Architecture thinking with stable provenance.

By Prasad Deshpande · Source of record: LinkedIn · Original source: LinkedIn

Frames the SAP development architect role as moving beyond automation scripts into evidence-aware AI engineering, with stronger attention to governance, execution boundaries, validation and accountable architecture decisions.

Public LinkedIn article; the original source remains the destination.

By Prasad Deshpande · Source of record: LinkedIn · Original source: LinkedIn

Captures hands-on lessons from ABAP4CLayer experimentation, separating generated code, transport reachability, runtime validation, evidence receipts and human-controlled execution boundaries.

Public LinkedIn article; the original source remains the destination.

By Prasad Deshpande · Source of record: Architected by Prasad™ · Original source: Architected by Prasad™

A comparison of two ABAP4C Cloud Deployment Pipeline architectures: Standard ADT MCP and Capella Governed Bridge.

Architecture-level comparison with bounded evidence claims

By Prasad Deshpande · Source of record: LinkedIn · Original source: LinkedIn

Introduces the first ABAP4C architecture option as a standards-led route through coding-agent orchestration, Eclipse ADT MCP tooling and SAP ADT execution while preserving proof boundaries.

Public LinkedIn article; the original source remains the destination.

By Prasad Deshpande · Source of record: LinkedIn · Original source: LinkedIn

Documents a bounded SAP engineering experiment that connected Cline, Eclipse ADT and SAP ADT MCP from a requirement toward an active CDS view, emphasizing evidence, constraints and validation limits.

Public LinkedIn article; the original source remains the destination.

By Prasad Deshpande · Source of record: Architected by Prasad™ · Original source: LinkedIn

Original: 2026-07-10 · Website: 2026-07-11

What began as a personal website evolved into a living architecture lab spanning AI-assisted engineering, GitHub governance, Cloudflare deployment, automated validation, security, portability, and human-controlled production decisions.

Independent professional publication

By Prasad Deshpande · Source of record: Architected by Prasad™ · Original source: Architected by Prasad™

This publication presents an SAP Testing MCP Server built around a technology-specific Adapter Framework and a Seeded-Defect Discrimination Framework to create a measurable, evidence-driven quality gate.

Independent professional publication

By Prasad Deshpande · Source of record: LinkedIn · Original source: LinkedIn

A hands-on reflection on moving from individual SAP AI use cases toward a governed operating layer with evidence control, explainability, role separation, traceability and human authority.

Public LinkedIn post; the original source remains the destination.

By Prasad Deshpande · Source of record: LinkedIn · Original source: LinkedIn

An architecture field observation exploring the layer between enterprise retrieval and LLM reasoning. It examines how SAP evidence can be evaluated for freshness, completeness, object-version consistency, dependency coverage, authority and contradiction before being admitted into AI reasoning. Runtime figures in the original observation are local framework measurements, not complete end-to-end SAP agent latency.

Public LinkedIn field observation; the original source remains the destination.

By Prasad Deshpande · Source of record: LinkedIn · Original source: LinkedIn

Explores why SAP AI needs an operating layer for controlled evidence, explainable reasoning, role separation, telemetry and accountable execution rather than prompt engineering alone.

Public LinkedIn article; full-text republication is not included.

By Prasad Deshpande · Source of record: LinkedIn · Original source: LinkedIn

Assesses vibe coding for SAP through a practical architecture POC, including where rapid generation helps and where SAP context, validation and engineering controls remain essential.

Public LinkedIn article; full-text republication is not included.

By Prasad Deshpande · Source of record: LinkedIn · Original source: LinkedIn

Documents a practical integration breakthrough that made ABAP Trial RAP and OData capabilities consumable by custom AI agents while preserving a controlled SAP boundary.

Public LinkedIn article; full-text republication is not included.

By Prasad Deshpande · Source of record: LinkedIn · Original source: LinkedIn

Compares AI-generated technical interpretation with the process, configuration and operational context SAP experts use to explain why an issue occurred.

Public LinkedIn article; full-text republication is not included.

By Prasad Deshpande · Source of record: LinkedIn · Original source: LinkedIn

Summarises practical observations from testing major AI models against real SAP architecture, development and diagnostic scenarios, including the limits of generic reasoning.

Public LinkedIn article; full-text republication is not included.