COMPUTATIONAL CODEX

AgentFlow Enterprise in Julia & Scheme.

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: numerical intelligence layer

Julia-style numerical modeling makes scoring, timing, simulation, optimization, and value estimates explicit enough to inspect, challenge, and improve.

Julia / lead intelligencespecification example
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))
end
Julia / workflow economicsspecification example
function 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)
end

Symbolic rules

Scheme: symbolic governance layer

Lisp-style symbolic reasoning expresses qualification rules, governance checks, state transitions, and audit semantics as small composable decisions.

Scheme / lead governancespecification example
#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]))
Scheme / lifecycle and auditspecification example
(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

Why Julia and Scheme?

The two languages describe complementary parts of a future intelligence layer without changing the production application stack.

Julia-style numerical modeling

Useful for numerical modeling, scoring, simulation, optimization, and AI evaluation.

Lisp-style symbolic reasoning

Useful for symbolic rules, governance logic, state machines, and composable reasoning.

Computational specification layer

Together they express a research direction for AgentFlow Enterprise's future intelligence layer.

System mapping

AgentFlow 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 ConceptJulia ModelScheme ModelProduction Status
Lead qualificationWeighted lead_scorequalified-lead?Implemented
Deal timing estimatedeal_timing_estimatenext-best-actionDemo estimate
AI outreach angleSignal ranking modelnext-best-actionDemo estimate
EU compliance filteringcompliance_weightcompliance-risk?Documented
Payment lifecycleLifecycle metricspayment-state-transitionImplemented
Trust Center controlsControl coverage scoretrust-control-statusDocumented
Audit trailEvent risk weightingaudit-eventPlanned
ROI dashboardroi_estimateApproval policy rulesPlanned
Workflow engineworkflow_valueComposable action rulesFuture
Enterprise SSOAdoption readiness modelAccess policy rulesFuture

Roadmap

From Codex to production intelligence

The codex separates what is public now from the capabilities that still require implementation, validation, governance, and operating evidence.

01 / Current

  • Public Lead Lab
  • PayPal subscription lifecycle
  • Trust Center
  • Technical Book
  • Public documentation

02 / Next

  • Audit logs export
  • ROI dashboard
  • AI evals
  • Workflow notifications
  • RBAC and auditor role

03 / Future

  • Julia-based scoring experiments
  • Scheme-style governance rules
  • Enterprise AI provider selection
  • Compliance-aware lead filtering
  • Deal timing intelligence
  • White-label agency layer

Research-grade architecture codex

Read the production architecture beside its computational models.

Use the Technical Book for implementation context and the Lead Lab for the current public intelligence demo.