AI RESEARCH MISSION — STATE-CONSTRAINED MODULARITY, CONTEXT REDUCTION, AND AGENT EXECUTION COST

ROLE

Act as a combined:

• AI-agent systems researcher
• software architecture researcher
• modularity / information-hiding researcher
• programming-language researcher
• compiler and static-analysis researcher
• AI inference-cost researcher
• empirical software-engineering researcher

Your task is to investigate whether explicit state-transition architecture can improve software modularity in a way that materially reduces the context an AI coding or operational agent must consume.

The economic question is central:

Can state-constrained modular architecture reduce token consumption,
model calls, tool calls, repository reads, repair loops, and ultimately
dollars spent per correct agent task?

Do NOT assume the hypothesis is true.

Try to falsify it.

Separate:

architectural correctness benefits

from:

token/context benefits

from:

total economic benefits.

====================================================================== PRIMARY HYPOTHESIS

Investigate this hypothesis:

Explicit state, legal transitions, invariants, capabilities, obligations,
and semantic dependencies may create stronger module boundaries.

Those boundaries may allow an agent to work with a much smaller semantic slice of the software system.

Instead of providing an agent with:

a large repository
many implementation files
broad documentation
database schemas
scattered business rules
tests
API handlers
historical conventions

the system may be able to provide:

the relevant module
its current authoritative state
its legal transitions
its public transition interface
required evidence
available capabilities
outstanding obligations
declared dependencies
contracts for neighboring modules

If true, this could make state-constrained architecture function as:

semantic partitioning
    +
context compression
    +
action-space reduction

====================================================================== CORE RESEARCH QUESTION

Does explicit state-transition architecture make it easier to separate commercial software into semantically independent modules such that an AI agent can correctly modify or operate one module while loading substantially less context from the rest of the system?

If yes:

How much context can be eliminated?

How many tokens can be saved?

How much money can be saved?

Does the improvement persist after including:
    semantic schemas
    interfaces
    dependency metadata
    compiler feedback
    cross-module queries
    additional tool calls?

Does correctness improve at the same time?

At what scale does the approach become economically meaningful?

====================================================================== IMPORTANT DISTINCTION

Do not assume:

smaller source module = smaller agent context.

A physically small module may still require understanding many external rules.

Likewise:

a larger module with strong semantic contracts may require very little
external context.

Therefore study:

SEMANTIC CONTEXT SIZE

rather than merely:

lines of code per module.

====================================================================== PART 1 — RESEARCH WHAT SOFTWARE MODULARITY ALREADY TELLS US

Research established work on:

• information hiding
• modular programming
• separation of concerns
• cohesion and coupling
• abstract data types
• domain-driven design bounded contexts
• functional core / imperative shell
• object capability systems
• algebraic data types
• state machines
• typestate
• protocol/state types
• session types
• effect systems
• actor systems
• event sourcing
• workflow systems
• microservices
• modular monoliths
• package/module systems
• component contracts
• API design

Focus on mechanisms that reduce the amount of information a developer must understand in order to safely change one part of a system.

Ask:

Is this analogous to what AI coding agents need?

Do not simply assume human cognitive modularity transfers directly to LLM context efficiency.

Identify where the analogy is justified and where it is speculative.

====================================================================== PART 2 — DEFINE SEMANTIC MODULE

Develop a precise working definition of a semantic module.

Candidate definition:

A semantic module owns authoritative state and exposes changes to that
state only through a declared transition boundary.

A module may contain:

owned subject types
authoritative state families
legal transitions
invariants
capability derivation
obligations
trusted events
effect ports
public claims
version information
explicit dependencies

It should hide implementation details that external agents do not need.

Example:

Payment Module

Owns: PaymentLifecycle RefundExecution Payment evidence payment effects

Exposes: CapturePayment RequestRefund ReconcileRefund payment capabilities trusted payment events

Does NOT expose: arbitrary mutable Payment.Status raw database mutation Stripe SDK internals unrelated order internals

====================================================================== PART 3 — MODULE OWNERSHIP OF STATE

Investigate whether authoritative-state ownership naturally defines module boundaries.

Test this rule:

One authoritative state family should have one semantic owner.

Other modules may:

observe it
depend on it
react to events from it
request transitions through it

but should not mutate it directly.

Ask whether this reduces coupling.

Compare:

CONVENTIONAL:

Order service changes Payment.Status
Payment service changes Order.Status
Shipping code checks customer flags
UI writes approval fields
SQL jobs mutate status columns

against:

STATE-OWNED:

Payment owns PaymentState
Order owns OrderState
Customer owns CustomerState
Shipment owns ShipmentState

Cross-module operations occur through:

transitions
events
capabilities
obligations
coordinators

====================================================================== PART 4 — CROSS-MODULE CONTRACTS

Determine the minimum information one module must expose to another.

Candidate semantic contract:

Module identity

exported states / claims

exported trusted events

requestable transitions

capability requirements

effect contracts

obligations relevant to consumers

version/hash

Avoid exposing internal implementation unnecessarily.

Ask:

Could an AI agent modify the Payment module while receiving only:

    Payment semantic contract
    Payment implementation
    contracts of direct dependencies

instead of the complete repository?

====================================================================== PART 5 — CONTEXT GRAPH

Model the repository as a semantic dependency graph.

Nodes may include:

Module
StateCase
Transition
Capability
Obligation
Claim
Event
Policy
Effect

Edges:

owns
requires
produces
observes
authorizes
satisfies
triggers
dependsOn

For a requested change, calculate the transitive semantic impact.

Then construct the smallest relevant context slice.

Example task:

"Add partial refunds."

Potential context slice:

Payment module
RefundExecution state
Refund capability
payment policy
payment effect contract
Order contract if refund changes order eligibility

Do NOT automatically include:

Shipping implementation
Customer UI
unrelated reporting
authentication internals

Test whether semantic dependency closure can safely determine this slice.

====================================================================== PART 6 — DEFINE MINIMUM SUFFICIENT AGENT CONTEXT

Create a measurable concept:

Minimum Sufficient Semantic Context (MSSC)

Definition:

The smallest context set from which an agent can complete a requested
task with the required semantic correctness.

Measure MSSC using:

tokens
bytes
files
semantic nodes
modules

Distinguish:

Actual Context Supplied

from:

Minimum Sufficient Context

and:

Full Repository Context.

====================================================================== PART 7 — CONTEXT REDUCTION RATIO

Define:

    tokens in minimum sufficient semantic context

CSR = ------------------------------------------------ tokens in conventional task context

Call this:

Context Size Ratio (CSR)

or propose a better name.

Also calculate:

Context Reduction = 1 - CSR

Example:

conventional context:
    80,000 tokens

semantic module context:
    12,000 tokens

CSR:
    0.15

context reduction:
    85%

Do NOT invent expected percentages.

Measure them experimentally.

====================================================================== PART 8 — TOKEN ECONOMICS

For each experiment record actual model usage where possible:

input tokens
output tokens
cached input tokens
reasoning tokens if exposed
model calls

Calculate:

input-token cost
output-token cost
total inference cost

Use actual current model pricing when conducting the research.

Record:

provider
model
pricing date
input price
cached input price
output price

Do not assume one model or one vendor.

Build calculations so prices can be substituted later.

====================================================================== PART 9 — COST PER CORRECT TASK

Token cost alone is insufficient.

Calculate:

                total agent cost across attempts

CostCorrect = ----------------------------------------------- number of semantically correct completions

Include where measurable:

inference
tool usage
retrieval
test/CI execution
human intervention
repair attempts

A modular architecture that saves tokens but produces more integration errors may be economically worse.

Likewise, an architecture that uses slightly more tokens but greatly reduces failure may be economically superior.

====================================================================== PART 10 — MODULARITY EXPERIMENT

Construct the same domain system in at least two forms.

A. CONVENTIONAL / IMPLICIT SYSTEM

Characteristics:

shared mutable entities
status fields/enums
rules distributed through services
cross-module database access
broad repository knowledge required

B. STATE-CONSTRAINED MODULAR SYSTEM

Characteristics:

explicit state ownership
explicit transitions
protected mutation
module contracts
explicit dependencies
capabilities
obligations
semantic dependency graph

Keep business functionality equivalent.

Do not intentionally make the conventional implementation bad.

It should represent competent normal commercial software.

====================================================================== PART 11 — DOMAIN SIZE

Use a domain large enough for modularity to matter.

Suggested modules:

Customer
Order
Payment
Fraud/Risk
Shipment
Refund
Notification

Potential states:

Customer: Unverified Verified Blocked

Order: Draft Submitted Approved Cancelled Completed

Payment: None Authorized Captured Refunded

Shipment: NotReady Ready Shipped Delivered

RefundExecution: Idle Requested Succeeded Failed OutcomeUnknown

Include cross-module rules.

Example:

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

====================================================================== PART 12 — EXPERIMENT: LOCAL CHANGE

Task:

Add a new local Payment state or transition.

Example:

Payment gains PartiallyRefunded.

Measure what an agent needs to inspect.

Conventional system:

repository searches
files read
context tokens
tests
repair loops

State-modular system:

Payment module
semantic impact report
direct dependency contracts
affected consumers

Question:

Does semantic ownership contain the change?

====================================================================== PART 13 — EXPERIMENT: CROSS-MODULE CHANGE

Task:

Allow shipping for a new order category only when payment and fraud
conditions are satisfied.

This intentionally crosses boundaries.

Measure whether semantic modularity still reduces context or whether cross-module dependencies eliminate the advantage.

This experiment is important for falsification.

====================================================================== PART 14 — EXPERIMENT: STATE SPLIT

Use:

Approved

split into:

ConditionallyApproved
FullyApproved

Measure:

modules touched
semantic dependencies surfaced
context tokens
files read
repair cycles
missed assumptions

Compare full repository search against semantic dependency slicing.

====================================================================== PART 15 — EXPERIMENT: NEW BUSINESS RULE

Example:

Block shipment when Customer becomes Blocked after payment capture.

Measure whether the agent can operate with:

Customer contract
Shipment contract
cross-module policy/coordinator

without needing internal implementations of unrelated modules.

====================================================================== PART 16 — EXPERIMENT: UNKNOWN EFFECT

Inject:

Refund API timeout

Compare whether the agent needs broad repository reasoning to determine safe recovery.

State-constrained system should expose:

RefundExecution = OutcomeUnknown

obligations:
    ReconcileRefund

legal frontier:
    no new refund
    reconcile/investigate

Measure context and token cost.

====================================================================== PART 17 — EXPERIMENT: MODULE INTERNAL REFACTOR

Change implementation without changing the semantic contract.

Example:

replace Payment gateway adapter

or:

change payment persistence implementation

Hypothesis:

no other module context should be necessary.

Test whether the agent can reliably perform such work using only:

module implementation
semantic contract
effect interface

This may be where modularity produces the largest context savings.

====================================================================== PART 18 — EXPERIMENT: CONTRACT CHANGE

Change an exported semantic contract.

Example:

Payment introduces PartiallyCaptured.

The semantic dependency graph should identify affected consumers.

Measure whether:

only dependent modules

must enter agent context.

Compare against broad repository retrieval.

====================================================================== PART 19 — CONTEXT LOADING STRATEGIES

Compare at least:

Strategy A: Entire repository / very broad retrieval

Strategy B: Conventional search-based retrieval

Strategy C: static module boundary retrieval

Strategy D: semantic dependency slice

Strategy E: semantic dependency slice + lazy expansion

Lazy expansion means:

start with the smallest semantic context
retrieve additional modules only when a declared dependency or compiler
diagnostic shows they are necessary.

Measure total tokens across the entire task, not just the first prompt.

====================================================================== PART 20 — LAZY CONTEXT EXPANSION

This may be a particularly important optimization.

Potential algorithm:

1. Parse task.
2. Map task to semantic subjects/modules.
3. Load their semantic contracts.
4. Load impacted implementation.
5. Calculate dependency closure.
6. Include direct consequential dependencies.
7. Execute.
8. If compiler/runtime exposes another dependency:
       fetch only that semantic slice.
9. Continue.

Compare against preloading all potentially related code.

====================================================================== PART 21 — TOKENIZATION ITSELF

Study actual tokenizer behavior.

Do not assume:

fewer characters = proportionally fewer tokens.

Measure token counts for:

source code
JSON semantic IR
compact semantic IR
generated contracts
natural-language documentation
tool schemas

Test different representations of the same semantic information.

Example:

Verbose JSON:

{
  "stateFamily": "PaymentLifecycle",
  "state": "Captured"
}

Compact representation:

PaymentLifecycle=Captured

Binary or highly compressed formats may save storage but are not necessarily useful to an LLM.

Find representations that optimize:

token count
readability
model comprehension
reliable generation

====================================================================== PART 22 — SEMANTIC CONTEXT COMPRESSION

Compare the token size required to convey the same business rule using:

A. source implementation

B. tests

C. prose documentation

D. semantic IR

E. generated compact agent view

Example rule:

Shipment can occur only when:
    Order Approved
    Payment Captured
    Customer Verified
    Shipment Ready

Measure tokenization of each representation.

Then test whether the model actually performs equally well with the shorter representation.

Compression that reduces understanding is not useful.

====================================================================== PART 23 — AGENT-SPECIFIC VIEW

Investigate generating a compact agent-facing representation from the richer IR.

The full IR may be ideal for compilers but unnecessarily verbose for inference.

Example:

FULL SEMANTIC MODEL: detailed IDs provenance policy metadata relations versions

AGENT SLICE:

Shipment S17

Current:
    Ready

Requires Ship:
    Order=Approved
    Payment=Captured
    Customer=Verified

Blocked because:
    Customer=Blocked

Available:
    HoldShipment
    RequestCustomerReview

Obligations:
    ResolveCustomerEligibility

Question:

Can a compact generated agent projection preserve semantic correctness
while drastically reducing tokens?

====================================================================== PART 24 — MODULE SUMMARIZATION WITHOUT DRIFT

Traditional AI repository summaries can become stale.

Investigate whether semantic summaries generated directly from the semantic IR avoid this problem.

Compare:

AI-written module summary

against:

compiler-generated semantic contract

Measure:

token size
completeness
drift after state changes
correctness for agent tasks

====================================================================== PART 25 — CROSS-MODULE INFORMATION HIDING

Test whether agents unnecessarily inspect implementation details when semantic contracts are available.

For each task record:

modules whose source was read
modules whose contract was read
modules actually modified

Define:

Implementation Exposure Ratio

    unrelated implementation tokens supplied

IER = -------------------------------------------- total implementation tokens supplied

Lower may be better if correctness is maintained.

====================================================================== PART 26 — TOKEN SAVINGS AT REPOSITORY SCALE

Model several repository sizes.

For example:

50k LOC
250k LOC
1M LOC
5M LOC

Do not assume agents ever load all LOC.

Instead estimate/measure typical retrieved context per task.

Then model semantic slices such as:

local module
module + 1 dependency
module + 3 dependencies
large cross-cutting change

Calculate distributions rather than one average.

====================================================================== PART 27 — ECONOMIC SCENARIOS

Using current verified model prices, calculate example economics for:

100 agent tasks/day
1,000 agent tasks/day
10,000 agent tasks/day

For each architecture calculate:

average input tokens/task
average output tokens/task
average retries/task
average model calls/task
correctness rate
cost per correct completion

Then:

daily cost
monthly cost
annual cost

Run multiple scenarios.

Example categories:

conservative savings
moderate savings
large savings

Do not fabricate savings.

Base scenarios on measured experiment results.

====================================================================== PART 28 — BREAK-EVEN ANALYSIS

State-constrained modularity has implementation cost.

Include:

domain modeling
semantic compiler/tooling
module boundary work
migrations
training
generated contracts
governance

Estimate:

upfront engineering cost

against:

recurring inference savings
maintenance savings
defect reduction

Calculate:

number of agent tasks to break even

and:

months to break even

under different usage levels.

====================================================================== PART 29 — SMALLER MODEL HYPOTHESIS

Test a potentially larger economic effect:

Does semantic modularity allow a smaller/cheaper model to perform a task
that otherwise requires a larger model?

Compare:

large model + broad repository context

against:

smaller model + constrained semantic module context

Measure:

semantic correctness
token usage
retries
cost per correct completion

If supported, model substitution may produce larger savings than token compression alone.

====================================================================== PART 30 — CACHE EFFECTS

Modular semantic contracts may be stable.

Investigate:

prompt caching
cached module contracts
stable semantic IR prefixes
incremental state/context updates

Measure whether modular architecture increases the percentage of context that can be cached across tasks.

This could materially change economics.

====================================================================== PART 31 — PARALLEL AGENTS

Explicit module boundaries may allow agents to work independently.

Test:

Agent A -> Payment
Agent B -> Shipment
Agent C -> Customer

with semantic contracts between them.

Compare with agents sharing a broad mutable code surface.

Measure:

context duplication
merge conflicts
semantic conflicts
coordination messages/tokens
rework

Question:

Does modularity reduce per-agent context enough to offset coordination cost?

====================================================================== PART 32 — MODULAR MONOLITH VS MICROSERVICES

Do not confuse semantic modularity with network deployment architecture.

Compare:

modular monolith with strong state ownership

against:

microservices with weak semantic boundaries

and possibly:

microservices with strong semantic boundaries.

Hypothesis:

semantic modularity may matter more to agent context than physical service
separation.

====================================================================== PART 33 — DATABASE COUPLING

Investigate one major source of broken modularity:

shared database tables

Test whether semantic modules can remain meaningful if multiple modules can directly mutate the same tables.

Likely architectural question:

Does authoritative state ownership require write ownership even when
physical storage remains shared?

Measure agent context implications.

====================================================================== PART 34 — WHAT CAN BREAK THE TOKEN SAVINGS?

Actively look for failure modes:

1. Semantic contracts become huge.

2. Cross-module dependencies are dense.

3. Most commercial changes are cross-cutting.

4. Agents repeatedly fetch hidden implementation anyway.

5. Generated IR uses more tokens than source summaries.

6. Compiler diagnostics create additional loops.

7. Module boundaries require duplicated semantic descriptions.

8. State machines fragment simple logic excessively.

9. Policy modules create indirect coupling.

10. Agent retrieval systems already achieve similar context reduction without state-constrained architecture.

11. Repository context is cheap because caching dominates.

12. Output/reasoning tokens dominate input-token savings.

13. Tool latency dominates inference cost.

14. Correctness gains are small.

15. Semantic compiler maintenance costs exceed token savings.

Design empirical tests for each.

====================================================================== PART 35 — MODULARITY METRICS

Create and evaluate useful metrics.

Possible metrics:

SEMANTIC MODULE CONTEXT SIZE Tokens needed to understand one module's consequential behavior.

SEMANTIC DEPENDENCY FAN-OUT Number of other modules directly affected by a semantic change.

SEMANTIC TRANSITIVE FAN-OUT Total impacted modules through dependency closure.

CONTEXT SIZE RATIO semantic task context / conventional task context

CONTEXT REDUCTION 1 - context size ratio

IMPLEMENTATION EXPOSURE implementation tokens outside the modified semantic module

CONTRACT-TO-IMPLEMENTATION RATIO contract tokens required / hidden implementation tokens

TOKENS PER CORRECT COMPLETION

DOLLARS PER CORRECT COMPLETION

MODULE ESCAPE RATE percentage of tasks that initially look local but require context outside the module.

LAZY EXPANSION RATE number of additional semantic slices fetched after initial context.

====================================================================== PART 36 — EXPERIMENTAL CONTROL REQUIREMENTS

Be careful about confounders.

Keep constant where possible:

business behavior
task wording
model
temperature/settings
tool availability
test suite
repository size
documentation quality

Randomize:

task order

Repeat runs.

Use fresh agent sessions where appropriate.

Do not let one architecture receive better documentation unless the documentation is itself part of the architecture being tested.

====================================================================== PART 37 — LONGITUDINAL TEST

Run repeated changes.

For example:

50 sequential domain changes

Measure whether module boundaries remain stable or decay.

Important question:

Does state ownership resist architectural erosion under repeated AI
modifications?

Track:

cross-module dependencies
module context size
tokens/task
semantic fan-out
direct mutation attempts
bypasses
architectural drift

The strongest hypothesis would predict that context requirements grow more slowly in the constrained modular system.

====================================================================== PART 38 — RESEARCH EXISTING AI-AGENT EVIDENCE

Search current primary research and official technical work on:

coding-agent context windows
repository retrieval
context engineering
codebase indexing
repository maps
agent memory
dependency-aware retrieval
graph-based code retrieval
compiler-guided coding agents
typed tool interfaces
program slicing
static analysis for LLM coding
modular code generation
multi-agent software engineering
context compression
inference cost optimization

Look specifically for empirical measures involving:

tokens
retrieved files
trajectory length
tool calls
cost
success rate

Distinguish:

direct evidence

from:

adjacent evidence

from:

architectural inference.

====================================================================== PART 39 — TOKENIZER EXPERIMENTS

Use actual tokenizers for representative current models where available.

Tokenize:

whole implementation files
module-level implementation
semantic contracts
IR
compact agent projections

Calculate actual token differences.

Do not estimate token counts from word or character counts when a tokenizer is available.

====================================================================== PART 40 — COST CALCULATOR

Produce a reusable cost model.

Inputs:

tasks per period
average uncached input tokens
average cached input tokens
output tokens
average retries
model input price
cached input price
output price
correctness rate

Outputs:

cost per attempt
expected cost per correct completion
daily cost
monthly cost
annual cost

Allow conventional and state-modular architectures to be compared side by side.

====================================================================== PART 41 — FALSIFICATION CRITERIA

Before seeing results, define conditions that would weaken or reject the economic hypothesis.

Examples:

Reject or substantially weaken the token-saving hypothesis if:

semantic context is not materially smaller

or:

additional semantic metadata offsets repository savings

or:

cross-module expansion occurs on most tasks

or:

correctness does not improve

or:

total cost per correct completion is not reduced

or:

upfront engineering cost has an impractical break-even period.

Define numerical thresholds only after pilot measurements provide a rational basis.

====================================================================== PART 42 — STRONG RESULT CRITERIA

Define what evidence would justify the claim:

"State-constrained modular architecture makes AI agents cheaper to run."

Require more than one small benchmark.

A strong result should show, across multiple realistic tasks:

materially smaller context
fewer input tokens
no reduction in semantic correctness
lower cost per correct completion
repeatability
persistence over sequential maintenance
acceptable upfront/break-even cost

An even stronger result would show:

smaller models achieving equivalent correctness.

====================================================================== PART 43 — REQUIRED OUTPUT

Produce a research report containing:

1. Executive conclusion

2. State of existing research

3. Theory connecting: state ownership modularity semantic dependency agent context size token usage cost

4. Definition of semantic module

5. Definition of Minimum Sufficient Semantic Context

6. Semantic dependency/context-slicing model

7. Experimental architecture

8. Tokenizer experiment design

9. Cost model

10. Cost-per-correct-completion model

11. Pilot experiments

12. Results tables

13. Module-local vs cross-module results

14. Smaller-model results if practical

15. Caching implications

16. Longitudinal implications

17. Counterevidence and failure modes

18. Break-even analysis

19. What is empirically supported

20. What remains speculative

21. Recommended next experiments

22. Final verdict

====================================================================== PART 44 — RESEARCH QUEUE

As research progresses, maintain a research queue.

For every meaningful finding:

ask what uncertainty it creates

add follow-up questions

rank them by:
    falsification value
    economic importance
    architectural importance
    experimental feasibility

Do not stop when the initial questions are answered.

Look for results that challenge the architecture.

====================================================================== FINAL QUESTION

The final question is not:

"Are state machines good?"

It is:

Can explicit ownership of consequential state and transitions create
semantic module boundaries strong enough that an AI agent can safely
operate on a small, mechanically selected portion of a software system?

And if so:

How many context tokens disappear?

How much total agent execution cost disappears?

How much does cost per correct completion improve?

At what software scale and agent volume does the additional architecture
pay for itself?

====================================================================== RESEARCH STANDARD

Be skeptical.

Do not turn architectural elegance into assumed economic benefit.

Measure actual tokenization.

Measure actual context retrieval.

Measure actual model cost.

Measure correctness.

Count retries.

Count cross-module escapes.

Include all semantic metadata in the token accounting.

Use current verified model prices for cost calculations.

Prefer primary research and reproducible experiments.

Clearly label:

measured result
sourced result
inference
hypothesis

The desired outcome is not confirmation.

The desired outcome is a credible answer to whether semantic modularity can become an inference-cost optimization technique for AI-operated software.