Build autonomous agents that understand, decide, and execute complex business operations. From fraud detection to dynamic pricing.
Intelligent flow for autonomous decision making
Everything you need to build intelligent automation
Agents analyze real-time data streams, understand context, and make optimal decisions without human intervention.
React to business events in microseconds. Built on Apache Kafka and Redis for extreme performance.
Agents improve over time using reinforcement learning. Every interaction makes the system smarter.
RESTful APIs, GraphQL endpoints, and gRPC for high-performance inter-service communication.
Built-in observability with Prometheus, Grafana, and custom dashboards for agent performance.
SOC2 compliant, end-to-end encryption, and role-based access control for sensitive operations.
Simple SDK, powerful results
from caema import TravelAgent, FlightSearch, HotelSearch
# Define a travel booking agent
class TravelBookingAgent(TravelAgent):
def __init__(self):
super().__init__(
name="travel_booking",
providers=["amadeus", "sabre", "expedia"]
)
async def search_flights(self, request: dict) -> dict:
# Multi-provider flight search
options = await self.parallel_search(
origin=request['origin'],
destination=request['destination'],
date=request['date']
)
# AI-powered ranking
ranked = await self.rank_by_preference(
options,
user_profile=request.get('user_id')
)
return {
'flights': ranked,
'total_results': len(options),
'search_time_ms': self.last_search_time
}
async def book_flight(self, flight_id: str, passenger_data: dict):
# Automated booking with validation
return await self.execute_booking(flight_id, passenger_data)
# Deploy the agent
agent = TravelBookingAgent()
agent.deploy(workers=15, region="global")from caema import Agent, Context, Action
# Define a fraud detection agent
class FraudDetectionAgent(Agent):
def __init__(self):
super().__init__(
name="fraud_detector",
model="caema-fraud-v2"
)
async def analyze(self, transaction: dict) -> Action:
# AI-powered analysis
context = await self.get_context(transaction['user_id'])
risk_score = await self.model.predict(transaction, context)
if risk_score > 0.8:
return Action.block(reason="High risk detected")
return Action.approve()
# Deploy the agent
agent = FraudDetectionAgent()
agent.deploy(workers=10, region="us-east-1")import { Agent, Context, Action } from '@caema/sdk';
// Create a travel booking agent
const bookingAgent = new Agent({
name: 'travel_booking',
model: 'caema-travel-v3',
async process(request) {
// Extract intent and entities
const { intent, entities } = await this.understand(request);
// Search across multiple providers
const options = await this.searchProviders({
destination: entities.destination,
dates: entities.dates,
preferences: await this.getUserPreferences()
});
// Optimize and rank results
const ranked = await this.optimize(options);
return Action.present(ranked);
}
});
// Start the agent
bookingAgent.start({ port: 8080 });# agent-config.yaml
version: '1.0'
agents:
- name: price_optimization
type: reinforcement_learning
model:
algorithm: PPO
layers: [512, 256, 128]
learning_rate: 0.0003
inputs:
- market_data
- competitor_prices
- demand_forecast
actions:
- type: adjust_price
range: [-20, 20] # percentage
- type: send_alert
channels: [slack, email]
deployment:
replicas: 5
memory: 4Gi
cpu: 2Proven solutions for complex operational challenges
Detect fraudulent consumption patterns in real-time. 87% accuracy improvement over rule-based systems.
Automated booking, pricing, and customer service. Handle 10M+ requests per day with sub-second response.
Market analysis and investment optimization using predictive models. 3x faster decision making.
End-to-end logistics optimization with multi-agent coordination. 40% reduction in operational costs.
Production-ready from day one
PyTorch
Apache Kafka
Kubernetes
PostgreSQL
Elasticsearch
Apache Spark
Redis
Grafana
AWS
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