Cross-System Labor

 

Managing Automation “labor-heavy” Documentation

Reducing the $100 Billion annual cost of Manual ETL:

Fusion (the synergy of IIS, ArcXA, and KGNN) is the missing link in the "Agentic AI" opportunity evolving from the adoption of Agentic AI. While many AI agents fail because they lack structured "memory" or context, Equitus.ai provides the high-fidelity data foundation required to move from basic automation to true coordination.

Fusion enables the conversion of labor into software spending, by automation of the ingestion process (IIS):

1. IIS: Capturing the “Unstructured” Coordination

90% of the market is uncaptured because coordination work is often buried in messy emails, PDFs, and chats.

• The Role of IIS: The Intelligent Ingestion System acts as the "sensory input." It doesn't just scrape data; it identifies entities and intent across fragmented silos. By automating the ingestion of "labor-heavy" documentation, it provides the raw material for agents to act without human hand-holding.

2. ArcXA: The Agentic Orchestrator

Move from SaaS to "Software as a Service-Provider," a system needs a "manager."

• The Role of ArcXA: Think of ArcXA as the connective tissue. It manages the workflows between the data and the execution layer. It ensures that when an agent receives a task, the "policy" and "security" constraints of the organization are enforced. It allows agents to navigate complex enterprise environments safely, turning high-cost manual oversight into automated software logic.

3. KGNN: The Brain of the “Fusion”

The biggest hurdle for Agentic AI is "hallucination" and lack of context. A Knowledge Graph Neural Network (KGNN) solves this by providing a Triple Store (Subject-Predicate-Object) memory.

• The Role of KGNN: It creates a "Digital Twin" of the organization's knowledge. Instead of an agent simply searching for keywords, the KGNN allows the agent to understand relationships (eg, "How does this supply chain delay in Asia affect my contract with Bob in London?").

• Data Advantage: As Bain suggests, winners use every deployment to capture data. In Fusion, every action an agent takes is fed back into the Knowledge Graph, making the system "smarter" with every iteration.

HAS ArcXA, builds evaluation sets to measure KPI's of Agentic Efficiencies:

EQUITUS AI | ARCXA Building Evaluation Sets for Agentic AI USE CASE · DATA GOVERNANCE INTELLIGENCE – Itelligent mapping Integration

Challenge: Eval Sets for Agentic AI Are Hard to Build:

Agentic AI systems must be evaluated on multi-step reasoning, tool use, constraint compliance, and data accuracy — yet most organizations lack the structured, verifiable ground truth needed to build rigorous eval sets. Manual curation is slow, brittle, and disconnected from real enterprise data.

 

How ArcXA Solves It — Improving (6) Core Capabilities

 

🗂   Schema & Lineage Discovery Auto-discovers data schemas and lineage to generate verifiable ground truth — agent outputs are checked against ArcXA's known-correct data map, not human estimates.				🕸   Knowledge Graph (KGNN) Models entity relationships across datasets, enabling multi-hop reasoning eval tasks — eg, trace a record through 4 system hops and return the authoritative value.
🔒   Data Governance Rules Machine-readable governance policies (PII, access controls, classification) power constraint-aware eval scenarios — testing whether agents halt, redact, or escalate correctly.				⚙   Migration Intelligence Schema-to-schema transformation pairs from real migration projects become natural input/output eval pairs — verifying agents produce the same validated result as ArcXA.
⚠   Anomaly & Drift Detection Surfaces real data anomalies and schema drift to feed adversarial edge cases directly into eval sets — building realistic complexity instead of synthetic toy problems.				📋   Audit Trails & Provenance Every action is logged with full provenance. ArcXA's audit layer provides replay capability — trace exactly what data state the agent operated on to explain eval pass/fail.
The Bottom Line: Most agentic eval frameworks fail because they lack grounded, structured, multi-domain data with verified correct answers. ArcXA turns your enterprise data environment into a living eval harness — ground truth continuously maintained, governance constraints machine-readable, and lineage providing full context for every test case.   ArcXA: Especially powerful for DoD and regulated-sector deployments, where eval rigor, auditability, and data governance compliance are mission-critical requirements — not optional extras.
Target Verticals:
🏛 Federal / DoD CJADC2, DFAS, DLA, Army G-6	🏥   Healthcare Clinical AI validation, EHR governance	💼   Financial Services Regulatory compliance, fraud detection			☁   Enterprise Cloud Migration governance, data ops

 

Equitus AI   ·   ArcXA Data Governance & Migration Intelligence Listed Awardable · CDAO Tradewinds Solutions Marketplace

github.com/equitus.ai/arcxa.com equitus.ai  · aimlux.ai

ArcXA as an Eval Set Foundation for Agentic AI

1. Schema & Lineage Discovery → Ground Truth Construction

ArcXA automatically discovers data schemas, relationships, and lineage across source systems. For agentic evals, this means you can generate verifiable ground truth — the agent's output can be checked against ArcXA's known-correct data map, not just a human's best guess.

2. Knowledge Graph (KGNN) → Multi-Hop Reasoning Evals

The Knowledge Graph Neural Network layer models entity relationships across datasets. This is ideal for building eval tasks that require an agent to reason across connected data — eg, "trace this record through 4 system hops and return the authoritative value." Exactly the kind of complex, multi-step task that separates capable agents from brittle ones.

3. Data Governance Rules → Constraint-Aware Eval Scenarios

ArcXA enforces data governance policies (access controls, PII handling, classification). You can build eval sets where the correct agent behavior includes respecting those constraints — testing whether an agent halts, redacts, or escalates appropriately rather than blindly executing.

4. Migration Intelligence → Transformation Accuracy Evals

ArcXA tracks schema-to-schema transformations during migrations. These transformation pairs (source state → target state) are natural input/output pairs for supervised evals — test whether an agent applying transformations produces the same verified result ArcXA would.

5. Anomaly & Drift Detection → Adversarial / Edge Case Evals

ArcXA's monitoring surfaces data anomalies and schema drift. Feed these edge cases directly into eval sets — agents should detect, flag, or handle them correctly. This builds evals with realistic adversarial complexity rather than synthetic toy problems.

6. Audit Trails → Eval Traceability

Every action in ArcXA is logged with provenance. For agentic systems where you need to explain why an eval passed or failed, ArcXA's audit layer provides the replay capability — you can trace exactly what data state the agent operated on.