation Boundary
Many automation projects begin with a promise to automate a process and discover the real boundary only after substantial development has already started.
A released Glages Model provides a fixed semantic reference against which a licensee can evaluate its available facts, authoritative data sources, execution mechanisms, and implementation readiness. Required facts may be unavailable or untrustworthy. An important runtime state may not be recorded. A required action may have no safe execution mechanism. Local practice may depend on judgment that is not represented by the model.
The model can reveal these limits before full implementation.
What the Boundary Shows
An automation boundary separates the part of an activity that the licensee can execute under the released model's defined conditions from the part that still depends on missing runtime facts, authoritative sources, implementation mappings, or execution mechanisms.
The result may state:
this part can be executed under the released model
this part cannot yet be executed
these required facts, authoritative sources, mappings, or execution mechanisms are missing
This is an explicit implementation-readiness finding.
Missing Facts
A process may require a fact that the licensee does not possess or cannot trust.
A refund model may require the amount already refunded across all related transactions. A delivery model may require the actual handoff state rather than a customer-service note. A claim model may require evidence with a resolvable source and time.
If a required fact is absent, model-governed execution cannot proceed. The runtime must preserve the missing-fact condition rather than authorize an agent to fill the gap with a likely answer.
Undefined Rules and Authority
A licensee may have local practices or instructions that do not map cleanly to the released model's explicit rules and authority structure.
For example, a local instruction such as “managers may make reasonable exceptions” does not by itself create a new modeled permission. The licensee must either operate within the released model, use an applicable released Glages Model specialization, or treat the situation as outside the modeled executable boundary.
Applying the released model exposes that difference before runtime.
Missing Execution Mechanisms
The business meaning may be clear while the technical environment remains incomplete.
The model may permit an order modification, but no available API can perform it safely. The process is modeled, yet controlled execution cannot proceed until an appropriate mechanism exists.
This distinction helps licensees avoid confusing model-defined process meaning with integration work.
A Verified Result Before Runtime
The automation boundary can support due diligence, budget preparation, project scoping, software planning, integration planning, and risk analysis.
A licensee can see which parts of the activity can already be executed under the released model with the available facts and mechanisms, which implementation gaps remain, and where the main cost and uncertainty lie.
This is often more valuable than an early demonstration that handles only common cases while hiding its assumptions.
A Foundation for Incremental Automation
The boundary also creates a rational path forward.
Instead of declaring the whole process automated or not automated, the licensee can address implementation gaps such as adding a required field, establishing an authoritative source, exposing a safe API, or improving the mapping between its systems and the model. If a required behavior lies outside the released model's declared scope, or new generalized domain evidence reveals a genuine coverage issue within that scope, that is a separate Model Factory evolution question rather than a local company-specific correction.
The executable area can then expand as the required facts and mechanisms become available.
A released Glages Model can provide value before runtime automation begins by giving a precise account of what can be executed now and what implementation gaps must be resolved before additional model-defined behavior becomes executable.