Julia-style numerical modeling
Useful for numerical modeling, scoring, simulation, optimization, and AI evaluation.
COMPUTATIONAL CODEX
A research-grade computational codex expressing AgentFlow Enterprise as numerical models, symbolic rules, lead intelligence functions, payment lifecycle state machines, trust logic, and future workflow reasoning.
Specification maturity
This codex is a public technical specification and research artifact. The production platform remains implemented with the existing web, database, payment, and deployment stack. The Julia and Scheme examples are designed to clarify architecture, reasoning, and future computational models; they do not expose secrets or replace production services.
Numerical models
Julia-style numerical modeling makes scoring, timing, simulation, optimization, and value estimates explicit enough to inspect, challenge, and improve.
struct LeadScoreInput
fit::Float64
intent::Float64
urgency::Float64
data_quality::Float64
eu_compliance_risk::Float64
end
clamp01(value::Real) = clamp(Float64(value), 0.0, 1.0)
function compliance_weight(eu_compliance_risk::Real)::Float64
risk = clamp01(eu_compliance_risk)
return 1.0 - 0.45 * risk
end
function lead_score(input::LeadScoreInput)::Float64
weighted_signal =
0.34 * clamp01(input.fit) +
0.28 * clamp01(input.intent) +
0.22 * clamp01(input.urgency) +
0.16 * clamp01(input.data_quality)
return round(100 * weighted_signal *
compliance_weight(input.eu_compliance_risk); digits=1)
end
function deal_timing_estimate(
urgency::Real,
stakeholder_access::Real,
process_friction::Real,
)::Int
momentum = 0.55 * clamp01(urgency) +
0.45 * clamp01(stakeholder_access)
friction = 1.0 + 1.8 * clamp01(process_friction)
return round(Int, clamp(90 * friction / (0.35 + momentum), 7, 365))
endfunction roi_estimate(
hours_saved_per_month::Real,
hourly_cost::Real,
monthly_platform_cost::Real,
)::Float64
monthly_value = max(0.0, hours_saved_per_month) * max(0.0, hourly_cost)
cost = max(1.0, monthly_platform_cost)
return round((monthly_value - cost) / cost; digits=2)
end
function risk_adjusted_priority(
lead_score_value::Real,
expected_value::Real,
execution_risk::Real,
)::Float64
value_signal = log1p(max(0.0, expected_value)) / log(100_001)
priority = 0.62 * clamp01(lead_score_value / 100) +
0.38 * clamp01(value_signal)
return round(100 * priority * (1.0 - 0.6 * clamp01(execution_risk)); digits=1)
end
function workflow_value(
monthly_runs::Integer,
minutes_saved_per_run::Real,
hourly_cost::Real,
error_reduction::Real,
)::Float64
time_value = max(0, monthly_runs) * max(0.0, minutes_saved_per_run) / 60
quality_multiplier = 1.0 + 0.35 * clamp01(error_reduction)
return round(time_value * max(0.0, hourly_cost) * quality_multiplier; digits=2)
endSymbolic rules
Lisp-style symbolic reasoning expresses qualification rules, governance checks, state transitions, and audit semantics as small composable decisions.
#lang racket
(define lead
'((fit . 0.86)
(intent . 0.74)
(urgency . 0.68)
(data-quality . 0.92)
(eu-region? . #t)
(lawful-basis-recorded? . #t)
(human-review-required? . #f)))
(define (lead-ref lead key [fallback #f])
(define pair (assoc key lead))
(if pair (cdr pair) fallback))
(define (qualified-lead? lead)
(and (>= (lead-ref lead 'fit 0) 0.70)
(>= (lead-ref lead 'intent 0) 0.60)
(>= (lead-ref lead 'data-quality 0) 0.75)
(not (compliance-risk? lead))))
(define (compliance-risk? lead)
(and (lead-ref lead 'eu-region? #f)
(not (lead-ref lead 'lawful-basis-recorded? #f))))
(define (requires-human-review? lead)
(or (lead-ref lead 'human-review-required? #f)
(compliance-risk? lead)
(< (lead-ref lead 'data-quality 0) 0.75)))
(define (next-best-action lead)
(cond
[(requires-human-review? lead) 'route-to-human-review]
[(qualified-lead? lead) 'prepare-controlled-outreach]
[(>= (lead-ref lead 'intent 0) 0.45) 'request-more-context]
[else 'hold-and-monitor]))(define (payment-state-transition state event)
(match (list state event)
[(list 'pending 'approval-received) 'approved]
[(list 'approved 'capture-verified) 'active]
[(list 'active 'payment-failed) 'past-due]
[(list 'past-due 'payment-recovered) 'active]
[(list 'active 'cancellation-confirmed) 'cancelled]
[_ state]))
(define (trust-control-status evidence)
(cond
[(and (member 'owner-assigned evidence)
(member 'evidence-current evidence)
(member 'review-complete evidence)) 'operating]
[(member 'owner-assigned evidence) 'documented]
[else 'planned]))
(define (audit-event actor action subject outcome metadata)
`((event-type . audit-event)
(actor . ,actor)
(action . ,action)
(subject . ,subject)
(outcome . ,outcome)
(metadata . ,metadata)))Dual model
The two languages describe complementary parts of a future intelligence layer without changing the production application stack.
Useful for numerical modeling, scoring, simulation, optimization, and AI evaluation.
Useful for symbolic rules, governance logic, state machines, and composable reasoning.
Together they express a research direction for AgentFlow Enterprise's future intelligence layer.
System mapping
The status column refers to the current product or documented roadmap. The Julia and Scheme columns describe the codex model, not a production runtime claim.
| AgentFlow Concept | Julia Model | Scheme Model | Production Status |
|---|---|---|---|
| Lead qualification | Weighted lead_score | qualified-lead? | Implemented |
| Deal timing estimate | deal_timing_estimate | next-best-action | Demo estimate |
| AI outreach angle | Signal ranking model | next-best-action | Demo estimate |
| EU compliance filtering | compliance_weight | compliance-risk? | Documented |
| Payment lifecycle | Lifecycle metrics | payment-state-transition | Implemented |
| Trust Center controls | Control coverage score | trust-control-status | Documented |
| Audit trail | Event risk weighting | audit-event | Planned |
| ROI dashboard | roi_estimate | Approval policy rules | Planned |
| Workflow engine | workflow_value | Composable action rules | Future |
| Enterprise SSO | Adoption readiness model | Access policy rules | Future |
Roadmap
The codex separates what is public now from the capabilities that still require implementation, validation, governance, and operating evidence.
01 / Current
02 / Next
03 / Future
Research-grade architecture codex
Use the Technical Book for implementation context and the Lead Lab for the current public intelligence demo.