Completion semantics describes how execution is considered finished.
This work treats completion not as an output event, but as a formally defined condition under which execution is allowed to terminate. The distinction is foundational.
From output generation to completion conditions
In most contemporary AI systems, execution is implicitly considered complete when an output is produced.
This assumption holds for conversational interfaces, but it collapses once language is used as an execution driver rather than a response surface.
When language is executable, completion cannot be inferred from generation alone.
Completion is a semantic condition, not a temporal one
Completion is not defined by time, token limits, or user satisfaction signals.
It is defined by whether a set of semantic conditions has been satisfied.
These conditions may include, but are not limited to:
- Required intents being resolved
- Obligations being fulfilled or explicitly delegated
- Constraints being verified
- Side effects being acknowledged and recorded
- Responsibility boundaries being respected
Execution may continue after output, or terminate without visible output, depending on whether completion conditions are met.
Why completion semantics is required for accountable execution
Without explicit completion semantics:
- Execution cannot be audited
- Responsibility cannot be assigned
- Partial execution cannot be distinguished from failure
- Retry, rollback, or delegation cannot be reasoned about
Systems that rely solely on generated responses implicitly conflate saying something with having done something.
This work treats that conflation as architecturally invalid.
Completion semantics and agent responsibility
In this framework, an agent is considered to have acted only when completion conditions associated with its delegated scope are met.
An agent that produces output without satisfying completion conditions has not completed execution, regardless of fluency or plausibility.
This distinction is essential for:
- Delegation chains
- Liability attribution
- Multi-agent coordination
- Institutional acceptance of AI-mediated actions
Completion semantics as an execution boundary
Completion semantics defines the boundary between:
- Ongoing execution
- Suspended execution
- Failed execution
- Completed execution
These states are not inferred heuristically. They are derived from explicitly declared semantic criteria.
This allows execution state to be:
- Inspected
- Signed
- Replayed
- Disputed
independently of any specific model or runtime.
Relation to intermediate semantic structures
Completion semantics operates at the same layer as intermediate semantic representations.
It is not a user-facing concept, nor an application-level convenience.
It constrains how intermediate structures are evaluated, and when they are permitted to resolve into effects.
Without completion semantics, intermediate representations degrade into transient prompts.
Implications
Treating completion as a semantic condition implies that:
- Execution correctness is evaluable
- Partial compliance is distinguishable from success
- AI systems can be integrated into institutional workflows without redefining accountability standards
Completion semantics is therefore not an optimization. It is a prerequisite for executable language systems that operate beyond demonstration environments.
Reading note
This concept underpins later discussions of:
- Delegation and responsibility
- Semantic execution models
- Agent lifecycle governance
- Institutional AI compatibility
It should be read as a foundational constraint, not as an isolated mechanism.