Use Cases
See how CognOS solves real-world problems for intelligent workloads with governance, memory, and reactive signals.
❌ Problem
Unpredictable AI costs, no governance controls, agents loop unexpectedly and burn through budgets overnight.
✓ Solution
Budgeted workflows with admission control. Gate-before-budget prevents wasting money on failed operations.
Example: Document Analysis Agent
Agent analyzes documents using GPT-4 with budget limits ($0.50, 5000 tokens, 30 seconds). HITL gate requires human approval for sensitive content. If OpenAI is degraded, admission delays until healthy—budget not consumed.
Admission: Policy ✓ → Capability ✓ → Gate ✓ → Budget ✓ → HITL ✓
Budget Reserved: $0.50
Execution: GPT-4 analysis completes
Actual Cost: $0.42
Budget Reclaimed: $0.08 returned to account❌ Problem
AI agents lack memory and context. Hallucinations, no provenance, can't explain reasoning.
✓ Solution
Knowledge Mesh with provenance and multi-surface queries. Agents access structured memory with full lineage.
Example: Customer Support Agent
Agent queries customer profile (Graph), retrieves previous tickets (Search), analyzes trends (Cube). Every fact has provenance: who extracted it, when, from what source. Contradictions flagged for review.
Graph Query: Customer entity + subscription + contacts
Search Query: Previous tickets matching "critical"
Cube Query: Ticket volume trend (last 30 days)
Result: Grounded response with citations❌ Problem
Reactive systems are hard to build. Polling is inefficient, webhooks are fragile, monitoring is separate.
✓ Solution
Streams, pipelines, triggers with command streams. Event-driven execution with built-in observability.
Example: Anomaly Detection & Escalation
Pipeline watches support ticket events, detects pattern (3+ critical tickets in 15 min), triggers escalation workflow via command stream. Workflow queries knowledge for context, creates escalation with specialist routing.
Pipeline: support.ticket.created
.filter(priority == "Critical")
.window(15 minutes)
.aggregate(count by customerId)
.where(count >= 3)
.trigger(workflow: "escalate-customer")❌ Problem
Building AI platforms from scratch: integration complexity, technical debt, no governance foundation.
✓ Solution
Unified platform with Three Pillars architecture. Governance, memory, and events integrated from day one.
Example: Production AI Platform
Platform provides orchestration (budgeted workflows), knowledge mesh (domain memory), event fabric (reactive triggers). Teams focus on domain logic, not infrastructure. Built on Orleans for proven scale.
- Orchestration: Admission pipeline enforces budgets and policies across all teams
- Knowledge Mesh: Shared domain knowledge with provenance and multi-surface queries
- Event Fabric: Metrics, alerts, and workflow triggers unified in event streams
- Foundation: Orleans grains for scale, module system for extensibility
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