See Agent-Native
Infrastructure in Action
Watch how Knowledge Graphs, Event Stores, and System Ontologies work together to solve customer problems in real-time.
The Architecture Stack
Four layers working together to give AI agents the context they need. Click any layer to learn more.
Assembles relevant context from all layers into a coherent graph structure that AI can reason over. Manages attention by prioritizing relevant information and filtering noise.
Technical Details
- •Dynamic context assembly based on query intent
- •Token budget optimization for LLM consumption
- •Attention focusing through relevance scoring
- •Temporal weighting for recent vs. historical data
Maps entities and their relationships across all connected systems. Enables multi-hop queries that would require expensive JOINs in traditional databases. Pre-computes common traversal patterns.
Technical Details
- •O(1) relationship traversal vs O(n) JOIN operations
- •Pre-computed similarity scores for pattern matching
- •Bi-directional edge traversal for context discovery
- •Real-time graph updates from event streams
Captures every significant business event as it happens. Preserves temporal relationships that enable pattern detection and historical analysis. Events are immutable and ordered.
Technical Details
- •Append-only storage for complete audit trail
- •Temporal queries: "What happened before X?"
- •Event correlation across system boundaries
- •Stream processing for real-time pattern detection
Connects to your existing enterprise systems through system-specific ontologies. Ontologies act as a "Rosetta Stone" translating between different system schemas into a unified business model.
Technical Details
- •Pre-built ontologies for common enterprise systems
- •Schema mapping handles system-specific terminology
- •Incremental sync minimizes API load
- •Conflict resolution for contradictory data
Result: AI agents get unified, contextual access to your entire business—in milliseconds, not minutes.
See It Work
Choose a scenario and watch the system trace interactions, traverse the graph, and build context for AI decision-making.
Why This Matters
Effective AI isn't just about the prompt.
It's about context management.
From the moment a query arrives to the final response, context management determines whether your AI gives accurate answers or makes things up.
Context Assembly
- ✓Build the right context from the start
- ✓Pull from multiple systems instantly
- ✓Graph traversal finds non-obvious connections
Traditional RAG retrieves chunks blindly. Graph-based context assembly understands entity relationships, pulling connected information that vector similarity would miss. When Sarah Chen files a ticket, we don't just search for "invoice" - we traverse her account graph to find related orders, previous issues, and resolution patterns.
Attention Management
- ✓Constantly refocus AI attention on relevant information
- ✓Filter noise, amplify signal
- ✓Manage token budget efficiently
Transformer attention mechanisms work best when context is relevant and structured. Random retrieval wastes tokens on noise. By pre-filtering through the knowledge graph and event store, we deliver precisely the context that matters—structured so the attention mechanism can weight it appropriately. This isn't prompt engineering; it's architecture.
Execution Accuracy
- ✓Use less context window, get higher accuracy
- ✓Ground responses in real data
- ✓Ask less of the model, get better results
We discovered something counterintuitive in development: reducing context window usage dramatically increased accuracy. By sending only precisely relevant data—not everything we could find—we give the model less opportunity to confuse or hallucinate. Smaller, focused context means the model reasons over facts, not noise.
The Hallucination Problem, Attacked
We discovered something counterintuitive: by drastically reducing context window usage—sending only what matters—accuracy increased exponentially. Less noise means fewer gaps for AI to fill with fiction.
We haven't solved hallucinations. We've contained them by asking less of the model.
Want to understand the technical architecture in depth? Read our architecture overview →
Ready to See This With Your Data?
Schedule a demo to see how Agent-Native Infrastructure can transform your AI initiatives with unified context and real-time intelligence.
Or email us directly: hello@adteco.com