import { useState } from 'react' import { Target, AlertTriangle, RefreshCw, BarChart2, ShieldCheck } from 'lucide-react' interface EvaluationResult { decision: string objective_id: string selected_action: { action_id: string description: string expected_value: number expected_cost: number risk_score: number net_expected_utility: number } ranked_candidates_count: number verification_status: string } interface OutcomeRecord { action_id: string predicted_outcome: string actual_outcome: string success: boolean prediction_variance: number evidence_refs: string[] } const SAMPLE_EVALUATION: EvaluationResult = { decision: 'EXECUTE_NEXT_BEST_ACTION', objective_id: 'obj_reduce_cloud_cost_01', selected_action: { action_id: 'act_downscale_idle_nodes', description: 'Downscale idle worker nodes during off-peak hours (01:00-05:00 UTC)', expected_value: 120.0, expected_cost: 5.0, risk_score: 0.10, net_expected_utility: 114.0 }, ranked_candidates_count: 2, verification_status: 'CONSTRAINTS_VERIFIED' } const SAMPLE_OUTCOMES: OutcomeRecord[] = [ { action_id: 'act_downscale_idle_nodes', predicted_outcome: '22% cost reduction with 100% uptime', actual_outcome: '24% cost reduction with 100% uptime verified', success: true, prediction_variance: 0.02, evidence_refs: ['ev_billing_audit_202'] } ] export default function ObjectiveEngineViewer() { const [evalResult] = useState(SAMPLE_EVALUATION) const [outcomes, setOutcomes] = useState(SAMPLE_OUTCOMES) const [isEvaluating, setIsEvaluating] = useState(false) const handleEvaluate = () => { setIsEvaluating(true) setTimeout(() => { const newOutcome: OutcomeRecord = { action_id: `act_opt_${Math.floor(100 + Math.random() * 900)}`, predicted_outcome: 'Optimal query execution path caching', actual_outcome: '1.14 μs latency reduction verified', success: true, prediction_variance: 0.01, evidence_refs: [`ev_outcome_${Math.floor(100 + Math.random() * 900)}`] } setOutcomes(prev => [newOutcome, ...prev]) setIsEvaluating(false) }, 600) } return (
Closed Decision-Control Loop

Objective Engine & Outcome Memory

AI agents generate intentions. Bartholomew evaluates constraints, ranks expected utility vs. risk, and verifies real-world outcomes against predictions to build a compounding proprietary outcome memory.

{/* Closed Loop Architecture Flow */}
THE CLOSED DECISION-CONTROL LOOP
1. OBJECTIVE Goal & Limits
2. PERCEIVE Observe State
3. REASON Utility vs Risk
4. VERIFY Constraints
5. ACT Execute
6. OUTCOME Real World
7. LEARN Store Memory
8. RE-EVALUATE Closed Loop
{/* Evaluation & Outcome History Grid */}
{/* Left Column: Selected Action & Utility Ranking */}

Selected Action & Utility Evaluation

Objective ID: {evalResult.objective_id}
Decision: {evalResult.decision}
Selected Action Description:

{evalResult.selected_action.description}

Expected Value ${evalResult.selected_action.expected_value}
Expected Cost ${evalResult.selected_action.expected_cost}
Net Utility +{evalResult.selected_action.net_expected_utility}
{/* Constraint Violation Rejection Note */}
Candidate act_migrate_to_spot_instances was rejected because it violated the zero_downtime_guarantee constraint.
{/* Right Column: Persistent Outcome Memory */}

Proprietary Outcome Memory (Predicted vs. Actual)

{outcomes.map((rec, idx) => (
{rec.action_id} VERIFIED_MATCH
Predicted: {rec.predicted_outcome}
Actual Outcome: {rec.actual_outcome}
))}
Decision-Outcome Memory Size: {outcomes.length} Verified Records
) }