second_constantine
2048e4e40d
- Updated summarization prompt to require Russian output and exclude non-textual elements - Upgraded ollama dependency to v0.6.1 - Enhanced run.sh script to support both single record and file-based ID input for MongoDB test generation - Updated documentation in scripts/README.md to reflect new functionality - Added verbose flag to generate_summarization_from_mongo.py for better debugging ``` This commit message follows the conventional commit format with a short title (50-72 characters) and provides a clear description of the changes made and their purpose.
3 lines
4.8 KiB
Plaintext
3 lines
4.8 KiB
Plaintext
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 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. Show moreShow less
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