AURA — THE CONTROL PLANE
AURA — Agentic Unified Runtime Architecture
Four components. One bundle. The only architecture that combines metadata, domain skills, live data collection, and closed-loop APIs — assembled per query, scoped to the domain, grounded in production.
ANI is what operators experience. AURA is what makes it possible — and what makes any agentic AI operational.
Built in production. Proven at scale.
THE STRUCTURAL PROBLEM
The three things agentic AI actually needs to be operational — and why owning two out of three isn't enough.
Governance vendors: Atlan · Collibra · Alation
Context
They give you ontology, metadata, semantics. The agent knows what "customer" means. It cannot answer: "which customers are at risk right now." Design-time intelligence, not runtime intelligence.
A dictionary is not a Control Plane.
Agentic frameworks: LangChain · Bedrock Agents · Vertex
The Right Slice of Data
They give you orchestration. But without a semantic and data layer, every tool call is a guess dressed up as an action. The framework fires — and gets the wrong data, or no data, or data it can't interpret in context.
Workflow without a Control Plane is faster failure.
Enterprise platforms: ServiceNow · Salesforce
Decision Integration
They give you workflows and systems of record. But generic horizontal — no domain ontology, no domain data. Telco is a use case. Not a native environment.
They hope telco is a use case. We were built by it.
"AURA solves all three simultaneously. That is the moat."
THE ARCHITECTURE
Not a dictionary. Not an orchestration layer. Not a data platform. All four — scoped, grounded, and assembled at runtime, not at design time.
Metadata & Ontology
Domain knowledge and entity relationships.
Closes the context gap. The agent knows what the data means — in this domain, in this topology.
Domain Skills
Vertical-specific reasoning rules.
Closes the judgment gap. The agent reasons like an HFC engineer, not a generic ML model.
Data Collection Endpoints
Live pulls from production systems at runtime.
Closes the data gap. Context without live data is metadata. AURA assembles both at runtime.
Closed Loop APIs
The action layer — decisions land in the real world.
Closes the integration gap. The agent's answer doesn't end in a chat window — it lands in an operational system.
SOVEREIGN BY ARCHITECTURE · PATENTED
Data Stays in Your Network
Raw telemetry, subscriber records, network topology — all processed at the edge inside your perimeter. Not a compliance policy. A patented architectural guarantee. The data never moves because the architecture never requires it to.
Edge Gateway
A lightweight process deploys inside your environment. LLM instructions flow in, execute directly against local data, structured results flow out. The model never sees raw subscriber data. The AI comes to the data — not the other way around.
Full Audit Control
Every data flow is inspectable. Every query is logged. Operator-configurable data retention. No black box. The architecture is designed for the operator who needs to show their security team exactly what moves and what doesn't.
"Patented. Sovereign by design. The only AI control plane that doesn't require you to move your data."
AWS BEDROCK · CO-SELL ARCHITECTURE
Telco is the proof. The architecture is horizontal.
The Gap
Bedrock is powerful — capable foundation models, strong orchestration via Bedrock Agents, compelling infrastructure story. The gap: complex, regulated, domain-specific verticals. Operators won't put sensitive network and subscriber data into a generic cloud AI pipeline. And without a grounding layer, LLMs hallucinate in vertical environments where wrong answers have operational consequences.
Two problems. Same root cause: no sovereign, domain-specific context layer between Bedrock and the operator's environment.
What AURA Provides
AURA runs inside the operator's perimeter. It feeds correctly scoped, semantically resolved, domain-grounded context to Bedrock at runtime. Sensitive data never moves. The model gets exactly the right slice — enriched, grounded, safe to act on.
The Semantic MCP Server pattern: deploy fine-tuned SLMs at the edge, keep sensitive data on-prem, send only structured metadata to the cloud. AURA is this pattern, production-proven in telco.
"We have an AURA architecture brief and co-sell documentation ready for AWS SAs. Let's talk."
Talk to an architect →SYSTEMS INTEGRATORS · REPEATABLE DEPLOYMENT
Deploy AURA once. Configure per customer. Your margin is in the relationship, not the plumbing.
The Current Reality
SIs win managed services contracts. Then spend 18 months building a bespoke AI layer per operator — every time, from scratch. Custom data pipelines. Custom ontology. Custom integration. Custom everything. Expensive. Slow. Not repeatable. Operators who've tried building it themselves end up with one use case that works, three that stalled, and a team maintaining custom code instead of running the network. Every engagement starts at zero.
The AURA Model
AURA is the repeatable Control Plane. Telco domain knowledge, DOCSIS reasoning rules, OSS/BSS connectors — pre-built and pre-validated in production. Deploy once. Configure per operator. This is not build versus buy — it's buy the platform, get immediate value, and build what's unique to your customer on top of it. Your team's value-add shifts from building the context layer to owning the operator outcome.
The plumbing is done. Your margin is in the value above it.
"Stop rebuilding the Control Plane for every operator. Deploy AURA once. Configure per customer. Your margin is in the relationship, not the plumbing."
SI architects — AURA deployment documentation and architecture deep-dives available on request.
Talk to an architect →THE CREDENTIAL
The hardest environment on the planet demanded all four layers simultaneously — or the system didn't work.
Production, Not Pilot
1.2M+ subscriber HFC network. 8+ source systems. Sovereign data requirements. A NOC that cannot afford a wrong answer. AURA was built because that environment required it — and because no existing vendor provided it.
Telco-Native by Necessity
Not adapted from generic ML. Not a telco skin on a horizontal platform. The DOCSIS domain skills exist because upstream SNR degradation in an HFC plant means something specific — and the operational consequence of getting it wrong is a truck roll and a subscriber call. The constraint built the capability.
AWS — Architecture Level
The architecture was evaluated at the infrastructure level — not as an application that happens to run on Bedrock. That is what co-sell architecture qualification means. It is not a badge. It is a technical statement.
13 Weeks to Production
LLA went from engagement to production in 13 weeks. Not a lab setup. Not a pilot environment. A live network at scale, with real alarms, real subscribers, real dispatch decisions. The onboarding architecture is as designed as the platform.
THE LANDSCAPE
The vendors you already work with are excellent at what they do. None of them crosses lanes. That is not a criticism — it is an architectural reality. A Control Plane requires all four simultaneously.
Context
Ontology, metadata, semantic meaning.
Lane stops at: design-time knowledge. The agent knows what the data means. It cannot query it.
Data
Live data collection, runtime access, source system connectors.
Lane stops at: the data layer. No domain reasoning. No action.
Orchestration
Agentic workflow, tool calling, LLM coordination.
Lane stops at: execution scaffolding. Without grounded context, every tool call is a guess.
Action Integration
Decisions landing in operational systems — workflows, network elements, ticketing.
Lane stops at: generic horizontal workflow. No domain intelligence. Telco is a use case, not a native environment.
HONEST ANSWERS
"This sounds heavy and long-cycle."
The ontology and domain skills are pre-built for telco — not authored from scratch per customer. The operator configures, not builds. There is no blank-slate onboarding. LLA was 13 weeks from engagement to production on a 1.2M+ subscriber network with sovereign data requirements. The architecture is designed to be deployed, not designed.
"Why won't AWS build this themselves?"
AWS builds the runtime. They don't build the domain. AWS doesn't know that upstream SNR degradation means something different than downstream SNR degradation in an HFC plant. DvSum does. Domain specificity is not something hyperscalers replicate — it's what they partner for.
COMMON QUESTIONS
AURA — Agentic Unified Runtime Architecture — is the Control Plane for agentic AI. It combines four components into one context bundle assembled per query: Metadata & Ontology, Domain Skills, Data Collection Endpoints, and Closed Loop APIs. ANI is what operators experience. AURA is what makes it possible.
Agentic AI needs three things simultaneously: context, the right slice of live data, and decision integration. Governance vendors own context — but a dictionary is not a Control Plane. Agentic frameworks own orchestration — but workflow without a Control Plane is faster failure. Enterprise platforms own workflows — but telco is a use case for them, not a native environment. AURA solves all three simultaneously. That is the moat.
AURA runs inside the operator's perimeter and feeds correctly scoped, domain-grounded context to Bedrock at runtime. Sensitive data never moves. It uses the Semantic MCP Server pattern: fine-tuned SLMs at the edge, sensitive data on-prem, only structured metadata sent to the cloud. This is what makes Bedrock operational in sovereign, domain-specific environments.
AWS builds the runtime. They don't build the domain. AWS doesn't know that upstream SNR degradation means something different than downstream SNR degradation in an HFC plant. Domain specificity is not something hyperscalers replicate — it's what they partner for.
The ontology and domain skills are pre-built for telco — not authored from scratch per customer. The operator configures, not builds. Liberty Latin America went from engagement to production in 13 weeks — a 1.2M+ subscriber HFC network with 8+ source systems and sovereign data requirements.
GO DEEPER
AURA documentation, co-sell briefing materials, and architecture deep-dives are available for qualified AWS SAs and SI architects. For operators evaluating the platform: we will run the architecture against your specific integration environment before you commit to a pilot.