Multi-Agent Inventory Intelligence Reference Architecture

Problem

Network inventory systems hold data across multiple domains — physical, logical, service, customer — and no single query model can efficiently traverse all of them. Traditional inventory queries rely on flat APIs that return incomplete relational context. When AI agents need to reason about network state, they require a query mechanism that respects domain boundaries while enabling cross-domain traversal.

Context and Assumptions

Source inventory systems are authoritative and expose TMF-aligned or proprietary APIs
A graph database (Neo4j or compatible) materialises inventory topology periodically or event-driven
Agent framework is LangGraph or equivalent with tool-use capability
Execution environment is OpenShift AI or equivalent Kubernetes-native ML platform
Human-in-the-loop is required for any write or remediation action

Reference Architecture

Architecture DiagramReplace with approved SVG

Multi-agent inventory architecture diagram

Supervisor agent orchestrating domain agents (Physical, Logical, Service, Customer) each backed by graph traversal. Replace with approved SVG diagram.

Component Responsibilities

Supervisor Agent

Receives intent queries, decomposes into sub-tasks, routes to domain agents, aggregates responses, applies reasoning.

Physical Domain Agent

Queries physical resource topology — sites, shelves, cards, ports, fibres — using graph traversal.

Logical Domain Agent

Traverses logical connectivity — VLANs, tunnels, pseudowires, IP paths — and correlates with physical layer.

Service Domain Agent

Retrieves service instances, dependencies, SLA parameters and associated customer contracts.

Customer Domain Agent

Maps customer accounts, contracts, affected sites and commercial impact from service state.

Graph Database

Neo4j (or compatible). Stores normalised inventory graph. Populated by ETL from source systems via TMF APIs.

Topology Canvas

Read-only visualisation layer consuming graph queries. Renders affected topology for human review.

Audit Logger

Captures every agent query, tool call and reasoning step with timestamps and source context.

Data Flow

1. User intent query arrives at Supervisor via API or operator interface
2. Supervisor decomposes query into domain sub-tasks
3. Domain agents invoke graph traversal tools against Neo4j
4. Graph returns typed node and edge results with relationship attributes
5. Domain agents format and return structured context to Supervisor
6. Supervisor synthesises cross-domain response and populates topology canvas
7. All interactions are logged to the audit store before returning to user

Security and Governance Controls

Read-only execution boundary

All agent queries are read-only. Write actions require separate approval workflow.

Source data trust boundary

Graph is populated only from authoritative inventory systems. No agent may write to the graph directly.

Query scope limiting

Each domain agent operates against a labelled subgraph. Cross-domain traversal is controlled at the Supervisor layer only.

Audit log integrity

Immutable append-only audit log. Every agent step, tool call and result is captured.

Human review gate

Any output that drives an action — remediation, change, notification — must pass through a human approval step.

Failure Modes

Stale graph data: ETL lag between source system and graph materialisation leads to outdated topology context
Domain agent timeout: individual domain agent fails to return within threshold — Supervisor applies partial-context reasoning or escalates
Graph query explosion: poorly scoped traversal returns excessive node sets — mitigate with depth limits and index-backed property filters
Hallucination on sparse data: agent generates plausible but unverifiable topology assertions — mitigate with citation requirements
Audit log gap: agent step executed without log entry — all agents must complete log write before returning result

What Is Deterministic vs. AI-Assisted

Deterministic vs. AI-Assisted

Deterministic

Graph traversal queries (Cypher / GQL)
ETL from source inventory systems
Audit log writes
Human approval gate logic
Topology canvas rendering

✦AI-Assisted

Query decomposition by Supervisor
Cross-domain synthesis and reasoning
Natural-language impact summaries
Anomaly pattern detection across graph state
Suggested remediation recommendations

Reference patterns must be adapted to the customer environment, product versions, security policies and vendor architecture. This pattern does not constitute a production design without site-specific validation.