A TypeScript-based decision-scoring and risk-management system designed to evaluate, classify, and govern autonomous agent actions. The module intercepts proposed actions, scores them across multiple risk dimensions, classifies them into approval tiers, and supports human oversight and continuous threshold optimization.
When an autonomous agent proposes an action (file deletion, data export, deployment, etc.), the Scoring Module intercepts and transforms it into a structured DecisionObject. That object then flows through a multi-stage pipeline:
- Evaluation -- raw action metadata is parsed into a standardized format with resource estimates, authority scope, policy exposure, impact simulations, compliance forecasts, and strategic alignment assessments.
- Risk Scoring -- a composite risk score is computed across nine weighted dimensions that dynamically recalibrate based on system state and historical feedback.
- Classification -- the risk score is mapped to one of three outcomes: auto-approve, flag for review, or block. Threshold bands shift adaptively using violation trends, entropy, and preemptive risk signals.
- Human Override -- flagged decisions can be reviewed by authorized stakeholders who approve, reject, or escalate with structured rationale and contextual annotations. Override patterns feed back into the scoring and classification engines.
- Threshold Optimization -- reinforcement signals from real outcomes, human overrides, false positives, and missed violations drive gradual recalibration of risk boundaries. Every configuration change is version-controlled and reversible.
Raw Agent Action
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DecisionEvaluationFramework
|-- ImpactSimulationModule (forward simulation of downstream effects)
|-- ResourceAnalyzer (cost, bandwidth, opportunity-cost analysis)
|-- ComplianceEstimator (lifecycle compliance probability forecast)
|-- StrategicAlignmentModule (alignment with organizational goals/initiatives)
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DecisionObject
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PreemptiveDetectionLayer (pattern-based early risk escalation)
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RiskScoringEngine (multi-dimension composite risk score)
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ClassificationEngine (adaptive threshold classification)
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HumanOverrideInterface (stakeholder review, approval, rejection)
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ThresholdOptimizationEngine (reinforcement-driven threshold recalibration)
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DecisionBlockingAPI (end-to-end orchestration and enforcement)
The standardized data structure that all modules operate on. Contains the action type, intent, required resources, authority scope, policy exposure, projected impact, compliance forecast, strategic alignment assessment, resource analysis, human override record, and agent metadata.
Intercepts raw agent actions and transforms them into DecisionObject instances. Orchestrates intent extraction, resource calculation, authority mapping, policy exposure assessment, impact simulation, compliance estimation, and strategic alignment evaluation.
Runs lightweight forward simulations to estimate downstream consequences of a proposed action. Produces four metrics: real-world task impact, predictive synergy density, trust-weighted influence propagation, and cooperative intelligence evolution. Simulation assumptions are adjustable through bounded learning-rate updates.
Estimates direct resource usage and economic impact. Calculates computational cost, estimated financial expenditure, bandwidth utilization, opportunity tradeoff, projected opportunity cost of blocking, and economic efficiency. These values feed into risk scoring to balance cost burden against the cost of inaction.
Calculates the probability that an action will remain compliant across its full lifecycle (initiation, execution, persistence, termination). Uses policy schemas, authority graphs, and historical violation patterns to produce a probabilistic forecast. Model parameters are calibratable through historical feedback.
Evaluates proposed actions against declared organizational goals, active initiatives, and long-term cooperative impact objectives. Produces a structured assessment with per-goal, per-initiative, and per-objective alignment scores, plus a misalignment penalty that is fed into risk scoring as an additional dimension.
Learns recurring patterns tied to downstream failures or compliance issues and proactively raises risk for future similar actions. Generates composite signatures from action type, agent type, authority layer, permission profile, and policy exposure band. When accumulated failure rates exceed configurable thresholds, it recommends classification escalation.
Computes a context-aware composite risk score across nine dimensions:
- Operational risk
- Regulatory exposure
- Financial cost
- Reputational impact
- Cooperative system stability
- Predicted compliance probability
- Simulation impact
- Opportunity cost projection
- Strategic misalignment
Dimension weights are dynamically recalibrated on each call based on context, system state, and adaptive multipliers. Supports online recalibration through post-execution feedback loops.
Maps a decision score into one of three states -- auto-approve, review, or block -- using adaptive threshold bands. Thresholds shift continuously based on risk posture, entropy, violation trends, and preemptive risk lift. Includes preemptive escalation logic that can upgrade a classification when the detection layer signals elevated risk.
Provides a structured workflow for authorized stakeholders to approve, reject, or escalate flagged decisions. Features include:
- Stakeholder authorization with clearance levels (Observer, Reviewer, Approver, Admin)
- Override request creation, submission, and resolution
- Structured rationale capture with dimension-level disagreements
- Contextual annotations (policy references, risk observations, historical precedents, domain context, compliance notes, mitigation suggestions)
- Immutable audit trail of all override decisions
- Adaptation signal computation that aggregates override patterns for threshold feeding
Recalibrates risk boundaries using reinforcement signals from four sources: real outcomes, human overrides, false positives, and missed violations. Every adjustment is:
- Bounded -- constrained by configurable maximum shift per cycle
- Gradual -- applied through exponential moving averages and bounded learning rates
- Version-controlled -- each configuration change produces an immutable snapshot
- Reversible -- supports rollback to any previous version
Tracks error rate indicators (false positive rate, missed violation rate) and provides a full signal history summary.
Compares predictions against realized outcomes and applies calibrated updates to risk weighting, simulation assumptions, and compliance probability models. Operates across all three subsystems (scoring engine, simulation module, compliance estimator) to close the feedback loop.
The top-level orchestration layer that ties the full pipeline together. Accepts a raw agent action request, runs it through evaluation, scoring, and classification, then produces a comprehensive response including:
- Enforcement directives per target platform (OS policy, runtime gate, workflow approval)
- Governance audit entries with pass/warn/fail status
- A tamper-evidence hash for integrity verification
- A full explanation trace covering every pipeline stage
1. Raw action received via DecisionBlockingAPI.evaluateAction()
2. DecisionEvaluationFramework transforms it into a DecisionObject
- ImpactSimulationModule estimates downstream effects
- ResourceAnalyzer computes cost and opportunity metrics
- ComplianceEstimator forecasts lifecycle compliance
- StrategicAlignmentModule assesses organizational alignment
3. PreemptiveDetectionLayer checks for known failure patterns
4. RiskScoringEngine computes composite risk score (9 dimensions)
5. ClassificationEngine maps score to auto-approve / review / block
6. If flagged: HumanOverrideInterface manages stakeholder review
7. Override patterns and real outcomes feed into:
- ThresholdOptimizationEngine (threshold recalibration)
- HistoricalFeedbackIntegrator (model parameter updates)
- RiskScoringEngine adaptive multipliers
- Node.js (v18 or later)
- npm
npm installnpx tscnode dist/demo.jsThe demo walks through a complete lifecycle: intercepting an agent action, scoring it, classifying it, running human override scenarios (including authorization failures, rejections with annotations, and conditional approvals), computing adaptation signals, and running threshold optimization cycles with rollback.
src/
DecisionObject.ts Standardized decision data structure
DecisionEvaluationFramework.ts Action interception and transformation
ImpactSimulationModule.ts Forward simulation of downstream effects
ResourceAnalyzer.ts Resource and economic analysis
ComplianceEstimator.ts Lifecycle compliance probability forecast
StrategicAlignmentModule.ts Strategic alignment evaluation
PreemptiveDetectionLayer.ts Pattern-based preemptive risk detection
RiskScoringEngine.ts Multi-dimension composite risk scoring
ClassificationEngine.ts Adaptive threshold classification
HumanOverrideInterface.ts Stakeholder override workflow and audit
ThresholdOptimizationEngine.ts Reinforcement-driven threshold optimization
HistoricalFeedbackIntegrator.ts Prediction-vs-outcome feedback loop
DecisionBlockingAPI.ts End-to-end orchestration and enforcement
demo.ts Full pipeline demonstration
decisionBlockingDemo.ts DecisionBlockingAPI-focused demonstration
- Language: TypeScript (ES2022, NodeNext modules)
- Runtime: Node.js
- Dependencies: uuid (for unique identifier generation)