How Agents Work
Seven agents. One shared brain. Intelligence that compounds over time.
System Architecture
The Intelligence Network
Every agent connects to every other. Information flows freely. Knowledge compounds.
Shared Context
Every agent has access to the full conversation history and all learned patterns. No silos.
Dynamic Routing
Questions automatically route to the right agents. Complex queries trigger multi-agent collaboration.
Continuous Sync
When one agent learns, all agents benefit. Knowledge propagates across the network in real-time.
Agent Specifications
Under the Hood
Each agent is a specialized system. Here's exactly what they do and how they learn.
Capabilities
- 90-day revenue forecasting with confidence intervals
- CAC/ROAS prediction by channel
- Seasonal pattern recognition
- Trend deviation alerts
Technical Specs
Learning Loop
Every forecast is tracked against actuals. Misses are analyzed for cause (external event? seasonal shift? data anomaly?). Model weights auto-adjust. Accuracy compounds over time.
Live Orchestration
Watch Them Think
One question. Seven agents. Real-time collaboration. See exactly how they reach a recommendation.
Should we shift 20% budget from Google to Meta for Q4?
Compound Learning
Every Decision Teaches Them
This isn't static AI. Every forecast is compared against reality. Every recommendation is tracked. Every outcome refines the model. Accuracy doesn't plateau, it compounds.
Measure Everything
Every prediction has a timestamp. Every outcome is recorded. The delta between expected and actual is the learning signal.
Identify the Cause
Was the miss due to external factors (competitor sale, algorithm change)? Seasonality? Bad data? The cause determines the fix.
Update the Model
Model weights adjust automatically. Elasticity curves recalibrate. Confidence intervals tighten. Next prediction is better.
Forecast Accuracy Over Time
Memory Architecture
Perfect Institutional Memory
When your analyst quits, tribal knowledge walks out the door. Maya stores everything, forever.
Constraints
Stated in conversation #234
Mentioned during onboarding
Preference from Q2 review
Patterns
Learned from 3 years of data
127 creatives analyzed
14 months of bid data
History
Conversation #456
Performance tracked
Shopify data
Maya's Memory Stats
Every constraint you've mentioned. Every test you've run. Every preference you've expressed. Stored with full context. Retrieved in milliseconds. Never asks twice.
Architectural Advantage
Why 7 Agents Beat 1 AI
Specialization creates mastery. Coordination creates intelligence.
Single AI Approach
The common approach
One model does everything, mediocre at all tasks
Context window limits constrain memory
Can't specialize in your specific domain
Static accuracy, doesn't learn from your data
Generic responses, no personalization
Cresva Multi-Agent
Specialized + coordinated
Each agent masters one domain, expert at their task
Infinite memory via dedicated Memory Agent
Learns your specific patterns and constraints
Accuracy compounds with every decision
Deeply personalized to your business
The principle: Felix only forecasts, so he gets really good at forecasting. Parker only does attribution, so he becomes expert at de-biasing. Maya only manages memory, so she never forgets. Specialization creates mastery.
See It Work. Live.
Watch seven agents collaborate in real-time. Ask your own questions. See the orchestration happen.
30 minutes. Your questions. Real answers from real agents.