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Research Lab

Experiments behind the Living Digital Twin.

Evaluation notes, prototypes, reproduction paths and lessons stay here so the Digital Twin remains the primary decision experience.

Research modes

Inspect, evaluate, reproduce.

The Research Lab supports the Twin with experiments and lessons. It is not a competing AI surface.

TWIN

Interrogate the Digital Twin

Ask for proof, limits and provenance.

Open Twin
Challenge an architecture decisionFrame facts, unknowns and options.SAP Finance R2R OptimizerAssess posting configuration.SAP AI Agent DemoExplore governed agent patterns.
Enterprise agent trust and execution modelResearch trace from approved question to accountable decision.
01Approved business question
02Scoped enterprise evidence
03Identity and policy check
04Deterministic analysis
05Model-assisted explanation
06Confidence and trace
07Human decision
08Audit and replay
Architecture Evolution Timeline

Ideas evolve through evidence, not chronology alone.

Each node names problem, evidence, learning and next question.

  1. Observation

    AI vs SAP field observations

    Problem
    Generic AI can sound useful while missing SAP-specific evidence and operating context.
    Experiment
    Compare AI outputs against practical SAP architecture and diagnostic scenarios.
    Architecture
    Separate generated explanation from SAP evidence and expert accountability.
    Evidence
    Public LinkedIn observation series.
    Learning
    Evidence and context must be visible before AI advice is promoted.
    Next question
    How should an agent consume SAP evidence safely?
  2. Experiment

    Cline, Eclipse ADT and SAP ADT MCP

    Problem
    AI-assisted SAP development needs a controlled route from requirement to target-system evidence.
    Experiment
    Connect Cline, Eclipse ADT and SAP ADT MCP around CDS-view creation and activation.
    Architecture
    Standards-led ADT MCP path with proof boundaries.
    Evidence
    Public LinkedIn article and bounded architecture record.
    Learning
    Protocol success, activation proof and semantic correctness must be separated.
    Next question
    Can this become a repeatable ABAP Cloud architecture option?
  3. Architecture

    ABAP4C Architecture Option 1

    Problem
    ABAP Cloud automation needs an explainable architecture option with SAP execution controls.
    Experiment
    Model the direct AI or coding-agent to ADT MCP route.
    Architecture
    AI/Coding Agent to VS Code/Cline/Python to Standard ADT MCP to SAP ADT tools.
    Evidence
    Public article plus product architecture record.
    Learning
    A direct standards path is useful when scope, provider schema and proof limits remain visible.
    Next question
    Where does a governed bridge add control?
  4. Validation

    ABAP4CLayer hands-on lessons

    Problem
    Generated SAP code is not validated SAP capability until execution, receipts and boundaries are proven.
    Experiment
    Use an ABAP4CLayer architecture to test AI-assisted SAP code generation and automation controls.
    Architecture
    Governed bridge pattern with explicit execution and validation gates.
    Evidence
    Public LinkedIn lessons; public website describes only sanitized architecture boundaries.
    Learning
    Generated, activated and runtime-proven states must not be collapsed into one claim.
    Next question
    How does the SAP architect role evolve around these controls?
  5. Learning

    SAP architect to AI engineering

    Problem
    SAP automation work needs a broader AI engineering discipline, not just faster tooling.
    Experiment
    Synthesize architecture, validation, governance and human-control lessons into a role model.
    Architecture
    Evidence-aware AI engineering with deterministic authority and human production gates.
    Evidence
    Public LinkedIn article and website governance model.
    Learning
    The architecture role expands toward evidence systems, controls, release reliability and accountable AI.
    Next question
    Which public research question should be validated next?
Public Architecture Graph

Publication, product, research and evidence relationships.

Relationships come from explicit public data.

informs

active-sap-cds-view-cline-eclipse-adt-mcp → abap4c-cloud-deployment-pipeline-architecture-1

evidence_for

active-sap-cds-view-cline-eclipse-adt-mcp → mcp-abap-cloud

relates_to

abap4c-architecture-option-1 → abap4c-cloud-deployment-pipeline-architecture-1

informed_by

abap4c-architecture-option-1 → mcp-evaluation-framework

relates_to

abap4clayer-architecture-option-1-25-lessons → abap4c-cloud-deployment-pipeline-capella

compared_with

abap4clayer-architecture-option-1-25-lessons → abap4c-cloud-deployment-pipeline-architecture-1

evidence_for

abap4clayer-architecture-option-1-25-lessons → public-safe-sap-automation-evidence

informs

sap-development-architect-automation-to-ai-engineering → capai-engineering-control-center

related_to

sap-development-architect-automation-to-ai-engineering → responsible-autonomy

related_to

sap-development-architect-automation-to-ai-engineering → execution-traceability

Operating principles

Evidence before model assistance.

Open research agenda

Evidence first

Facts, unknowns and source boundaries stay visible.

SAP authority

The public Lab cannot execute SAP actions.

Risk differs from confidence

Impact and evidence reliability stay separate.

Human approval

Accountable professionals make decisions.

Research tracks

Architecture positions under active examination.

Open research agenda
01

Governed SAP AI agents

Use deterministic evidence collection first, LLM reasoning second, and explicit human approval for consequential actions. Open theme

02

Agent execution traceability

Expose stage, evidence source, tool call, policy decision, latency and failure state without leaking secrets. Open theme

03

Prompt drift and traceability

Version prompts, policies, tools and model context together and link them to execution evidence. Open theme

04

Performance architecture and thin loading

Classify capabilities by P1/P2/P3 criticality, load only when needed, measure every route and make operations switches explicit. Open theme

05

MCP and ABAP Cloud wrappers

Use read-only, scoped tools with clear schemas, audit and connection health; avoid embedding credentials in agents. Open theme

06

Responsible autonomy

Start read-only, add controlled actions only with policy, approval, rollback and audit. Open theme

07

LLM orchestration, RAG and vector databases

Use deterministic public or enterprise evidence first; add retrieval, vector search and model orchestration only behind explicit security, quality, freshness and fallback controls. Open theme

08

Telemetry beyond LLM calls

Measure user-perceived loading, rendering, APIs, backend work, database time, dependencies, policy decisions, retries, failures and recovery without collecting restricted content. Open theme

09

Human-in-the-loop operating controls

Define human checkpoints by risk, action scope, evidence quality and reversibility rather than placing approval at the end of every workflow. Open theme

10

Enterprise agent control planes

Use a federated control-plane model for identity, policy, catalogue, trace, release, telemetry and human accountability while preserving domain-specific execution. Open theme

Approved answers and grounded AI

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Ask a non-confidential question. Approved knowledge answers work directly; optional language enhancement uses approved website context only.

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Boundary: Responses use approved public website content only, remain advisory and never access live SAP systems or private environments.