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

AI is no longer a research experiment or a novelty in the IDE: it is part of the software delivery pipeline. Teams are learning that integrating AI into production is less about model performance and more about architecture, process, and accountability. In this article series, we examine what happens after the proof of concept and how AI changes the way we build, test, and operate systems.

Across the articles, a consistent message emerges: sustainable AI development depends on the same fundamentals that underpin good software engineering—clear abstractions, observability, version control, and iterative validation. The difference now is that part of the system learns while it runs, which raises the bar for context design, evaluation pipelines, and human accountability. As teams mature, attention shifts from tools to architecture, from what a model can do to how the surrounding system ensures reliability, transparency, and control.

You will see this in practice here, from resource-aware model building and human-in-the-loop data creation to the use of layered protocols, such as A2A with MCP, that enable agents to discover capabilities and collaborate without requiring rewrites. Agentic architectures are no longer a thought experiment. Systems that coordinate, adapt, and negotiate are moving into production, and the safest path is incremental, with clear guardrails and shared workflows.

The InfoQ "AI Assisted Development: Real World Patterns, Pitfalls, and Production Readiness" article series captures where we are today: engineers turning experimentation into engineering, and AI moving from curiosity to craft. You can download the entire series collated in PDF format, in the associated eMag.

Series Contents

1. AI Trends Disrupting Software Teams

This article positions AI as the most significant shift in software since cloud computing, reshaping how teams build, operate, and collaborate. It highlights emerging trends from generative development to agentic systems, providing concrete guidance for developers, architects, and product managers as they adapt to this new era of AI-assisted software engineering.

ARTICLE BY: Bilgin Ibryam

2. Virtual Panel: AI in the Trenches: How Developers Are Rewriting the Software Process

The virtual panel titled shifts from observation to hands-on experience. It brings together engineers, architects, and technical leaders to explore how AI is changing the landscape of software development. Practitioners share their insights on what succeeds and what fails when AI is incorporated into daily workflows, emphasizing the significance of context, validation, and cultural adaptation in making AI a sustainable element of modern engineering practices.

PANELISTS: Mariia Bulycheva, May Walter, Phil Calçado, Andreas Kollegger HOSTED BY: Arthur Casals TO BE RELEASED: week of January 26, 2026

3. Why Most Machine Learning Projects Fail to Reach Production

This article takes a diagnostic approach, examining why many initiatives stall before delivery, from weak problem framing and brittle data practices to the gap between promising models and real products. It offers practical guidance on setting clear business goals, treating data as a product, building early evaluation and monitoring, and aligning teams to move from prototype to production with confidence.

ARTICLE BY: Wenjie Zi TO BE RELEASED: week of February 2, 2026

4. Building LLMs in Resource Constrained Environments

In this article, the focus shifts to exploring how limitations in infrastructure, data, and compute can drive innovation rather than hinder it. Drawing on real-world examples, it demonstrates how smaller, more efficient models, synthetic data generation, and disciplined engineering practices enable the creation of impactful AI systems even under severe resource constraints.

ARTICLE BY: Olimpiu Pop TO BE RELEASED: week of February 9, 2026

5. Architecting Agentic MLOps: A Layered Protocol Strategy with A2A and MCP

This article shows how combining Agent-to-Agent communication with the Model Context Protocol enables interoperable, extensible multi-agent systems for real MLOps workflows. It outlines an architecture that decouples orchestration from execution, allowing teams to add new capabilities through discovery rather than rewrites and evolve from static pipelines to coordinated, intelligent operations.

ARTICLE BY: Shashank Kapoor, Sanjay Surendranath Girija, Lakshit Arora TO BE RELEASED: week of February 16, 2026

About the Author

Arthur Casals is a researcher and technology strategist exploring the intersection of Artificial Intelligence, Distributed Systems, and Multi-Agent Architectures. With more than two decades of experience in software engineering and leadership roles, he focuses on bridging advanced AI concepts with real-world systems and development practices.

Context for Practitioners

This series is designed for engineers, architects, and technical leaders who are moving beyond pilot projects and into sustained AI integration. Each article addresses specific operational challenges:

• Process & Culture: How do teams adapt their workflows when AI becomes a collaborator? The virtual panel explores real-world patterns from teams that have successfully integrated AI tools into their daily development cycles.

• Production Readiness: The gap between a working model and a production system is often where projects fail. The diagnostic article provides a framework for identifying weak points early—starting with problem framing and data governance.

• Resource-Aware Engineering: Not every team has access to massive compute or proprietary datasets. The resource-constrained article demonstrates how efficiency and creativity can compensate for limitations, using techniques like model distillation, synthetic data, and careful architectural choices.

• Agentic Systems in MLOps: As AI systems grow more complex, coordination becomes critical. The A2A/MCP protocol article presents a layered approach to building multi-agent systems that can discover capabilities and collaborate dynamically, reducing the need for hard-coded integrations.

Why This Matters Now

AI is no longer a separate discipline; it's becoming a core competency of software engineering teams. The tools are evolving rapidly—from code assistants to autonomous agents—but the underlying principles remain the same: clear requirements, robust testing, observability, and iterative improvement.

The series emphasizes that sustainable AI development isn't about chasing the latest model or framework. It's about building systems that can learn, adapt, and remain reliable over time. This requires new patterns for evaluation, new approaches to data management, and new ways of thinking about system boundaries.

For teams starting this journey, the key takeaway is to start with fundamentals. Establish clear evaluation metrics before you scale. Design for observability from day one. Treat data as a product, not an afterthought. And when you introduce agentic systems, do it incrementally—with guardrails and shared workflows that allow you to learn and adjust.

Further Resources

• Download the full eMag containing all five articles in PDF format
• InfoQ AI, ML & Data Engineering topic for related articles and news
• Observability-First Development: Staying in Flow While Shipping AI-Assisted Software (Webinar Feb 10th)

The series will be released weekly starting January 26, 2026, providing a structured path for teams to assess their current practices and plan their next steps in AI-assisted development.