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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17677764 |
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| _version_ | 1866901185707900928 |
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| author | Bisardi, Francesco |
| author_facet | Bisardi, Francesco |
| contents | <p>ABSTRACT<br>AI-assisted development promises substantial velocity gains, yet teams routinely fall short due to context loss and fragmented workflows. This paper introduces a two-layer context engineering architecture that treats context as a first-class system: (1) a declarative rule layer that encodes stable invariants, and (2) a programmatic Model Context Protocol (MCP) layer that exposes live project structure to AI agents. This architecture sits within a broader four-pillar synthesis consisting of an AI-optimized zero-friction stack, disciplined prompt workflows, the two-layer context system, and agentic orchestration patterns. We evaluate the approach through the development of a production-grade, multi-tenant SaaS platform (220k+ LoC) built by two part-time developers in fifteen weeks. Analysis of 3,676 AI-assisted sessions shows that Context Reuse Efficiency rose from 90.1% to 92.2% and Generative Amplification declined from 5.4% to 4.6%, consistent with a shift from generative substitution to retrieval-augmented reuse as context became systematized. Using a Design Science Research framework, we show that the two-layer pattern is feasible at production scale and produces measurable effects on development velocity. While based on a single case, the architecture and metrics establish a foundation for practitioners and researchers to validate and extend these practices across diverse contexts.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17677764 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | An Approach to AI High-Velocity Development Through Systematic Context Engineering: A Case Study Bisardi, Francesco LLMs generative AI MCP <p>ABSTRACT<br>AI-assisted development promises substantial velocity gains, yet teams routinely fall short due to context loss and fragmented workflows. This paper introduces a two-layer context engineering architecture that treats context as a first-class system: (1) a declarative rule layer that encodes stable invariants, and (2) a programmatic Model Context Protocol (MCP) layer that exposes live project structure to AI agents. This architecture sits within a broader four-pillar synthesis consisting of an AI-optimized zero-friction stack, disciplined prompt workflows, the two-layer context system, and agentic orchestration patterns. We evaluate the approach through the development of a production-grade, multi-tenant SaaS platform (220k+ LoC) built by two part-time developers in fifteen weeks. Analysis of 3,676 AI-assisted sessions shows that Context Reuse Efficiency rose from 90.1% to 92.2% and Generative Amplification declined from 5.4% to 4.6%, consistent with a shift from generative substitution to retrieval-augmented reuse as context became systematized. Using a Design Science Research framework, we show that the two-layer pattern is feasible at production scale and produces measurable effects on development velocity. While based on a single case, the architecture and metrics establish a foundation for practitioners and researchers to validate and extend these practices across diverse contexts.</p> |
| title | An Approach to AI High-Velocity Development Through Systematic Context Engineering: A Case Study |
| topic | LLMs generative AI MCP |
| url | https://doi.org/10.5281/zenodo.17677764 |