Agentic Knowledge Graphs with A2UI: Practical AI Use Cases for Real-Time Reasoning

Agentic Knowledge Graphs with A2UI unlock a new way to make AI reasoning visible, especially for high‑stakes domains like fraud detection, cybersecurity, supply chain, and financial risk analysis. By streaming small, context-specific graph snippets into the UI, AI agents can highlight suspicious patterns, explain decisions, and guide human experts through complex relationships in real time. This turns knowledge graphs from static back-end infrastructure into an interactive, explainable interface where users can explore, validate, and act on AI insights with far greater confidence.

In A2UI-driven Agentic Knowledge Graphs, the visualization itself becomes part of the agent’s reasoning loop, enabling humans and AI to co-navigate complexity in real time.

Lets Look at some examples:

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Example 1: Supply Chain Disruption & Rerouting

In this scenario, a supply chain agent detects a port strike. It doesn't just tell the user; it generates a graph to show the impact and the proposed solution.

The Scenario

• The Problem: The "Suez Canal" node is blocked
• The Agent's Task: Identify affected shipments and suggest a rail alternative

The A2UI Message Sequence

The agent sends these messages in a single stream:

[
  {
    "surfaceUpdate": {
      "surfaceId": "logistics-view",
      "components": [{
        "id": "route-map",
        "component": {
          "KnowledgeGraph": {
            "title": "Supply Chain Rerouting Logic",
            "layout": "cose",
            "data": { "path": "/graph/logistics" }
          }
        }
      }]
    }
  },
  {
    "dataModelUpdate": {
      "surfaceId": "logistics-view",
      "contents": [{
        "key": "graph",
        "valueMap": [{
          "key": "logistics",
          "valueArray": [
            { 
              "key": "0", 
              "valueMap": [
                {"key": "id", "valueString": "port-strike"}, 
                {"key": "label", "valueString": "Suez Blockage (Critical)"}, 
                {"key": "color", "valueString": "red"}
              ]
            },
            { 
              "key": "1", 
              "valueMap": [
                {"key": "id", "valueString": "shipment-A"}, 
                {"key": "label", "valueString": "Order #992 (Delayed)"}
              ]
            },
            { 
              "key": "2", 
              "valueMap": [
                {"key": "id", "valueString": "rail-option"}, 
                {"key": "label", "valueString": "Suggested Rail Path"}, 
                {"key": "color", "valueString": "green"}
              ]
            },
            { 
              "key": "3", 
              "valueMap": [
                {"key": "source", "valueString": "port-strike"}, 
                {"key": "target", "valueString": "shipment-A"}, 
                {"key": "label", "valueString": "blocks"}
              ]
            },
            { 
              "key": "4", 
              "valueMap": [
                {"key": "source", "valueString": "shipment-A"}, 
                {"key": "target", "valueString": "rail-option"}, 
                {"key": "label", "valueString": "reroute to"}
              ]
            }
          ]
        }]
      }]
    }
  },
  { 
    "beginRendering": { 
      "root": "route-map", 
      "surfaceId": "logistics-view" 
    } 
  }
]

See: supply-chain-disruption.json for working example

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Example 2: IT Infrastructure & Root Cause Analysis

An AI agent monitoring a network can use a graph to show exactly where a system failure is cascading. This helps IT teams move from "the internet is down" to "the database auth service failed."

Use Case Breakdown

Feature	Agentic Behavior
Node Types	Services, Databases, API Gateways, and Users
Edge Types	"Depends on," "Calls," and "Authenticates"
Dynamic Update	As the agent pings different services, it updates node colors from green to amber to red in real-time via dataModelUpdate

Agent Insight: "I have analyzed the 500 error logs. The graph below shows that the Payment Service is failing because the Vault-Secret-Manager is timing out."

See: infrastructure-failure.json for working example

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Example 3: The "Interactive Loop" (User Action)

This example shows how the graph becomes a UI controller.

The Flow

1. Agent Displays Graph: The agent shows a high-level view of a company's departments
2. User Interacts: The user clicks the "Engineering" node

3. Client Sends userAction:
   

   {
     "userAction": {
       "name": "node_click",
       "surfaceId": "org-chart",
       "context": { "nodeId": "eng-dept" }
     }
   }
   

4. Agent Responds: The agent doesn't refresh the page. It sends a dataModelUpdate that "unfolds" the Engineering node to show sub-teams (Frontend, Backend, DevOps)

Benefits of this Loop

• Information Density: You don't overwhelm the user with 100 nodes at once
• Discovery: The user "explores" the data, and the agent generates the "map" as they walk through it

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Example 4: Fraud Detection Network

A fraud detection agent identifies suspicious transaction patterns by analyzing relationships between accounts, merchants, and transactions.

The Scenario

• Detection: Agent identifies a ring of connected accounts making unusual transactions
• Visualization: Shows the network of suspicious accounts, shared devices, and transaction flows
• Agent Action: Updates graph in real-time as new transactions are processed

Key Features

• Risk Scoring: Node colors indicate risk levels (green = safe, yellow = suspicious, red = high risk)
• Temporal Analysis: Edge weights show transaction frequency and amounts
• Pattern Recognition: Agent highlights clusters of related suspicious activity

Agent Insight: "I've detected a potential fraud ring. 5 accounts are sharing the same device and making coordinated purchases within 2-hour windows."

See: fraud-detection-network.json for working example

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Example 5: Medical Diagnosis Knowledge Graph

A medical AI assistant helps doctors understand symptom relationships and potential diagnoses by visualizing patient symptoms, test results, and possible conditions.

The Scenario

• Patient Presentation: Multiple symptoms entered by the doctor
• Agent Analysis: Generates a graph connecting symptoms to possible conditions
• Differential Diagnosis: Shows probability weights on edges

Interactive Features

• Click a Condition: Agent expands to show recommended tests and treatments
• Add New Symptom: Graph updates to reprioritize conditions
• Filter by Specialty: Agent adjusts to show only relevant conditions

Agent Insight: "Based on the symptom constellation, I recommend ruling out Condition A (78% match) with these three lab tests."

See: medical-diagnosis.json for working example

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Example 6: Social Network Influence Mapping

A marketing agent maps social media influence to identify key opinion leaders and virality paths for campaign targeting.

The Scenario

• Campaign Goal: Launch a product with influencer marketing
• Agent Task: Identify users with highest reach and engagement potential
• Dynamic Updates: As campaign progresses, agent updates graph to show actual spread

Visualization Elements

• Node Size: Follower count
• Node Color: Engagement rate (darker = higher)
• Edge Thickness: Interaction frequency
• Clusters: Community detection showing distinct audience segments

Agent Insight: "This influencer has 10K followers but 5x engagement of the 100K account. I recommend targeting this micro-influencer cluster."

See: influencer-network.json for working example

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Example 7: Financial Portfolio Risk Analysis

A financial advisor agent visualizes portfolio correlations and risk exposure across asset classes.

The Scenario

• Portfolio Review: Client wants to understand concentration risk
• Agent Analysis: Maps assets, sectors, and geographic exposure
• Risk Simulation: Shows impact of various market scenarios

Agentic Capabilities

• Stress Testing: Agent updates graph to show portfolio under different market conditions
• Rebalancing: Suggests trades and shows the resulting risk profile
• Correlation Alerts: Highlights unexpected correlations that emerged recently

Agent Insight: "Your portfolio has 60% exposure to tech sector through both direct stocks and index funds. The graph shows hidden correlation risk."

See: portfolio-risk-analysis.json for working example

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Common Patterns Across All Examples

1. Context-Aware Generation

• Agents don't show generic graphs; they tailor the visualization to the specific problem
• Node and edge types are domain-specific

2. Real-Time Updates

• Graphs aren't static; agents send dataModelUpdate messages as situations evolve
• Users see live changes without page refreshes

3. Interactive Exploration

• Users can click nodes/edges
• Agents respond with deeper context or expanded views
• Two-way conversation through visualization

4. Actionable Insights

• Agents don't just visualize; they annotate with recommendations
• Color coding, sizing, and labels convey meaning
• Graph structure itself communicates analysis results

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Technical Implementation Notes

All examples follow the A2UI protocol:

1. surfaceUpdate - Declares the KnowledgeGraph component
2. dataModelUpdate - Provides node and edge data
3. beginRendering - Triggers visualization

The agent maintains full control over:

• Layout selection (breadthfirst for hierarchies, cose for networks)
• Data filtering (showing relevant subgraphs)
• Visual encoding (colors, sizes, labels)
• Update timing (when to push new data)

This enables truly agentic visualization where the graph is an active participant in problem-solving, not just a passive display.