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In this issue, "AI Assisted Development: Real World Patterns, Pitfalls, and Production Readiness", we examine what happens after the proof of concept and how AI becomes part of the software delivery pipeline.
As AI transitions from proof of concept to production, teams are discovering that the challenge extends beyond model performance to include architecture, process, and accountability. Developers are learning to integrate AI into their delivery pipelines responsibly, designing systems where part of the workflow learns, adapts, and interacts with human judgment. From agentic MLOps and context-aware automation to evaluation pipelines and team culture, this transition is redefining what constitutes good software engineering.
This issue captures that evolution, with experimentation becoming engineering and AI assistance becoming a core part of modern software practice.
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This eMag includes:
- Bilgin Ibryam's "AI Trends Disrupting Software Teams" positions AI as the most significant shift since cloud computing, fundamentally reshaping how software teams build, operate, and collaborate. It guides developers, architects, and product managers through emerging trends like generative development and agentic systems.
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The virtual panel "AI in the Trenches: How Developers Are Rewriting the Software Process," moderated by Arthur Casals, featuring Mariia Bulytcheva, Phil Calçado, Andreas Kollegger, and May Walter, shifts the focus to hands-on experience. The panelists share essential insights on AI successes and failures within daily workflows, underscoring the need for context, validation, and cultural adaptation to ensure AI becomes a sustainable element in modern engineering.
- Wenjie Zi's "Why Most Machine Learning Projects Fail to Reach Production" diagnoses common failures, including weak problem framing and the prototype-to-production gap. The piece offers practical guidance on setting clear business goals, treating data as a product, and aligning teams for successful ML project delivery.
- Olimpiu Pop's "Building LLMs in Resource Constrained Environments" argues that infrastructure and compute limitations can drive innovation. It demonstrates how smaller, efficient models, synthetic data generation, and disciplined engineering enable the creation of impactful AI systems despite severe resource constraints.
- Finally, "Architecting Agentic MLOps: A Layered Protocol Strategy with A2A and MCP," by Shashank Kapoor, Sanjay Surendranath Girija, and Lakshit Arora, outlines protocols that create extensible, multi-agent MLOps systems. The core architecture decouples orchestration from execution, enabling teams to add capabilities via discovery and evolve operations from static pipelines to intelligent coordination.
We hope you find value in the articles and resources in this eMag and are inspired by these stories. We would love to receive your feedback via editors@infoq.com, on Bluesky, or on X (formerly Twitter). We hope you enjoy reading it!