In this series, we examine what happens after the proof of concept and how AI becomes part of the software delivery pipeline.

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This article series explores the transition of AI from proof of concept to production systems, focusing on practical engineering challenges rather than model performance. It covers emerging trends in AI-assisted development, why ML projects fail to reach production, building LLMs under resource constraints, and architecting multi-agent systems using protocols like A2A and MCP. The series emphasizes that sustainable AI development relies on software engineering fundamentals like clear abstractions, observability, version control, and iterative validation, while addressing new challenges around context design, evaluation pipelines, and human accountability in systems that learn during runtime.

Article Series - AI Assisted Development: Real World Patterns, Pitfalls, and Production Readiness