async function runETL(config: ETLConfig) { const source = createSource(config.source); const sink = createSink(config.destination); let processed = 0; let errors = 0; for await (const batch of source.stream(1000)) { const transformed = batch .map(record => { try { const clean = normalize(record); const valid = config.schema.parse(clean); return { ok: true, data: valid }; } catch (e) { errors++; deadLetter.send(record, e); return { ok: false }; } }) .filter(r => r.ok) .map(r => r.data); await sink.writeBatch(transformed); processed += transformed.length; metrics.gauge("etl.processed", processed); metrics.gauge("etl.errors", errors); } return { processed, errors }; } async function runETL(config: ETLConfig) { const source = createSource(config.source); const sink = createSink(config.destination); let processed = 0; let errors = 0; for await (const batch of source.stream(1000)) { const transformed = batch .map(record => { try { const clean = normalize(record); const valid = config.schema.parse(clean); return { ok: true, data: valid }; } catch (e) { errors++; deadLetter.send(record, e); return { ok: false }; } }) .filter(r => r.ok) .map(r => r.data); await sink.writeBatch(transformed); processed += transformed.length; metrics.gauge("etl.processed", processed); metrics.gauge("etl.errors", errors); } return { processed, errors }; }

resource "aws_ecs_service" "api" { name = "novostra-api-prod" cluster = aws_ecs_cluster.main.id task_definition = aws_ecs_task_definition.api.arn desired_count = 3 launch_type = "FARGATE" network_configuration { subnets = var.private_subnets security_groups = [aws_security_group.api.id] assign_public_ip = false } load_balancer { target_group_arn = aws_lb_target_group.api.arn container_name = "api" container_port = 8080 } service_registries { registry_arn = aws_service_discovery_service.api.arn } deployment_circuit_breaker { enable = true rollback = true } } resource "aws_ecs_service" "api" { name = "novostra-api-prod" cluster = aws_ecs_cluster.main.id task_definition = aws_ecs_task_definition.api.arn desired_count = 3 launch_type = "FARGATE" network_configuration { subnets = var.private_subnets security_groups = [aws_security_group.api.id] assign_public_ip = false } load_balancer { target_group_arn = aws_lb_target_group.api.arn container_name = "api" container_port = 8080 } service_registries { registry_arn = aws_service_discovery_service.api.arn } deployment_circuit_breaker { enable = true rollback = true } }

class PatientDataPipeline: def __init__(self, ehr_client, fhir_endpoint): self.ehr = ehr_client self.fhir = fhir_endpoint self.audit = AuditLogger("hipaa") async def sync_encounters(self, patient_id): self.audit.log("access", patient_id) raw = await self.ehr.get_encounters(patient_id) normalized = [ self.to_fhir_encounter(e) for e in raw ] await self.fhir.batch_upsert(normalized) return normalized def to_fhir_encounter(self, encounter): return { "resourceType": "Encounter", "status": encounter["status"], "class": encounter["visit_type"], "subject": encounter["patient_ref"], "period": { "start": encounter["admitted_at"], "end": encounter["discharged_at"], }, } class PatientDataPipeline: def __init__(self, ehr_client, fhir_endpoint): self.ehr = ehr_client self.fhir = fhir_endpoint self.audit = AuditLogger("hipaa") async def sync_encounters(self, patient_id): self.audit.log("access", patient_id) raw = await self.ehr.get_encounters(patient_id) normalized = [ self.to_fhir_encounter(e) for e in raw ] await self.fhir.batch_upsert(normalized) return normalized def to_fhir_encounter(self, encounter): return { "resourceType": "Encounter", "status": encounter["status"], "class": encounter["visit_type"], "subject": encounter["patient_ref"], "period": { "start": encounter["admitted_at"], "end": encounter["discharged_at"], }, }

async function runAgentWorkflow(task: Task) { const planner = new PlannerAgent({ model: "claude-sonnet-4-20250514", tools: [searchTool, sqlTool, emailTool], }); const plan = await planner.decompose(task); const results: StepResult[] = []; for (const step of plan.steps) { const agent = AgentPool.acquire(step.type); const result = await agent.execute(step, { context: results, timeout: 30_000, retries: 2, }); results.push(result); if (result.requiresApproval) { await HumanInTheLoop.request({ step, result, assignee: task.owner, }); } } return planner.synthesize(results); } async function runAgentWorkflow(task: Task) { const planner = new PlannerAgent({ model: "claude-sonnet-4-20250514", tools: [searchTool, sqlTool, emailTool], }); const plan = await planner.decompose(task); const results: StepResult[] = []; for (const step of plan.steps) { const agent = AgentPool.acquire(step.type); const result = await agent.execute(step, { context: results, timeout: 30_000, retries: 2, }); results.push(result); if (result.requiresApproval) { await HumanInTheLoop.request({ step, result, assignee: task.owner, }); } } return planner.synthesize(results); }

class RetrievalPipeline: def __init__(self, embedder, vector_db, reranker): self.embedder = embedder self.vector_db = vector_db self.reranker = reranker async def retrieve(self, query, top_k=10): embedding = await self.embedder.embed(query) candidates = await self.vector_db.search( vector=embedding, top_k=top_k * 3, filter={"status": "published"}, ) reranked = await self.reranker.rank( query=query, documents=[c.text for c in candidates], top_k=top_k, ) return [ RetrievedDoc( text=doc.text, score=doc.score, source=doc.metadata["source"], ) for doc in reranked ] class RetrievalPipeline: def __init__(self, embedder, vector_db, reranker): self.embedder = embedder self.vector_db = vector_db self.reranker = reranker async def retrieve(self, query, top_k=10): embedding = await self.embedder.embed(query) candidates = await self.vector_db.search( vector=embedding, top_k=top_k * 3, filter={"status": "published"}, ) reranked = await self.reranker.rank( query=query, documents=[c.text for c in candidates], top_k=top_k, ) return [ RetrievedDoc( text=doc.text, score=doc.score, source=doc.metadata["source"], ) for doc in reranked ]

class IntakeProcessor: def __init__(self, ocr, classifier, crm): self.ocr = ocr self.classifier = classifier self.crm = crm async def process(self, document): text = await self.ocr.extract(document.file) doc_type = await self.classifier.classify(text) if doc_type == "insurance_card": data = await self.extract_insurance(text) await self.crm.update_insurance( patient_id=document.patient_id, carrier=data["carrier"], policy_number=data["policy"], group_number=data["group"], ) elif doc_type == "referral": data = await self.extract_referral(text) await self.crm.create_referral( patient_id=document.patient_id, provider=data["referring_provider"], reason=data["reason"], valid_until=data["expiration"], ) await self.audit_log(document, doc_type) return doc_type class IntakeProcessor: def __init__(self, ocr, classifier, crm): self.ocr = ocr self.classifier = classifier self.crm = crm async def process(self, document): text = await self.ocr.extract(document.file) doc_type = await self.classifier.classify(text) if doc_type == "insurance_card": data = await self.extract_insurance(text) await self.crm.update_insurance( patient_id=document.patient_id, carrier=data["carrier"], policy_number=data["policy"], group_number=data["group"], ) elif doc_type == "referral": data = await self.extract_referral(text) await self.crm.create_referral( patient_id=document.patient_id, provider=data["referring_provider"], reason=data["reason"], valid_until=data["expiration"], ) await self.audit_log(document, doc_type) return doc_type

use std::sync::Arc; use tokio::sync::Mutex; struct ConnectionPool { connections: Arc<Mutex<Vec<Connection>>>, max_size: usize, health_check_interval: Duration, } impl ConnectionPool { async fn acquire(&self) -> Result<Connection> { let mut pool = self.connections.lock().await; while let Some(conn) = pool.pop() { if conn.is_healthy().await { return Ok(conn); } conn.close().await; } self.create_connection().await } async fn release(&self, conn: Connection) { let mut pool = self.connections.lock().await; if pool.len() < self.max_size { pool.push(conn); } else { conn.close().await; } } async fn health_check_loop(&self) { loop { tokio::time::sleep(self.health_check_interval).await; let mut pool = self.connections.lock().await; pool.retain(|c| c.last_used.elapsed() < Duration::from_secs(300)); } } } use std::sync::Arc; use tokio::sync::Mutex; struct ConnectionPool { connections: Arc<Mutex<Vec<Connection>>>, max_size: usize, health_check_interval: Duration, } impl ConnectionPool { async fn acquire(&self) -> Result<Connection> { let mut pool = self.connections.lock().await; while let Some(conn) = pool.pop() { if conn.is_healthy().await { return Ok(conn); } conn.close().await; } self.create_connection().await } async fn release(&self, conn: Connection) { let mut pool = self.connections.lock().await; if pool.len() < self.max_size { pool.push(conn); } else { conn.close().await; } } async fn health_check_loop(&self) { loop { tokio::time::sleep(self.health_check_interval).await; let mut pool = self.connections.lock().await; pool.retain(|c| c.last_used.elapsed() < Duration::from_secs(300)); } } }

export async function POST(req: Request) { const { messages, tools } = await req.json(); const session = await auth.verify(req); const stream = await anthropic.messages.stream({ model: "claude-sonnet-4-20250514", max_tokens: 4096, system: buildSystemPrompt(session), messages, tools: tools ?? defaultTools, }); const encoder = new TextEncoder(); const readable = new ReadableStream({ async start(controller) { for await (const event of stream) { const data = JSON.stringify(event); controller.enqueue( encoder.encode("data: " + data + "\n\n") ); } controller.close(); }, }); return new Response(readable, { headers: { "Content-Type": "text/event-stream", "Cache-Control": "no-cache", }, }); } export async function POST(req: Request) { const { messages, tools } = await req.json(); const session = await auth.verify(req); const stream = await anthropic.messages.stream({ model: "claude-sonnet-4-20250514", max_tokens: 4096, system: buildSystemPrompt(session), messages, tools: tools ?? defaultTools, }); const encoder = new TextEncoder(); const readable = new ReadableStream({ async start(controller) { for await (const event of stream) { const data = JSON.stringify(event); controller.enqueue( encoder.encode("data: " + data + "\n\n") ); } controller.close(); }, }); return new Response(readable, { headers: { "Content-Type": "text/event-stream", "Cache-Control": "no-cache", }, }); }

fn optimize_dispatch( drivers: &[Driver], requests: &[TripRequest], constraints: &DispatchConstraints, ) -> Vec<Assignment> { let mut assignments = Vec::new(); let mut available = drivers.to_vec(); let scored: Vec<_> = requests.iter() .flat_map(|req| { available.iter().map(move |driver| { let eta = calculate_eta(driver, req); let score = score_match(driver, req, eta); (driver.id, req.id, score, eta) }) }) .collect(); scored.sort_by(|a, b| b.2.partial_cmp(&a.2).unwrap()); for (driver_id, req_id, score, eta) in scored { if !available.iter().any(|d| d.id == driver_id) { continue; } if eta > constraints.max_eta { continue; } assignments.push(Assignment { driver_id, req_id, score, eta, }); available.retain(|d| d.id != driver_id); } assignments } fn optimize_dispatch( drivers: &[Driver], requests: &[TripRequest], constraints: &DispatchConstraints, ) -> Vec<Assignment> { let mut assignments = Vec::new(); let mut available = drivers.to_vec(); let scored: Vec<_> = requests.iter() .flat_map(|req| { available.iter().map(move |driver| { let eta = calculate_eta(driver, req); let score = score_match(driver, req, eta); (driver.id, req.id, score, eta) }) }) .collect(); scored.sort_by(|a, b| b.2.partial_cmp(&a.2).unwrap()); for (driver_id, req_id, score, eta) in scored { if !available.iter().any(|d| d.id == driver_id) { continue; } if eta > constraints.max_eta { continue; } assignments.push(Assignment { driver_id, req_id, score, eta, }); available.retain(|d| d.id != driver_id); } assignments }

async def route_maintenance_request(request): urgency = await classifier.assess_urgency( description=request.description, photos=request.attachments, ) vendor = await find_best_vendor( category=urgency.category, location=request.unit.address, budget=request.property.maintenance_budget, ) work_order = await create_work_order( request=request, vendor=vendor, priority=urgency.level, estimated_cost=vendor.estimate, ) await notify_tenant( tenant=request.tenant, work_order=work_order, eta=vendor.next_available, ) await notify_property_manager( manager=request.property.manager, work_order=work_order, ) return work_order async def route_maintenance_request(request): urgency = await classifier.assess_urgency( description=request.description, photos=request.attachments, ) vendor = await find_best_vendor( category=urgency.category, location=request.unit.address, budget=request.property.maintenance_budget, ) work_order = await create_work_order( request=request, vendor=vendor, priority=urgency.level, estimated_cost=vendor.estimate, ) await notify_tenant( tenant=request.tenant, work_order=work_order, eta=vendor.next_available, ) await notify_property_manager( manager=request.property.manager, work_order=work_order, ) return work_order

SELECT tenants.id, tenants.name, units.address, leases.end_date, leases.monthly_rent, CASE WHEN leases.end_date - CURRENT_DATE <= 90 THEN 'urgent' WHEN leases.end_date - CURRENT_DATE <= 180 THEN 'upcoming' ELSE 'active' END AS renewal_status, tenant_scores.payment_score, market_rates.suggested_rent FROM tenants JOIN leases ON tenants.id = leases.tenant_id JOIN units ON leases.unit_id = units.id LEFT JOIN tenant_scores ON tenants.id = tenant_scores.tenant_id LEFT JOIN market_rates ON units.zip_code = market_rates.zip_code WHERE leases.end_date <= CURRENT_DATE + 180 AND leases.status = 'active' ORDER BY leases.end_date ASC; SELECT tenants.id, tenants.name, units.address, leases.end_date, leases.monthly_rent, CASE WHEN leases.end_date - CURRENT_DATE <= 90 THEN 'urgent' WHEN leases.end_date - CURRENT_DATE <= 180 THEN 'upcoming' ELSE 'active' END AS renewal_status, tenant_scores.payment_score, market_rates.suggested_rent FROM tenants JOIN leases ON tenants.id = leases.tenant_id JOIN units ON leases.unit_id = units.id LEFT JOIN tenant_scores ON tenants.id = tenant_scores.tenant_id LEFT JOIN market_rates ON units.zip_code = market_rates.zip_code WHERE leases.end_date <= CURRENT_DATE + 180 AND leases.status = 'active' ORDER BY leases.end_date ASC;

func (h *DispatchHub) Run() { for { select { case driver := <-h.register: h.drivers[driver.ID] = driver h.broadcastDriverCount() case driver := <-h.unregister: delete(h.drivers, driver.ID) close(driver.Send) h.broadcastDriverCount() case update := <-h.locationUpdates: h.drivers[update.DriverID].Location = update.Position h.grid.Update(update.DriverID, update.Position) nearby := h.grid.FindNearby( update.Position, 5*time.Minute, ) h.pendingRequests.TryMatch(nearby) case req := <-h.tripRequests: candidates := h.grid.FindNearby( req.Pickup, 10*time.Minute, ) if best := h.scorer.Rank(candidates, req); best != nil { h.assign(best, req) } else { h.pendingRequests.Enqueue(req) } } } } func (h *DispatchHub) Run() { for { select { case driver := <-h.register: h.drivers[driver.ID] = driver h.broadcastDriverCount() case driver := <-h.unregister: delete(h.drivers, driver.ID) close(driver.Send) h.broadcastDriverCount() case update := <-h.locationUpdates: h.drivers[update.DriverID].Location = update.Position h.grid.Update(update.DriverID, update.Position) nearby := h.grid.FindNearby( update.Position, 5*time.Minute, ) h.pendingRequests.TryMatch(nearby) case req := <-h.tripRequests: candidates := h.grid.FindNearby( req.Pickup, 10*time.Minute, ) if best := h.scorer.Rank(candidates, req); best != nil { h.assign(best, req) } else { h.pendingRequests.Enqueue(req) } } } }

class AuditTrail: def __init__(self, storage, encryption_key): self.storage = storage self.cipher = AES256(encryption_key) async def log_access(self, event: AuditEvent): record = { "timestamp": datetime.utcnow().isoformat(), "actor": event.user_id, "action": event.action, "resource": event.resource_id, "resource_type": event.resource_type, "ip_address": event.ip_address, "result": event.result, "metadata": event.metadata, } encrypted = self.cipher.encrypt( json.dumps(record).encode() ) await self.storage.append( partition=event.resource_type, key=f"{event.timestamp}_{event.user_id}", value=encrypted, ) if event.action in REPORTABLE_ACTIONS: await self.notify_compliance(record) class AuditTrail: def __init__(self, storage, encryption_key): self.storage = storage self.cipher = AES256(encryption_key) async def log_access(self, event: AuditEvent): record = { "timestamp": datetime.utcnow().isoformat(), "actor": event.user_id, "action": event.action, "resource": event.resource_id, "resource_type": event.resource_type, "ip_address": event.ip_address, "result": event.result, "metadata": event.metadata, } encrypted = self.cipher.encrypt( json.dumps(record).encode() ) await self.storage.append( partition=event.resource_type, key=f"{event.timestamp}_{event.user_id}", value=encrypted, ) if event.action in REPORTABLE_ACTIONS: await self.notify_compliance(record)

export async function processContract( document: Buffer, schema: ContractSchema, ) { const pages = await pdf.parse(document); const chunks = chunkBySection(pages, { maxTokens: 2000, overlap: 200, }); const extractions = await Promise.all( chunks.map(chunk => anthropic.messages.create({ model: "claude-sonnet-4-20250514", messages: [{ role: "user", content: [ { type: "text", text: EXTRACTION_PROMPT }, { type: "text", text: chunk.text }, ], }], max_tokens: 4096, }) ) ); const merged = mergeExtractions(extractions); const validated = schema.parse(merged); await db.contracts.upsert(validated); return validated; } export async function processContract( document: Buffer, schema: ContractSchema, ) { const pages = await pdf.parse(document); const chunks = chunkBySection(pages, { maxTokens: 2000, overlap: 200, }); const extractions = await Promise.all( chunks.map(chunk => anthropic.messages.create({ model: "claude-sonnet-4-20250514", messages: [{ role: "user", content: [ { type: "text", text: EXTRACTION_PROMPT }, { type: "text", text: chunk.text }, ], }], max_tokens: 4096, }) ) ); const merged = mergeExtractions(extractions); const validated = schema.parse(merged); await db.contracts.upsert(validated); return validated; }

template<typename T, size_t Dim> class VectorIndex { HNSWGraph<Dim> graph_; std::unordered_map<uint64_t, Metadata> meta_; mutable std::shared_mutex mutex_; public: void insert(uint64_t id, const Vector<Dim>& vec, Metadata meta) { std::unique_lock lock(mutex_); graph_.add_point(vec.data(), id); meta_[id] = std::move(meta); } std::vector<SearchResult> search( const Vector<Dim>& query, size_t top_k, const Filter& filter = {}) const { std::shared_lock lock(mutex_); auto neighbors = graph_.search( query.data(), top_k * 2 ); std::vector<SearchResult> results; for (auto& [id, dist] : neighbors) { if (filter.matches(meta_.at(id))) { results.push_back({id, dist, meta_.at(id)}); } if (results.size() >= top_k) break; } return results; } }; template<typename T, size_t Dim> class VectorIndex { HNSWGraph<Dim> graph_; std::unordered_map<uint64_t, Metadata> meta_; mutable std::shared_mutex mutex_; public: void insert(uint64_t id, const Vector<Dim>& vec, Metadata meta) { std::unique_lock lock(mutex_); graph_.add_point(vec.data(), id); meta_[id] = std::move(meta); } std::vector<SearchResult> search( const Vector<Dim>& query, size_t top_k, const Filter& filter = {}) const { std::shared_lock lock(mutex_); auto neighbors = graph_.search( query.data(), top_k * 2 ); std::vector<SearchResult> results; for (auto& [id, dist] : neighbors) { if (filter.matches(meta_.at(id))) { results.push_back({id, dist, meta_.at(id)}); } if (results.size() >= top_k) break; } return results; } };

async def evaluate_models( test_suite: TestSuite, models: list[ModelConfig], ) -> EvalReport: results = {} for model in models: client = init_client(model) scores = [] for case in test_suite.cases: response = await client.generate( prompt=case.prompt, temperature=0, max_tokens=case.max_tokens, ) score = await grade_response( response=response.text, expected=case.expected, rubric=case.rubric, grader_model="claude-sonnet-4-20250514", ) scores.append(score) results[model.name] = { "accuracy": mean(s.accuracy for s in scores), "latency_p50": percentile(s.latency for s in scores, 50), "latency_p99": percentile(s.latency for s in scores, 99), "cost_per_1k": sum(s.cost for s in scores) / len(scores) * 1000, } return EvalReport(results=results, suite=test_suite) async def evaluate_models( test_suite: TestSuite, models: list[ModelConfig], ) -> EvalReport: results = {} for model in models: client = init_client(model) scores = [] for case in test_suite.cases: response = await client.generate( prompt=case.prompt, temperature=0, max_tokens=case.max_tokens, ) score = await grade_response( response=response.text, expected=case.expected, rubric=case.rubric, grader_model="claude-sonnet-4-20250514", ) scores.append(score) results[model.name] = { "accuracy": mean(s.accuracy for s in scores), "latency_p50": percentile(s.latency for s in scores, 50), "latency_p99": percentile(s.latency for s in scores, 99), "cost_per_1k": sum(s.cost for s in scores) / len(scores) * 1000, } return EvalReport(results=results, suite=test_suite)

async function runETL(config: ETLConfig) { const source = createSource(config.source); const sink = createSink(config.destination); let processed = 0; let errors = 0; for await (const batch of source.stream(1000)) { const transformed = batch .map(record => { try { const clean = normalize(record); const valid = config.schema.parse(clean); return { ok: true, data: valid }; } catch (e) { errors++; deadLetter.send(record, e); return { ok: false }; } }) .filter(r => r.ok) .map(r => r.data); await sink.writeBatch(transformed); processed += transformed.length; metrics.gauge("etl.processed", processed); metrics.gauge("etl.errors", errors); } return { processed, errors }; } async function runETL(config: ETLConfig) { const source = createSource(config.source); const sink = createSink(config.destination); let processed = 0; let errors = 0; for await (const batch of source.stream(1000)) { const transformed = batch .map(record => { try { const clean = normalize(record); const valid = config.schema.parse(clean); return { ok: true, data: valid }; } catch (e) { errors++; deadLetter.send(record, e); return { ok: false }; } }) .filter(r => r.ok) .map(r => r.data); await sink.writeBatch(transformed); processed += transformed.length; metrics.gauge("etl.processed", processed); metrics.gauge("etl.errors", errors); } return { processed, errors }; }

resource "aws_ecs_service" "api" { name = "novostra-api-prod" cluster = aws_ecs_cluster.main.id task_definition = aws_ecs_task_definition.api.arn desired_count = 3 launch_type = "FARGATE" network_configuration { subnets = var.private_subnets security_groups = [aws_security_group.api.id] assign_public_ip = false } load_balancer { target_group_arn = aws_lb_target_group.api.arn container_name = "api" container_port = 8080 } service_registries { registry_arn = aws_service_discovery_service.api.arn } deployment_circuit_breaker { enable = true rollback = true } } resource "aws_ecs_service" "api" { name = "novostra-api-prod" cluster = aws_ecs_cluster.main.id task_definition = aws_ecs_task_definition.api.arn desired_count = 3 launch_type = "FARGATE" network_configuration { subnets = var.private_subnets security_groups = [aws_security_group.api.id] assign_public_ip = false } load_balancer { target_group_arn = aws_lb_target_group.api.arn container_name = "api" container_port = 8080 } service_registries { registry_arn = aws_service_discovery_service.api.arn } deployment_circuit_breaker { enable = true rollback = true } }

class PatientDataPipeline: def __init__(self, ehr_client, fhir_endpoint): self.ehr = ehr_client self.fhir = fhir_endpoint self.audit = AuditLogger("hipaa") async def sync_encounters(self, patient_id): self.audit.log("access", patient_id) raw = await self.ehr.get_encounters(patient_id) normalized = [ self.to_fhir_encounter(e) for e in raw ] await self.fhir.batch_upsert(normalized) return normalized def to_fhir_encounter(self, encounter): return { "resourceType": "Encounter", "status": encounter["status"], "class": encounter["visit_type"], "subject": encounter["patient_ref"], "period": { "start": encounter["admitted_at"], "end": encounter["discharged_at"], }, } class PatientDataPipeline: def __init__(self, ehr_client, fhir_endpoint): self.ehr = ehr_client self.fhir = fhir_endpoint self.audit = AuditLogger("hipaa") async def sync_encounters(self, patient_id): self.audit.log("access", patient_id) raw = await self.ehr.get_encounters(patient_id) normalized = [ self.to_fhir_encounter(e) for e in raw ] await self.fhir.batch_upsert(normalized) return normalized def to_fhir_encounter(self, encounter): return { "resourceType": "Encounter", "status": encounter["status"], "class": encounter["visit_type"], "subject": encounter["patient_ref"], "period": { "start": encounter["admitted_at"], "end": encounter["discharged_at"], }, }

async function runAgentWorkflow(task: Task) { const planner = new PlannerAgent({ model: "claude-sonnet-4-20250514", tools: [searchTool, sqlTool, emailTool], }); const plan = await planner.decompose(task); const results: StepResult[] = []; for (const step of plan.steps) { const agent = AgentPool.acquire(step.type); const result = await agent.execute(step, { context: results, timeout: 30_000, retries: 2, }); results.push(result); if (result.requiresApproval) { await HumanInTheLoop.request({ step, result, assignee: task.owner, }); } } return planner.synthesize(results); } async function runAgentWorkflow(task: Task) { const planner = new PlannerAgent({ model: "claude-sonnet-4-20250514", tools: [searchTool, sqlTool, emailTool], }); const plan = await planner.decompose(task); const results: StepResult[] = []; for (const step of plan.steps) { const agent = AgentPool.acquire(step.type); const result = await agent.execute(step, { context: results, timeout: 30_000, retries: 2, }); results.push(result); if (result.requiresApproval) { await HumanInTheLoop.request({ step, result, assignee: task.owner, }); } } return planner.synthesize(results); }

class RetrievalPipeline: def __init__(self, embedder, vector_db, reranker): self.embedder = embedder self.vector_db = vector_db self.reranker = reranker async def retrieve(self, query, top_k=10): embedding = await self.embedder.embed(query) candidates = await self.vector_db.search( vector=embedding, top_k=top_k * 3, filter={"status": "published"}, ) reranked = await self.reranker.rank( query=query, documents=[c.text for c in candidates], top_k=top_k, ) return [ RetrievedDoc( text=doc.text, score=doc.score, source=doc.metadata["source"], ) for doc in reranked ] class RetrievalPipeline: def __init__(self, embedder, vector_db, reranker): self.embedder = embedder self.vector_db = vector_db self.reranker = reranker async def retrieve(self, query, top_k=10): embedding = await self.embedder.embed(query) candidates = await self.vector_db.search( vector=embedding, top_k=top_k * 3, filter={"status": "published"}, ) reranked = await self.reranker.rank( query=query, documents=[c.text for c in candidates], top_k=top_k, ) return [ RetrievedDoc( text=doc.text, score=doc.score, source=doc.metadata["source"], ) for doc in reranked ]

class IntakeProcessor: def __init__(self, ocr, classifier, crm): self.ocr = ocr self.classifier = classifier self.crm = crm async def process(self, document): text = await self.ocr.extract(document.file) doc_type = await self.classifier.classify(text) if doc_type == "insurance_card": data = await self.extract_insurance(text) await self.crm.update_insurance( patient_id=document.patient_id, carrier=data["carrier"], policy_number=data["policy"], group_number=data["group"], ) elif doc_type == "referral": data = await self.extract_referral(text) await self.crm.create_referral( patient_id=document.patient_id, provider=data["referring_provider"], reason=data["reason"], valid_until=data["expiration"], ) await self.audit_log(document, doc_type) return doc_type class IntakeProcessor: def __init__(self, ocr, classifier, crm): self.ocr = ocr self.classifier = classifier self.crm = crm async def process(self, document): text = await self.ocr.extract(document.file) doc_type = await self.classifier.classify(text) if doc_type == "insurance_card": data = await self.extract_insurance(text) await self.crm.update_insurance( patient_id=document.patient_id, carrier=data["carrier"], policy_number=data["policy"], group_number=data["group"], ) elif doc_type == "referral": data = await self.extract_referral(text) await self.crm.create_referral( patient_id=document.patient_id, provider=data["referring_provider"], reason=data["reason"], valid_until=data["expiration"], ) await self.audit_log(document, doc_type) return doc_type

use std::sync::Arc; use tokio::sync::Mutex; struct ConnectionPool { connections: Arc<Mutex<Vec<Connection>>>, max_size: usize, health_check_interval: Duration, } impl ConnectionPool { async fn acquire(&self) -> Result<Connection> { let mut pool = self.connections.lock().await; while let Some(conn) = pool.pop() { if conn.is_healthy().await { return Ok(conn); } conn.close().await; } self.create_connection().await } async fn release(&self, conn: Connection) { let mut pool = self.connections.lock().await; if pool.len() < self.max_size { pool.push(conn); } else { conn.close().await; } } async fn health_check_loop(&self) { loop { tokio::time::sleep(self.health_check_interval).await; let mut pool = self.connections.lock().await; pool.retain(|c| c.last_used.elapsed() < Duration::from_secs(300)); } } } use std::sync::Arc; use tokio::sync::Mutex; struct ConnectionPool { connections: Arc<Mutex<Vec<Connection>>>, max_size: usize, health_check_interval: Duration, } impl ConnectionPool { async fn acquire(&self) -> Result<Connection> { let mut pool = self.connections.lock().await; while let Some(conn) = pool.pop() { if conn.is_healthy().await { return Ok(conn); } conn.close().await; } self.create_connection().await } async fn release(&self, conn: Connection) { let mut pool = self.connections.lock().await; if pool.len() < self.max_size { pool.push(conn); } else { conn.close().await; } } async fn health_check_loop(&self) { loop { tokio::time::sleep(self.health_check_interval).await; let mut pool = self.connections.lock().await; pool.retain(|c| c.last_used.elapsed() < Duration::from_secs(300)); } } }

export async function POST(req: Request) { const { messages, tools } = await req.json(); const session = await auth.verify(req); const stream = await anthropic.messages.stream({ model: "claude-sonnet-4-20250514", max_tokens: 4096, system: buildSystemPrompt(session), messages, tools: tools ?? defaultTools, }); const encoder = new TextEncoder(); const readable = new ReadableStream({ async start(controller) { for await (const event of stream) { const data = JSON.stringify(event); controller.enqueue( encoder.encode("data: " + data + "\n\n") ); } controller.close(); }, }); return new Response(readable, { headers: { "Content-Type": "text/event-stream", "Cache-Control": "no-cache", }, }); } export async function POST(req: Request) { const { messages, tools } = await req.json(); const session = await auth.verify(req); const stream = await anthropic.messages.stream({ model: "claude-sonnet-4-20250514", max_tokens: 4096, system: buildSystemPrompt(session), messages, tools: tools ?? defaultTools, }); const encoder = new TextEncoder(); const readable = new ReadableStream({ async start(controller) { for await (const event of stream) { const data = JSON.stringify(event); controller.enqueue( encoder.encode("data: " + data + "\n\n") ); } controller.close(); }, }); return new Response(readable, { headers: { "Content-Type": "text/event-stream", "Cache-Control": "no-cache", }, }); }

fn optimize_dispatch( drivers: &[Driver], requests: &[TripRequest], constraints: &DispatchConstraints, ) -> Vec<Assignment> { let mut assignments = Vec::new(); let mut available = drivers.to_vec(); let scored: Vec<_> = requests.iter() .flat_map(|req| { available.iter().map(move |driver| { let eta = calculate_eta(driver, req); let score = score_match(driver, req, eta); (driver.id, req.id, score, eta) }) }) .collect(); scored.sort_by(|a, b| b.2.partial_cmp(&a.2).unwrap()); for (driver_id, req_id, score, eta) in scored { if !available.iter().any(|d| d.id == driver_id) { continue; } if eta > constraints.max_eta { continue; } assignments.push(Assignment { driver_id, req_id, score, eta, }); available.retain(|d| d.id != driver_id); } assignments } fn optimize_dispatch( drivers: &[Driver], requests: &[TripRequest], constraints: &DispatchConstraints, ) -> Vec<Assignment> { let mut assignments = Vec::new(); let mut available = drivers.to_vec(); let scored: Vec<_> = requests.iter() .flat_map(|req| { available.iter().map(move |driver| { let eta = calculate_eta(driver, req); let score = score_match(driver, req, eta); (driver.id, req.id, score, eta) }) }) .collect(); scored.sort_by(|a, b| b.2.partial_cmp(&a.2).unwrap()); for (driver_id, req_id, score, eta) in scored { if !available.iter().any(|d| d.id == driver_id) { continue; } if eta > constraints.max_eta { continue; } assignments.push(Assignment { driver_id, req_id, score, eta, }); available.retain(|d| d.id != driver_id); } assignments }

async def route_maintenance_request(request): urgency = await classifier.assess_urgency( description=request.description, photos=request.attachments, ) vendor = await find_best_vendor( category=urgency.category, location=request.unit.address, budget=request.property.maintenance_budget, ) work_order = await create_work_order( request=request, vendor=vendor, priority=urgency.level, estimated_cost=vendor.estimate, ) await notify_tenant( tenant=request.tenant, work_order=work_order, eta=vendor.next_available, ) await notify_property_manager( manager=request.property.manager, work_order=work_order, ) return work_order async def route_maintenance_request(request): urgency = await classifier.assess_urgency( description=request.description, photos=request.attachments, ) vendor = await find_best_vendor( category=urgency.category, location=request.unit.address, budget=request.property.maintenance_budget, ) work_order = await create_work_order( request=request, vendor=vendor, priority=urgency.level, estimated_cost=vendor.estimate, ) await notify_tenant( tenant=request.tenant, work_order=work_order, eta=vendor.next_available, ) await notify_property_manager( manager=request.property.manager, work_order=work_order, ) return work_order

SELECT tenants.id, tenants.name, units.address, leases.end_date, leases.monthly_rent, CASE WHEN leases.end_date - CURRENT_DATE <= 90 THEN 'urgent' WHEN leases.end_date - CURRENT_DATE <= 180 THEN 'upcoming' ELSE 'active' END AS renewal_status, tenant_scores.payment_score, market_rates.suggested_rent FROM tenants JOIN leases ON tenants.id = leases.tenant_id JOIN units ON leases.unit_id = units.id LEFT JOIN tenant_scores ON tenants.id = tenant_scores.tenant_id LEFT JOIN market_rates ON units.zip_code = market_rates.zip_code WHERE leases.end_date <= CURRENT_DATE + 180 AND leases.status = 'active' ORDER BY leases.end_date ASC; SELECT tenants.id, tenants.name, units.address, leases.end_date, leases.monthly_rent, CASE WHEN leases.end_date - CURRENT_DATE <= 90 THEN 'urgent' WHEN leases.end_date - CURRENT_DATE <= 180 THEN 'upcoming' ELSE 'active' END AS renewal_status, tenant_scores.payment_score, market_rates.suggested_rent FROM tenants JOIN leases ON tenants.id = leases.tenant_id JOIN units ON leases.unit_id = units.id LEFT JOIN tenant_scores ON tenants.id = tenant_scores.tenant_id LEFT JOIN market_rates ON units.zip_code = market_rates.zip_code WHERE leases.end_date <= CURRENT_DATE + 180 AND leases.status = 'active' ORDER BY leases.end_date ASC;

func (h *DispatchHub) Run() { for { select { case driver := <-h.register: h.drivers[driver.ID] = driver h.broadcastDriverCount() case driver := <-h.unregister: delete(h.drivers, driver.ID) close(driver.Send) h.broadcastDriverCount() case update := <-h.locationUpdates: h.drivers[update.DriverID].Location = update.Position h.grid.Update(update.DriverID, update.Position) nearby := h.grid.FindNearby( update.Position, 5*time.Minute, ) h.pendingRequests.TryMatch(nearby) case req := <-h.tripRequests: candidates := h.grid.FindNearby( req.Pickup, 10*time.Minute, ) if best := h.scorer.Rank(candidates, req); best != nil { h.assign(best, req) } else { h.pendingRequests.Enqueue(req) } } } } func (h *DispatchHub) Run() { for { select { case driver := <-h.register: h.drivers[driver.ID] = driver h.broadcastDriverCount() case driver := <-h.unregister: delete(h.drivers, driver.ID) close(driver.Send) h.broadcastDriverCount() case update := <-h.locationUpdates: h.drivers[update.DriverID].Location = update.Position h.grid.Update(update.DriverID, update.Position) nearby := h.grid.FindNearby( update.Position, 5*time.Minute, ) h.pendingRequests.TryMatch(nearby) case req := <-h.tripRequests: candidates := h.grid.FindNearby( req.Pickup, 10*time.Minute, ) if best := h.scorer.Rank(candidates, req); best != nil { h.assign(best, req) } else { h.pendingRequests.Enqueue(req) } } } }

class AuditTrail: def __init__(self, storage, encryption_key): self.storage = storage self.cipher = AES256(encryption_key) async def log_access(self, event: AuditEvent): record = { "timestamp": datetime.utcnow().isoformat(), "actor": event.user_id, "action": event.action, "resource": event.resource_id, "resource_type": event.resource_type, "ip_address": event.ip_address, "result": event.result, "metadata": event.metadata, } encrypted = self.cipher.encrypt( json.dumps(record).encode() ) await self.storage.append( partition=event.resource_type, key=f"{event.timestamp}_{event.user_id}", value=encrypted, ) if event.action in REPORTABLE_ACTIONS: await self.notify_compliance(record) class AuditTrail: def __init__(self, storage, encryption_key): self.storage = storage self.cipher = AES256(encryption_key) async def log_access(self, event: AuditEvent): record = { "timestamp": datetime.utcnow().isoformat(), "actor": event.user_id, "action": event.action, "resource": event.resource_id, "resource_type": event.resource_type, "ip_address": event.ip_address, "result": event.result, "metadata": event.metadata, } encrypted = self.cipher.encrypt( json.dumps(record).encode() ) await self.storage.append( partition=event.resource_type, key=f"{event.timestamp}_{event.user_id}", value=encrypted, ) if event.action in REPORTABLE_ACTIONS: await self.notify_compliance(record)

export async function processContract( document: Buffer, schema: ContractSchema, ) { const pages = await pdf.parse(document); const chunks = chunkBySection(pages, { maxTokens: 2000, overlap: 200, }); const extractions = await Promise.all( chunks.map(chunk => anthropic.messages.create({ model: "claude-sonnet-4-20250514", messages: [{ role: "user", content: [ { type: "text", text: EXTRACTION_PROMPT }, { type: "text", text: chunk.text }, ], }], max_tokens: 4096, }) ) ); const merged = mergeExtractions(extractions); const validated = schema.parse(merged); await db.contracts.upsert(validated); return validated; } export async function processContract( document: Buffer, schema: ContractSchema, ) { const pages = await pdf.parse(document); const chunks = chunkBySection(pages, { maxTokens: 2000, overlap: 200, }); const extractions = await Promise.all( chunks.map(chunk => anthropic.messages.create({ model: "claude-sonnet-4-20250514", messages: [{ role: "user", content: [ { type: "text", text: EXTRACTION_PROMPT }, { type: "text", text: chunk.text }, ], }], max_tokens: 4096, }) ) ); const merged = mergeExtractions(extractions); const validated = schema.parse(merged); await db.contracts.upsert(validated); return validated; }

template<typename T, size_t Dim> class VectorIndex { HNSWGraph<Dim> graph_; std::unordered_map<uint64_t, Metadata> meta_; mutable std::shared_mutex mutex_; public: void insert(uint64_t id, const Vector<Dim>& vec, Metadata meta) { std::unique_lock lock(mutex_); graph_.add_point(vec.data(), id); meta_[id] = std::move(meta); } std::vector<SearchResult> search( const Vector<Dim>& query, size_t top_k, const Filter& filter = {}) const { std::shared_lock lock(mutex_); auto neighbors = graph_.search( query.data(), top_k * 2 ); std::vector<SearchResult> results; for (auto& [id, dist] : neighbors) { if (filter.matches(meta_.at(id))) { results.push_back({id, dist, meta_.at(id)}); } if (results.size() >= top_k) break; } return results; } }; template<typename T, size_t Dim> class VectorIndex { HNSWGraph<Dim> graph_; std::unordered_map<uint64_t, Metadata> meta_; mutable std::shared_mutex mutex_; public: void insert(uint64_t id, const Vector<Dim>& vec, Metadata meta) { std::unique_lock lock(mutex_); graph_.add_point(vec.data(), id); meta_[id] = std::move(meta); } std::vector<SearchResult> search( const Vector<Dim>& query, size_t top_k, const Filter& filter = {}) const { std::shared_lock lock(mutex_); auto neighbors = graph_.search( query.data(), top_k * 2 ); std::vector<SearchResult> results; for (auto& [id, dist] : neighbors) { if (filter.matches(meta_.at(id))) { results.push_back({id, dist, meta_.at(id)}); } if (results.size() >= top_k) break; } return results; } };

async def evaluate_models( test_suite: TestSuite, models: list[ModelConfig], ) -> EvalReport: results = {} for model in models: client = init_client(model) scores = [] for case in test_suite.cases: response = await client.generate( prompt=case.prompt, temperature=0, max_tokens=case.max_tokens, ) score = await grade_response( response=response.text, expected=case.expected, rubric=case.rubric, grader_model="claude-sonnet-4-20250514", ) scores.append(score) results[model.name] = { "accuracy": mean(s.accuracy for s in scores), "latency_p50": percentile(s.latency for s in scores, 50), "latency_p99": percentile(s.latency for s in scores, 99), "cost_per_1k": sum(s.cost for s in scores) / len(scores) * 1000, } return EvalReport(results=results, suite=test_suite) async def evaluate_models( test_suite: TestSuite, models: list[ModelConfig], ) -> EvalReport: results = {} for model in models: client = init_client(model) scores = [] for case in test_suite.cases: response = await client.generate( prompt=case.prompt, temperature=0, max_tokens=case.max_tokens, ) score = await grade_response( response=response.text, expected=case.expected, rubric=case.rubric, grader_model="claude-sonnet-4-20250514", ) scores.append(score) results[model.name] = { "accuracy": mean(s.accuracy for s in scores), "latency_p50": percentile(s.latency for s in scores, 50), "latency_p99": percentile(s.latency for s in scores, 99), "cost_per_1k": sum(s.cost for s in scores) / len(scores) * 1000, } return EvalReport(results=results, suite=test_suite)