Architecture Built for
Agent Intelligence
We asked one question: What do AI agents actually need to understand your business?
What Doesn't Work
We tried every reasonable approach before building this. Here's why they fail for agent workloads.
Direct API Integration
Connect directly to each system API
- Agents make thousands of calls, hit rate limits
- Fragmented context across responses
- No relationship awareness between systems
- Latency compounds with each call
Works for simple lookups. Fails for cross-system reasoning.
Traditional ETL/Data Warehouse
Batch extract, transform, load to central warehouse
- Batch processing means stale data
- No relationship awareness—just flat tables
- JOIN queries are slow at scale
- Schema changes break pipelines
Works for analytics. Fails for real-time agent workloads.
iPaaS Platforms
Integration platforms like Workato, MuleSoft
- Built for point-to-point sync, not reasoning
- No entity unification across systems
- Flow-based, not graph-based
- Still requires writing integration logic
Works for data sync. Fails for understanding relationships.
Vector-Only RAG
Embed documents, retrieve by similarity
- Retrieves text chunks, not entities
- Misses business relationships
- No structured data understanding
- Semantic similarity ≠ business relevance
Works for document Q&A. Fails for business operations.
Each of these solves part of the problem.
None solve the whole problem.
Starting from What Agents Need
We asked one question: What do AI agents actually need to understand your business?
Four requirements emerged. Each one drove a specific architectural decision.
Unified Entity Model
Agents need to...
Know that "Customer X" is the same entity across Salesforce, NetSuite, and Zendesk
THE PROBLEM
Without this, agents ask about "Sarah Chen" in Zendesk but can't find her orders in NetSuite
OUR SOLUTION
We build a unified entity graph where every entity has one identity, connected across all systems
Relationship Awareness
Agents need to...
Traverse connections: this invoice → this order → this customer → their history
THE PROBLEM
Traditional databases require complex JOINs that agents can't efficiently generate
OUR SOLUTION
Knowledge graph stores relationships as first-class citizens—traversal is O(1), not O(n)
Temporal Context
Agents need to...
Know what happened when, in what sequence
THE PROBLEM
Without event history, agents can't understand "this customer complained after shipping was delayed"
OUR SOLUTION
Event store captures every business moment with timestamps and causal relationships
Speed
Agents need to...
Millisecond responses, not 15-second JOIN queries
THE PROBLEM
Agents make dozens of context lookups per request—latency compounds
OUR SOLUTION
Pre-computed relationships in graph structure enable sub-200ms context assembly
These four requirements led us to a specific architecture.
Let us show you why each layer exists.
The Architecture Stack
Four layers, each solving a specific requirement. Click any layer to see why it exists.
AI Context Layer
In-memory context graph for AI reasoning
WHY THIS LAYER?
AI models need structured, relevant context to reason accurately. This layer assembles precisely the right context—no noise, no gaps—formatted for optimal attention mechanisms.
TECHNICAL DETAILS
- •In-memory graph optimized for read-heavy workloads
- •Context windows assembled per-request based on query intent
- •Token budget management—never waste context on irrelevant data
- •Sub-50ms context assembly from cached relationships
Knowledge Graph
Relationships, patterns, and similar cases
WHY THIS LAYER?
Business entities don't exist in isolation. Every customer, order, product, and issue is connected to others. Graph databases make these connections first-class citizens, enabling O(1) traversal instead of O(n) JOINs.
TECHNICAL DETAILS
- •Neo4j-based property graph with typed relationships
- •Multi-hop traversal in single query (impossible with SQL)
- •Pattern matching finds similar cases automatically
- •Real-time sync from event store keeps relationships current
Event Store
Key business moments and temporal sequences
WHY THIS LAYER?
Business context requires time. "This customer complained" means nothing without "after their shipment was delayed." Event sourcing captures every business moment with timestamps and causal relationships.
TECHNICAL DETAILS
- •Append-only event log with immutable history
- •Temporal queries: "what happened between X and Y"
- •Causal relationships between events
- •Event replay enables graph reconstruction
RDBMS + Ontologies
System connectors and schema mapping
WHY THIS LAYER?
Every system speaks its own language. Salesforce calls it "Account," NetSuite calls it "Customer," Zendesk calls it "Organization." Ontologies provide the Rosetta Stone—mapping system-specific schemas to a unified entity model.
TECHNICAL DETAILS
- •Pre-built ontologies for common systems (Salesforce, NetSuite, Zendesk, etc.)
- •Schema mapping handles system-specific naming
- •Change data capture syncs updates in real-time
- •PostgreSQL for durable source-of-truth storage
Data flows up through the stack. Events trigger graph updates, which populate the context layer for AI reasoning.
The Trade-offs We Made (And Why)
Every architectural decision has trade-offs. We made ours intentionally.
Complexity vs. Capability
THE TRADE-OFF
The architecture is complex—four interconnected systems.
WHY WE CHOSE THIS
That complexity is what enables multi-hop reasoning across your entire business. Simpler architectures can't answer "show me all customers whose orders contained products from suppliers with delivery issues last quarter."
THE MITIGATION
You never see the complexity—we manage it. Your interface is simple APIs and dashboards.
REAL EXAMPLE
Try asking a simple chatbot to find revenue at risk from a supply chain issue. It can't. We can—because of the graph.
Upfront Investment vs. Long-term Agility
THE TRADE-OFF
Initial setup requires ontology mapping.
WHY WE CHOSE THIS
Once the ontologies are in place, agents can learn new relationships on-the-fly. Add a new system? The graph expands automatically. No re-integration, no custom code.
THE MITIGATION
We've pre-built ontologies for common systems—Salesforce, NetSuite, Zendesk, HubSpot. Most deployments: hours, not weeks.
REAL EXAMPLE
Add Shopify next year? Connect it, map it to the ontology, and every existing agent immediately understands it.
Centralized Graph vs. Distributed Queries
THE TRADE-OFF
We build one unified graph instead of federating queries to each system.
WHY WE CHOSE THIS
Federated queries are slow and brittle. Each system has different APIs, rate limits, and latencies. Pre-computed relationships enable millisecond traversal.
THE MITIGATION
Real-time sync keeps the graph current. CDC (Change Data Capture) streams updates as they happen. You're never looking at stale data.
REAL EXAMPLE
A federated query across 4 systems takes 3-8 seconds. Our graph traversal takes 200ms.
Pre-computed vs. Runtime
THE TRADE-OFF
We pre-compute relationships instead of querying on demand.
WHY WE CHOSE THIS
O(1) graph traversal vs O(n) JOIN operations. At scale, this is the difference between sub-second and minutes.
THE MITIGATION
Event-driven updates mean pre-computed doesn't mean stale. Changes propagate in seconds, not hours.
REAL EXAMPLE
A complex analytical query that takes 15 seconds in SQL executes in 200ms as a graph traversal.
Every trade-off was evaluated against the core question: What do agents need to understand your business?
We Applied What Works in Developer Tools
The complexity problem isn't new. Development tools solved it decades ago.
Package Managers
npm, pip, cargo
THE PATTERN
Declare your dependencies, let the system resolve the entire dependency graph automatically.
OUR APPLICATION
Declare your system connections, we resolve the entity relationships. Add Salesforce, NetSuite, Zendesk—we figure out how Customer, Account, and Organization are the same thing.
WHY IT WORKS
Developers don't manually resolve every npm dependency. Why should businesses manually map every entity relationship?
Container Orchestration
Kubernetes, Docker Swarm
THE PATTERN
Complex infrastructure underneath, declarative configuration on top. Write a simple YAML, get production-grade infrastructure.
OUR APPLICATION
Complex graph infrastructure underneath, simple Terraform deployment on top. Write 10 lines of HCL, get a fully operational knowledge graph.
WHY IT WORKS
Nobody manually manages container networking. Why should you manually manage entity relationships?
Language Servers
TypeScript LSP, rust-analyzer
THE PATTERN
Make implicit knowledge explicit and queryable. Every reference, every type, every relationship—indexed and instantly accessible.
OUR APPLICATION
Make implicit business relationships explicit and traversable. Every customer, every order, every issue—indexed and instantly accessible.
WHY IT WORKS
IDEs don't grep through files for references. Why should agents scan databases for relationships?
These patterns solved complexity in software development.
We applied them to enterprise data.
Complex Architecture. Simple Deployment.
The complexity is real. Managing it is our job, not yours.
module "adteco" {
source = "adteco/infrastructure/aws"
systems = [
"salesforce",
"netsuite",
"zendesk"
]
region = "us-west-2"
}
output "api_endpoint" {
value = module.adteco.api_url
}For Teams Who Want Control
- Deploy to your own AWS/GCP/Azure
- You own the infrastructure
- Full visibility into every component
- Customize for your requirements
- Data never leaves your network
Deployment time: ~15 minutes
Either way, the complexity stays with us. Your team consumes intelligence, not infrastructure.
Questions Enterprise Buyers Ask
We've been through enterprise evaluations. Here are the answers.
Need more details? Our solutions team can walk through security architecture, compliance documentation, or pricing models.
See the Architecture at Work
Watch a customer problem traverse from trigger to AI decision in real-time.
See the DemoReady to Explore Further?
Whether you want a technical deep-dive or a strategic conversation, we're here.