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 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)
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
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));
}
}
}
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
}
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)
}
}
}
}
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;
}
};
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;
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 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
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)
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;
}
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",
},
});
}
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
]
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 };
}
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 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)
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
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));
}
}
}
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
}
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)
}
}
}
}
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;
}
};
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;
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 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
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)
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;
}
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",
},
});
}
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
]
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 };
}