AI RESEARCH MISSION 08 — SEMANTIC COMPILATION AND SPECIFICATION DRIFT

ROLE

Act as a combined:

• software-architecture researcher
• programming-languages researcher
• compiler / DSL researcher
• model-driven engineering researcher
• formal-methods researcher
• requirements-engineering researcher
• test-engineering researcher
• AI-agent systems researcher
• empirical software-engineering researcher
• AI inference-cost researcher

Your task is to investigate whether a single authoritative semantic model can reduce specification drift across software artifacts.

The architecture under investigation proposes that important business semantics should be represented once in an executable semantic specification and then used to generate or constrain:

runtime guards
state types
legal transitions
capabilities
obligations
policy checks
agent tool availability
generated tests
planning graphs
semantic dependency reports
migration checks
documentation
API contracts
analyzers

The central hypothesis is:

When the same semantic rule is independently expressed in many artifacts,
those artifacts drift.

When derivative artifacts are generated or mechanically validated from one
authoritative semantic source, cross-representation drift decreases.

Do NOT assume this hypothesis is true.

A single semantic source may instead create:

correlated failure
over-centralization
specification rigidity
catastrophic propagation of a wrong rule
excessive tooling complexity

The goal is to determine whether semantic compilation improves correctness and agent economics enough to justify those risks.

======================================================================

1. PRIMARY RESEARCH QUESTION ======================================================================

Does compiling multiple software artifacts from one authoritative semantic model reduce:

rule duplication
inconsistent validation
stale tests
stale documentation
contradictory agent instructions
policy drift
tool-schema drift
semantic reconstruction cost
AI-agent context requirements
maintenance effort
total cost per correct change?

And does it improve:

semantic consistency
change impact visibility
auditability
long-term maintainability
agent reliability?

====================================================================== 2. THE DRIFT PROBLEM

A single business rule may currently exist independently in:

product requirements
backend code
frontend code
SQL constraints
tests
API validation
documentation
runbooks
policy documents
agent prompts
tool descriptions
monitoring logic

Example rule:

An order may ship only when:
    Order = Approved
    Payment = Captured
    Customer = Verified
    Shipment = Ready

Possible independent implementations:

backend if statement
frontend button enablement
database procedure
integration test
agent prompt
workflow engine condition

Research how often these representations drift in normal systems.

====================================================================== 3. DEFINE SPECIFICATION DRIFT

Develop a precise definition.

Candidate:

Specification drift occurs when two or more artifacts that are intended to
represent the same semantic rule cease to agree.

Examples:

UI allows action backend rejects
test expects old state behavior
prompt says action is legal when runtime rejects it
policy document differs from code
tool description says retry is safe but implementation changed
database validation omits new state

Distinguish from:

intentional versioning
gradual rollout
backward compatibility
different context-specific policies

====================================================================== 4. REPRESENTATION MULTIPLICITY

Investigate the general problem:

one meaning
many representations

This appears in:

schema duplication
protocol definitions
validation logic
API/client generation
infrastructure configuration
policy-as-code
code/documentation synchronization

Ask:

Is semantic drift primarily caused by duplicate representation?

====================================================================== 5. SINGLE SOURCE OF TRUTH

Research the phrase carefully.

"Single source of truth" is often used loosely.

Distinguish:

authoritative specification

from:

one database
one document
one repository

The relevant concept is:

one authoritative semantic definition

with possibly many generated projections.

====================================================================== 6. SEMANTIC SOURCE VS IMPLEMENTATION SOURCE

Do not assume source code itself is the best semantic source.

Compare:

A. implementation code is authoritative

B. tests are authoritative

C. API schema is authoritative

D. model/DSL is authoritative

E. formal specification is authoritative

For each ask:

expressive power
readability
auditability
executability
derivability
drift risk
AI usability

====================================================================== 7. MODEL-DRIVEN ENGINEERING

Research deeply:

Model-Driven Engineering
Model-Driven Architecture
executable models
code generation
metamodels
model transformations
round-trip engineering

Ask:

Is semantic compilation simply MDE applied to AI-operated software?

Identify:

known benefits
known failure modes
adoption history
why MDE succeeded or failed commercially

====================================================================== 8. DSLs

Research domain-specific languages used to express:

state machines
workflows
policies
contracts
protocols
schemas

Compare:

textual DSL
JSON/YAML model
embedded DSL
host-language API
graphical model

Evaluate:

semantic precision
tooling
versioning
readability
token cost
agent generation accuracy

====================================================================== 9. EXECUTABLE SPECIFICATIONS

Research:

executable specifications
specification by example
formal executable models
behavior-driven development

Question:

When does executable specification reduce drift?

When does it simply become another implementation layer?

====================================================================== 10. SCHEMA-FIRST SYSTEMS

Compare with:

OpenAPI
GraphQL schemas
Protocol Buffers
Avro
JSON Schema
database schema generation

These already demonstrate:

one declaration
    ->
many generated artifacts

Ask what lessons apply to domain semantics.

====================================================================== 11. PROTOCOL COMPILERS

Study systems such as:

protobuf
Thrift
gRPC IDLs

They generate:

types
serialization
clients
servers

This reduces certain forms of drift.

But they do not usually encode:

legal state transitions
obligations
policy
evidence

Identify the incremental layer proposed here.

====================================================================== 12. POLICY-AS-CODE

Research:

OPA/Rego
Cedar
XACML
authorization policy systems

These centralize policy and generate/enforce decisions.

Ask:

what types of drift do they solve?
what types remain?

====================================================================== 13. INFRASTRUCTURE AS CODE ANALOGY

IaC replaced:

manual configuration

with:

declarative source

This improved reproducibility but introduced:

configuration complexity
drift detection
state synchronization

Research useful analogies without overextending them.

====================================================================== 14. DATABASE MIGRATIONS

Schema migration systems provide:

explicit version transitions
ordered history

Potential analogy:

semantic migrations

Research lessons from:

Flyway
Liquibase
migration-based schema evolution

====================================================================== 15. TEST GENERATION

If semantic specification says:

legal transitions = X

generate tests:

every X succeeds when prerequisites hold
illegal transitions fail
every state has required interpretations

Question:

Does generated testing reduce test drift?

Potential downside:

generated tests merely confirm generator/spec is self-consistent.

====================================================================== 16. ORACLE CORRELATION RISK

Critical issue:

If implementation and tests are generated from same wrong spec,
they agree perfectly while both are wrong.

This is correlated failure.

Research:

N-version programming
independent test oracles
mutation testing
differential testing
property-based testing

The system needs independent correctness checks.

====================================================================== 17. GENERATED DOCUMENTATION

Potentially generate:

state diagrams
transition docs
capability docs
policy impact docs
agent-readable summaries

Hypothesis:

generated docs stay structurally current.

But:

generated docs may omit rationale
business context
nuance

Research limitations.

====================================================================== 18. AI-WRITTEN DOCUMENTATION VS GENERATED DOCUMENTATION

Compare:

A. AI summarizes repository

B. semantic compiler renders current model

Measure:

completeness
staleness
hallucinated rules
token size
maintenance cost

====================================================================== 19. AGENT TOOL GENERATION

Semantic source may generate current tool interfaces.

Example:

Transition:

CapturePayment

can generate:

runtime handler
tool schema
capability rule
documentation

This may eliminate drift between:

what tool says it does

and:

what runtime allows.

====================================================================== 20. TOOL AVAILABILITY

Current semantic state could generate:

visible legal tools

rather than exposing all tools.

This connects specification compilation with action-space reduction.

Research whether a shared source reduces:

tool description/runtime mismatch.

====================================================================== 21. PROMPT GENERATION

Could semantic specification generate agent instructions?

Examples:

legal action constraints
blocked reasons
current policy summary

Potential benefit:

fewer hand-maintained prompts

Risk:

prompts become verbose or brittle.

====================================================================== 22. RUNTIME GUARD GENERATION

Transition definition:

Ship requires:
    OrderApproved
    PaymentCaptured

could generate:

runtime precondition
capability derivation
tests
agent blocked explanation

This is core hypothesis.

Research code-generation reliability.

====================================================================== 23. CLIENT VALIDATION

Frontend may receive generated semantic projection.

But client validation must not replace server authority.

Research how to avoid:

duplicated client/server semantics

without trusting client enforcement.

====================================================================== 24. SQL / DATA-LAYER GENERATION

Can some invariants become:

database constraints
generated queries

while others remain application-level?

Research limitations.

Do not force all domain semantics into SQL.

====================================================================== 25. API CONTRACT GENERATION

Semantic transitions might generate API operations.

Question:

Should API map 1:1 to semantic transitions?

Potentially dangerous if:

API concerns differ from domain operations.

Research abstraction boundaries.

====================================================================== 26. PLANNING GRAPH GENERATION

If transitions declare:

preconditions
effects

then planner graph can be generated.

This may eliminate drift between:

executable system

and:

planning model.

This is particularly important for AI agents.

====================================================================== 27. IMPACT ANALYSIS GENERATION

A semantic dependency graph can produce:

affected capabilities
affected obligations
affected transitions
affected policies

when semantics change.

Research:

model transformation impact analysis
dependency tracing
incremental compilation

====================================================================== 28. SEMANTIC MIGRATION

When:

Approved

splits into:

ConditionallyApproved
FullyApproved

compiler may require explicit redistribution of every dependent semantic rule.

This prevents old rule inheritance.

Research analogous model evolution systems.

====================================================================== 29. ROUND-TRIP ENGINEERING

MDE often struggled with:

generated code edited manually
model/code synchronization
round-trip engineering

Question:

Should generated semantic artifacts be editable?

Likely:

no

or:

only through controlled extension points.

Research lessons.

====================================================================== 30. GENERATED VS HANDWRITTEN CODE BOUNDARY

Potential rule:

Generated:

semantic types
transition interfaces
validators
contracts
analyzers

Handwritten:

implementation algorithms
adapters
UI
integration code

Research how to preserve developer control.

====================================================================== 31. ESCAPE HATCHES

Every generator eventually needs exceptions.

Danger:

bypass becomes normal path.

Design possible:

explicit override
justification
expiration
review
provenance

Research exception governance.

====================================================================== 32. SPECIFICATION RIGIDITY

A central semantic model may make change harder.

Potential symptoms:

every feature requires DSL change
generator update
migration
regenerated artifacts

This may reduce startup agility.

Study historical MDE failures.

====================================================================== 33. SEMANTIC MODEL BLOAT

If specification grows to describe everything:

UI layout
integration details
algorithms
persistence

it becomes:

universal programming language

and loses benefit.

Identify correct scope.

====================================================================== 34. MINIMUM SEMANTIC CORE

Hypothesis:

Only encode:

consequential state
legal transitions
invariants
authority
evidence
policy
effect semantics
dependencies

Do not encode:

ordinary computation
presentation
incidental implementation

Research whether this avoids MDE overreach.

====================================================================== 35. SPECIFICATION COMPLEXITY

Measure:

semantic model LOC/tokens
generated artifact LOC
manual implementation LOC

Potential metric:

semantic compression ratio

    semantic specification tokens

SCR = -------------------------------- equivalent semantic implementation tokens

But smaller specification is not automatically better.

====================================================================== 36. CHANGE AMPLIFICATION

One semantic edit may regenerate many files.

That can appear as:

large diff

even if semantic change is small.

Research generated-code review challenges.

====================================================================== 37. SEMANTIC DIFF

Potential solution:

review semantic diff

instead of:

generated code diff

Generated artifacts can be mechanically verified.

Investigate tooling patterns.

====================================================================== 38. VERSION CONTROL

Should generated files be committed?

Compare:

commit generated artifacts

vs:

generate during build

Tradeoffs:

reviewability
reproducibility
merge conflicts
build complexity

====================================================================== 39. REPRODUCIBLE GENERATION

Generator must be deterministic.

Same:

semantic model
compiler version

should produce same:

artifacts

Research reproducible builds.

====================================================================== 40. COMPILER VERSIONING

Generated behavior depends on:

semantic spec version
semantic compiler version
target generator version

Historical provenance may need all three.

====================================================================== 41. SEMANTIC HASH

Possible artifact metadata:

SemanticHash
SpecVersion
IRVersion
GeneratorVersion

This enables:

drift detection

Research analogous content-addressed systems.

====================================================================== 42. DRIFT DETECTION

Build could verify:

generated artifact semantic hash == current model

If not:

stale generated artifact

This mechanically detects one type of drift.

====================================================================== 43. HANDWRITTEN ARTIFACT DRIFT

Not everything will be generated.

Need analyzers to verify:

handwritten adapter respects semantic contract

Research contract checking.

====================================================================== 44. CONFORMANCE TESTING

Generated conformance suite may test handwritten implementations.

Example:

adapter must implement effect idempotency rules

This creates boundary verification.

====================================================================== 45. FORMAL REFINEMENT

Explore whether implementation can be viewed as refinement of semantic model.

Research:

refinement mappings
simulation relations
formal refinement
executable refinement

Could provide stronger guarantees but may be too expensive.

====================================================================== 46. GENERATED TYPES

Target languages may differ.

Compiler may generate:

F# DUs
Rust enums
Java sealed types
C# unions/sealed patterns
TypeScript tagged unions

Question:

how much semantic equivalence survives across languages?

====================================================================== 47. CROSS-LANGUAGE SEMANTIC CONSISTENCY

A shared semantic IR could generate types/contracts for:

backend
frontend
mobile
agent tools

This may reduce cross-language drift.

Research protocol/schema generation analogues.

====================================================================== 48. WEAK LANGUAGE COMPENSATION

Semantic compiler might compensate for languages with weaker exhaustive/type features.

Example:

TypeScript analyzer generated from semantic model

Question:

can generator enforce enough to match stronger languages?

This overlaps language research.

====================================================================== 49. SQL SEMANTIC DRIFT

SQL often contains duplicated business meaning in:

stored procedures
triggers
queries
status filters

Can semantic compiler detect/generate some of this?

Be careful not to overpromise static SQL analysis.

====================================================================== 50. POLICY DRIFT

Same policy may exist in:

docs
code
agent prompt
SQL
UI

Policy compiler could generate:

runtime check
explanation
tool constraint
audit metadata

Research existing policy systems.

====================================================================== 51. TEST DRIFT

Measure:

tests inconsistent with current semantic spec

Potential categories:

stale expected state
stale allowed action
missing new case
outdated policy

Compiler could regenerate or invalidate tests.

====================================================================== 52. DOCUMENTATION DRIFT

Measure:

docs describing obsolete state/transition

Generated docs should eliminate structural drift.

But rationale prose may still drift.

====================================================================== 53. PROMPT DRIFT

Agent instructions frequently encode business rules manually.

Example:

"Never retry unknown refund."

If semantic runtime already blocks this:

prompt rule may be redundant.

Semantic compilation may allow shorter prompts.

====================================================================== 54. TOOL-SCHEMA DRIFT

Tool descriptions can become stale after API/runtime changes.

Generated schemas reduce this risk.

Measure:

invalid tool calls
mismatched parameters
unavailable operations

====================================================================== 55. POLICY EXPLANATIONS

Runtime should be able to explain:

action blocked because:
    Payment != Captured

from same semantic rule.

This avoids separate hand-written explanation logic.

====================================================================== 56. TRACEABILITY

Semantic element may link to:

requirement
policy
generated tests
runtime guard
tool
documentation

This creates machine-readable traceability.

Compare with traditional requirements traceability systems.

====================================================================== 57. CHANGE IMPACT

When semantic rule changes, compiler knows derivative artifacts.

This is stronger than text search.

Research whether MDE already provides this strongly.

====================================================================== 58. AGENT CONTEXT COMPRESSION

One semantic model may present rule compactly.

Instead of agent reading:

backend guard
frontend guard
tests
docs
SQL

it reads:

one semantic contract

This may reduce context.

Measure actual tokens.

====================================================================== 59. MINIMUM SUFFICIENT SEMANTIC VIEW

Full semantic IR may be verbose.

Generate agent-facing projection:

state
legal transitions
requirements
obligations
dependencies

This may be much smaller.

====================================================================== 60. TOKENIZATION

Compare token cost of same rule expressed as:

source code
tests
docs
JSON IR
compact DSL
generated agent view

Use actual tokenizers where possible.

====================================================================== 61. ECONOMIC HYPOTHESIS

Potential savings:

fewer repository reads
fewer duplicated updates
fewer stale artifacts
fewer model calls
fewer repair loops
lower human review cost

Against:

model/compiler development
specification maintenance
generator maintenance
migrations
training

====================================================================== 62. UPFRONT COST

Model:

build semantic compiler
define IR
create generators
integrate CI

This cost may be high.

Estimate break-even.

====================================================================== 63. MAINTENANCE COST OF COMPILER

Generators themselves become critical infrastructure.

Need:

tests
versioning
backward compatibility
debugging

This cost must not be hidden.

====================================================================== 64. TARGET-LANGUAGE EVOLUTION

Language versions change.

Example:

C# adds native unions

Generator may need change.

Semantic IR should remain stable if possible.

====================================================================== 65. GENERATED BUGS

A generator bug may affect:

every generated artifact

This is correlated implementation failure.

Mitigation:

generator tests
golden files
differential generation
target compiler checks
independent conformance tests

====================================================================== 66. WRONG SPECIFICATION

Most dangerous scenario:

semantic rule itself is wrong

Then:

runtime
tests
docs
agent tools

may all agree incorrectly.

This is the key falsification concern.

====================================================================== 67. INDEPENDENT ORACLES

Need mechanisms outside semantic spec.

Examples:

hidden acceptance tests
business examples
formal invariants
production observations
human domain review
external regulation

Research architecture of independent verification.

====================================================================== 68. MUTATION TESTING OF SPECIFICATION

Could mutate semantic rules:

invert guard
remove state
weaken authority

Tests should fail.

This tests whether external tests independently validate spec.

====================================================================== 69. SPECIFICATION REVIEW

Semantic changes may deserve higher review than implementation changes.

Possible workflow:

semantic diff
    ->
domain review
    ->
compiler
    ->
generated changes

Research high-assurance configuration review.

====================================================================== 70. ROLE OF AI

AI may propose semantic changes.

But should agent be allowed to:

directly commit semantic authority?

Potential safer flow:

agent proposes
impact report generated
reviewer/authorized process approves

Investigate governance.

====================================================================== 71. SEMANTIC CHANGE AUTHORITY

Different semantic layers may require different approval:

state addition
policy change
authority change
evidence rule change

This could become governance-heavy.

Find minimal viable process.

====================================================================== 72. SPECIFICATION PROVENANCE

Each semantic rule may record:

source
rationale
issue
policy
actor
date

This may improve future agent comprehension.

====================================================================== 73. EXAMPLES AS SPECIFICATION

Could executable examples complement abstract semantic model?

Example:

Given Payment=Authorized
When Capture
Then Payment=Captured

Research example-driven specification.

====================================================================== 74. PROPERTY-BASED TESTING

Semantic model may generate properties:

no illegal transition
no duplicate refund
no shipment before captured payment

Property-based testing may provide stronger independent coverage.

====================================================================== 75. MODEL CHECKING

Semantic model may be translated to:

TLA+
Alloy
model checker

This could detect:

deadlocks
unreachable obligations
unsafe concurrency

Research whether generation is feasible/useful.

====================================================================== 76. SPECIFICATION LANGUAGE EXPRESSIVENESS

Danger:

too weak -> cannot express important rules

too strong -> becomes general-purpose language, difficult to analyze

Find useful bounded expressiveness.

====================================================================== 77. DECIDABILITY / ANALYZABILITY

A restricted requirement AST may enable:

dependency extraction
reachability
migration analysis

Arbitrary code callbacks would destroy this.

Research tradeoff.

====================================================================== 78. ESCAPE TO HOST CODE

Some rules may require complex computation.

Potential construct:

external predicate

But then compiler cannot fully inspect semantics.

Track opaque dependencies explicitly.

====================================================================== 79. OPAQUE SEMANTIC ESCAPES

Metric:

percentage of consequential rules implemented as opaque host-language code

High value may undermine benefits.

====================================================================== 80. SEMANTIC COVERAGE

Define:

consequential business rules represented in semantic model
----------------------------------------------------------
all consequential business rules

Call:

Semantic Coverage

Need a way to estimate denominator.

====================================================================== 81. DERIVATION COVERAGE

How many derivative artifacts come from semantic source?

Examples:

runtime guard
tests
docs
tools
policy explanation

High coverage may reduce drift.

====================================================================== 82. DRIFT INCIDENT RATE

Measure:

cross-artifact inconsistencies detected
---------------------------------------
semantic changes

Compare architectures.

====================================================================== 83. DUPLICATE RULE COUNT

Count independent manual copies of same rule.

Hypothesis:

semantic compilation reduces this.

====================================================================== 84. RULE FAN-OUT

One semantic rule may influence:

12 artifacts

Without generation:

12 manual updates

With generation:

1 semantic edit + regeneration

Measure change amplification.

====================================================================== 85. CHANGE ERROR RATE

When rule changes, measure:

derivative artifacts missed
stale behavior
repair loops

====================================================================== 86. AGENT SEARCH COST

Agent asked:

"Change refund eligibility."

Conventional:

search code/tests/docs/tool schemas

Semantic:

modify rule
impact report generated

Measure:

files read
tokens
searches

====================================================================== 87. SMALLER MODEL HYPOTHESIS

A smaller model may safely perform routine change if compiler identifies all affected artifacts.

Compare:

frontier model + conventional repository

with:

smaller model + semantic compiler

Measure cost/correctness.

====================================================================== 88. CROSS-TEAM CONSISTENCY

Different teams may work in:

web
backend
agent automation

Shared semantic source could prevent divergent interpretations.

Research organizational benefits.

====================================================================== 89. MONOREPO VS MULTIREPO

Semantic spec may be shared across repositories via:

versioned package/artifact

Potential drift between versions.

Research schema-package governance.

====================================================================== 90. DISTRIBUTED SEMANTIC VERSIONS

Different services may temporarily run:

spec v7
spec v8

Need compatibility strategy.

This complicates "single source" in distributed systems.

====================================================================== 91. ROLLING DEPLOYMENTS

During deployment:

old and new semantic versions coexist.

Transitions/events may cross versions.

Research protocol evolution.

====================================================================== 92. BACKWARD COMPATIBILITY

Semantic change may need:

dual-read
dual-write
version negotiation

Generator cannot magically solve compatibility.

====================================================================== 93. PRODUCT EXPERIMENTATION

Do not encode transient experiment rules into durable semantic core unless necessary.

Otherwise semantic compiler reduces pivotability.

Connect to startup research.

====================================================================== 94. EXPERIMENTAL SEMANTIC LAYER

Possible:

experimental semantic rules

with weaker guarantees / short lifecycle.

Investigate if this helps avoid over-centralization.

====================================================================== 95. GENERATED VS INTERPRETED RUNTIME

Semantic model can be:

A. compiled to target code

B. interpreted by generic runtime

Compare:

performance
debuggability
deployment
versioning
drift

====================================================================== 96. HYBRID

Possible:

compile types/contracts
interpret policies

Research benefits.

====================================================================== 97. REFERENCE MONITOR ANALOGY

If all consequential actions consult semantic runtime:

it resembles reference monitor.

Generated artifacts must not become alternate bypass paths.

====================================================================== 98. SECURITY

If semantic compiler generates authority rules incorrectly:

broad security failure

Need rigorous generator assurance.

====================================================================== 99. AUDITABILITY

Semantic compiler can trace:

rule
    ->
generated guard
    ->
action decision
    ->
policy version

This could significantly improve audits.

====================================================================== 100. REGULATORY CHANGE

Policy changes often require updating:

application logic
docs
tests
workflows

Semantic compilation may reduce compliance drift.

Use as case study.

====================================================================== 101. EXPERIMENT A — RULE CHANGE

Rule:

Refund allowed within 30 days

Change to:

14 days

Compare:

A. conventional competent system

B. semantic compiler

Measure:

artifacts updated
misses
tokens
tests
agent calls

====================================================================== 102. EXPERIMENT B — STATE ADDITION

Add:

PaymentState.Disputed

Measure all dependent interpretations.

Semantic compiler should surface/generate changes.

====================================================================== 103. EXPERIMENT C — STATE SPLIT

Approved ->

ConditionallyApproved
FullyApproved

Measure semantic redistribution and derivative artifacts.

====================================================================== 104. EXPERIMENT D — POLICY CHANGE

Fraud evidence freshness:

24h -> 4h

Measure:

runtime rule
agent tools
docs
tests
obligations

====================================================================== 105. EXPERIMENT E — TOOL CHANGE

Rename/restructure transition.

Measure:

agent tool schema
runtime
docs

Does semantic source eliminate mismatch?

====================================================================== 106. EXPERIMENT F — WRONG SPEC

Intentionally encode wrong rule.

Observe:

generated artifacts become consistently wrong

Measure whether independent tests detect it.

This is essential.

====================================================================== 107. EXPERIMENT G — GENERATOR BUG

Introduce generator defect.

Measure blast radius and detection.

====================================================================== 108. EXPERIMENT H — MANUAL ARTIFACT EDIT

Modify generated/runtime artifact without spec.

Build should detect drift.

====================================================================== 109. EXPERIMENT I — AGENT TASK

Ask agent:

change shipment eligibility

Compare context/search/cost.

====================================================================== 110. EXPERIMENT J — LONGITUDINAL

Run:

50 semantic changes

Measure:

drift incidents
duplicate rules
context growth
generated artifact errors

====================================================================== 111. METRICS

Track:

Specification Drift Incidents
Duplicate Semantic Rule Count
Semantic Coverage
Derivation Coverage
Stale Artifact Count
Rule Change Fan-Out
Missed Artifact Rate
Agent Files Read
Context Tokens
Tool Calls
Repair Cycles
Human Review Time
Cost per Correct Semantic Change

====================================================================== 112. SPECIFICATION DRIFT RATE

Define:

inconsistent derivative artifacts
---------------------------------
semantic changes

Compare systems.

====================================================================== 113. DERIVATION COVERAGE

Define:

derivative artifacts generated/validated from semantic source
-------------------------------------------------------------
derivative artifacts representing semantic rules

Higher may reduce drift.

====================================================================== 114. INDEPENDENT VALIDATION COVERAGE

Need metric:

semantic rules independently checked
-------------------------------
semantic rules

High derivation without independent validation may create false confidence.

====================================================================== 115. ECONOMIC MODEL

Conventional cost:

repeated search
multiple edits
stale artifacts
repair
defects

Semantic compiler cost:

initial tooling
semantic edit
regeneration
compiler maintenance
independent validation

Calculate cumulative break-even.

====================================================================== 116. BREAK-EVEN

Model over:

10
100
1,000
10,000

semantic changes.

Question:

At what change volume/system lifespan does semantic compilation pay off?

====================================================================== 117. STARTUP VS ENTERPRISE

Startup:

high change rate
high pivot risk
small team

Enterprise:

large codebase
many teams
compliance
slower semantics

Benefits/costs may differ.

====================================================================== 118. HIGH-CONSEQUENCE VS LOW-CONSEQUENCE

Semantic compiler likely more justified for:

payments
healthcare
compliance
deployment
security

Less justified for:

visual presentation
simple content

Develop threshold guidance.

====================================================================== 119. COUNTERARGUMENTS

Actively test:

1. This is just model-driven engineering, which has known adoption problems.
2. One wrong spec creates systemic failure.
3. Generator maintenance exceeds drift savings.
4. Strong types/tests already solve enough.
5. Good documentation and code review are cheaper.
6. Modern AI can update multiple representations reliably.
7. Generated artifacts are harder to debug.
8. Semantic DSL becomes a second programming language.
9. Developers bypass generator under deadline pressure.
10. Generated code creates noisy diffs.
11. Distributed versioning destroys single-source assumptions.
12. Business semantics are too nuanced for DSLs.
13. Specification becomes bottleneck.
14. Product iteration slows.
15. Independent validation still requires duplicate expression, reintroducing drift.

====================================================================== 120. FALSIFICATION CONDITIONS

The hypothesis should be weakened if:

drift rates are already low in competent conventional systems

or:

generated artifacts do not materially reduce missed updates

or:

wrong-spec correlated failures dominate

or:

semantic compiler maintenance exceeds savings

or:

agents still need same repository context

or:

simpler schema/policy/test generation achieves most benefit.

====================================================================== 121. EXISTING SYSTEMS TO STUDY

Research:

Model-Driven Engineering
UML executable models
state-machine generators
Yakindu/itemis CREATE
SCXML
protobuf
OpenAPI generators
GraphQL schema tooling
OPA
Cedar
XACML
TLA+/PlusCal
Alloy
DSL workbenches
JetBrains MPS
Xtext
language-oriented programming
database schema compilers
protocol compilers
workflow compilers

====================================================================== 122. AI-SPECIFIC SYSTEMS

Search current work on:

generating tool schemas from APIs
agent tool registries
typed tool systems
policy-controlled agents
executable agent specifications
agent runtime contracts
code generation for AI tools
semantic context generation

Determine whether unified semantic compilation for AI already exists.

====================================================================== 123. SOURCE QUALITY

Prefer:

foundational MDE research
primary empirical studies
official compiler/tool docs
formal methods literature
requirements traceability studies
current AI-agent research

Distinguish:

established result
empirical evidence
architectural inference
speculation

====================================================================== 124. REQUIRED OUTPUT

Produce:

1. Executive verdict
2. Definition of specification drift
3. Evidence that duplicated semantics drift
4. Model-driven engineering comparison
5. Executable-spec comparison
6. DSL comparison
7. Schema/protocol compiler comparison
8. Policy-as-code comparison
9. Generated-test analysis
10. Documentation generation analysis
11. Agent tool-generation analysis
12. Prompt-generation analysis
13. Runtime-guard generation analysis
14. Planning-graph generation analysis
15. Impact-analysis generation
16. Semantic migration analysis
17. Round-trip engineering lessons
18. Generated/handwritten boundary recommendation
19. Specification expressiveness recommendation
20. Semantic coverage model
21. Drift metrics
22. AI context/token implications
23. Smaller-model hypothesis
24. Wrong-spec risk
25. Generator-bug risk
26. Independent-validation model
27. Security/audit implications
28. Startup/enterprise differences
29. Counterarguments
30. Proposed experiments
31. Economic model
32. Break-even analysis
33. What is already established
34. What remains speculative
35. Architecture changes recommended
36. Minimum viable semantic compiler scope
37. Final verdict

====================================================================== 125. FINAL VERDICT FORMAT

Answer:

Does duplicate representation materially cause semantic drift? Strong evidence / Moderate evidence / Weak evidence / No evidence

Does generation from one semantic source reduce drift? Strong evidence / Moderate evidence / Weak evidence / No evidence

Does semantic compilation plausibly reduce AI-agent context cost? Strong / Moderate / Weak / Unclear

Does it plausibly enable smaller models? Strong / Moderate / Weak / Unclear

Is the concept fundamentally: Model-driven engineering / executable specification / meaningfully distinct / hybrid

Most valuable generated artifact: ...

Most dangerous correlated-failure mode: ...

Best independent validation mechanism: ...

Most important lesson from historical MDE: ...

Minimum semantic scope worth centralizing: ...

Biggest economic opportunity: ...

Biggest adoption risk: ...

Most important missing experiment: ...

====================================================================== 126. RESEARCH STANDARD

Be skeptical.

Do not treat "single source of truth" as automatically beneficial.

Do not count generated consistency as correctness.

Do not let tests generated from the same specification serve as the only oracle.

Do not ignore the historical limitations of model-driven engineering.

Do not turn the semantic model into a universal programming language.

Do not assume all derivative artifacts should be generated.

Do not ignore compiler/generator maintenance cost.

The central question is:

Can a narrowly scoped authoritative semantic specification eliminate enough
duplicated business meaning that runtime behavior, tests, policies,
documentation, and AI-agent tools remain synchronized — while avoiding the
correlated-failure and complexity traps that have historically limited
model-driven systems?