Architecture thinking with stable provenance.

Publication status: Published

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

Studies authority-bounded enterprise-agent execution and evidence-backed mission outcomes in a synthetic Class A research setting.

Public-review preprint, not peer reviewed or production proof. Historical MCR R1.9 is separate lineage with unresolved live CREATE commit state. The unchanged reviewer suite was 38/40 with raw runner exit 1; later reconciliation reached 40/40 without rewriting that result. Live integration, SAP business outcomes and generalized exactly-once execution are not established.

Publication status: Published

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

Studies minimum authoritative working sets and closed-loop outcome assurance for enterprise AI agents.

Public research preprint and reproducibility artifact, not journal or conference peer review. Negative and null findings remain preserved; Integrated assurance safety remains not established.

Publication status: Published

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

Presents an authority-aware context lifecycle and mission working-set architecture with public reproducibility evidence.

Version 1.1.0 preserves a held-out 28/29 result and a falsified strongest-authority assumption. It does not establish independent human validation. The earlier DOI is historical v1.0.0 lineage, not the preferred current citation.

Publication status: Published

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

Connects MCP tooling into a broader SAP full-stack engineering pipeline spanning ADT, Fiori, UI5 and governed delivery controls.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Frames token, context and tool governance as engineering controls for agentic AI applied to SAP development work.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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.

Publication status: Published

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.

Publication status: Published

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

Publication status: Published

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.

Publication status: Published

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.

Publication status: Published

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

Publication status: Published

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

Publication status: Published

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.

Publication status: Published

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.

Publication status: Published

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.

Publication status: Published

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.

Publication status: Published

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.

Publication status: Published

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

Examines why SAP reasoning in an agentic engineering platform must be grounded in target-bound, provenance-aware evidence before mutation or authorization decisions are made.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Frames observability as the ability to reconstruct why CAPELLA believes what it believes across requests, approvals, executions, SAP evidence and operator-facing states.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Separates successful execution from proof of final SAP outcome, emphasizing independent readback, deployment evidence and runtime validation boundaries.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Explains why a stopped CAPELLA execution can be a correct architectural control only when authority, SAP engagement, retry safety and unknown outcomes remain explicit.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Shows human approval as a bounded engineering authorization over frozen CAPELLA content rather than a generic human-in-the-loop gesture.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Positions n8n as workflow coordination around CAPELLA rather than the engineering authority for SAP mutations or approval decisions.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Explains why MCP exposes SAP engineering capability but does not itself grant permission, execution authority or proof of SAP state change.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Explains why AI capability, permission and execution authority must remain separate in SAP engineering, using CAPELLA AFEP to show bounded authorization, SAP-side authority and read-only evidence limits.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Explains why AI generation is not equivalent to SAP activation, deployment or execution, and why enterprise delivery needs governed lifecycle evidence.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Positions ERCC as the governed contract between AI-understood intent and enterprise-safe SAP engineering execution.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Describes the role of a semantic compiler in separating AI intent from deterministic SAP qualification and delivery controls.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Explains the CAPELLA engineering UX model through visible state, source authority, evidence and operator decision points.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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

Introduces CAPELLA AFEP as a governed path from AI intent to SAP RAP engineering with explicit source authority and evidence boundaries.

Public LinkedIn article; the original source remains the destination.

Publication status: Published

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.

Publication status: Published

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.