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161 articles tagged Software Engineering.
Dave Kerr discusses the shift from code review to mental model synchronization in AI-assisted engineering. Key insights cover the 'build vs. buy' bias in LLMs, the critical role of Kubernetes for agent governance, and the necessity of evidence-based verification in agentic workflows.
An analysis of how agentic coding shifts software development from individual execution to collaborative planning. This brief explores the limitations of text-based interfaces, the role of visual boundary objects, and the strategic redefinition of PM and engineering roles in an AI-native workflow.
AMP leverages specialized AI agents within Slack to automate triage, code generation, and portfolio analytics for commercial and industrial solar assets. The company demonstrates how combining LLMs with quantitative tools like DuckDB enables high-impact operational insights. This case study highlights the shift from manual monitoring to autonomous, data-driven energy management.
Expert panel analysis on the 2026 AI landscape, focusing on the transition from model selection to system design. Covers agent orchestration, inference economics, context engineering, and the emerging physical AI sector. Provides actionable frameworks for enterprise adoption and risk mitigation.
ThoughtWorks Technology Radar Volume 35 identifies four critical themes for AI-driven development: multi-agent orchestration, model cost optimization, AI factory infrastructure, and harness engineering. This analysis provides actionable strategies for managing inference costs, ensuring agent accountability, and scaling AI workflows effectively within enterprise environments.
A strategic analysis of how software architecture impacts AI coding efficiency. The discussion reveals that AI amplifies existing code structures, making deterministic architectural boundaries and cycle-free dependencies critical for maintaining productivity and preventing technical friction in modern software development.
Nois AG leverages LLMs to transform model-driven development into a scalable B2B product. This analysis details the strategic pivot from regulated banking to general-purpose software, emphasizing the critical role of precise specifications and guardrails in achieving production-ready AI code generation.
A strategic framework for securing autonomous AI agents in software factories. Learn how to implement sandboxing, least privilege, and supply chain controls to mitigate risks from probabilistic AI actors.
An analysis of the strategic shift toward local-first AI architectures, the economic implications of edge inference, and the future of software consumption. This brief explores how small language models (SLMs) are replacing cloud LLMs for privacy and cost efficiency, and why interoperability standards will define the next era of B2B and B2C software.
The CAFES framework identifies context quality as the primary bottleneck in agentic workflows. This analysis details five dimensions for improving AI agent reliability, reducing costs, and mitigating security risks in enterprise software development.
Delivery Hero's SVP of Tech Foundations details the shift from decentralized AI experimentation to a centralized agentic stack. The company achieved 18x its initial Q1 targets for merged PRs by integrating autonomous agents into CI/CD pipelines. This analysis covers governance, cost control, and the roadmap to a fully autonomous software factory.
A comparative analysis of AI-generated versus human-authored Architecture Decision Records (ADRs) for a bicycle repair shop system. The study evaluates decision quality, technical trade-offs, and the strategic implications of using LLMs for software architecture documentation and reasoning.
This analysis explores the architecture of agentic software factories, focusing on the implementation of inner, middle, and outer feedback loops. It details how engineering teams can transition from manual prompting to autonomous, self-healing systems by treating code, context, and data as integral factory components.
An executive analysis of how AI integration is reshaping software architecture, testing rigor, and talent development. Key insights cover the monolith vs. microservice debate, the critical role of precise requirements, and the emerging gap in junior engineer training.
Sam Newman redefines microservices as a last-resort architecture for organizational autonomy. This analysis covers the three rules of distributed systems, the shift from code to spec-driven development, and pragmatic hedging strategies for AI integration in enterprise software.
An executive analysis of sustainable software development, focusing on the shift from code-centric to organization-centric architecture. Discusses the impact of AI on validation bottlenecks, the strategic application of Conway's Law, and the necessity of engineering excellence to mitigate AI-driven technical debt.
Peter Mattis, CTO of Cockroach Labs, discusses leveraging AI agents to produce high-quality, production-ready code. The analysis covers the shift from manual coding to agentic orchestration, the importance of strong consistency in distributed databases, and the strategic implications of AI for software engineering leadership.
A strategic analysis of the shift from proprietary to open-source AI models, driven by cost efficiency and supply chain sovereignty. The discussion highlights the critical need for rigorous verification frameworks, responsible AI governance, and a fundamental redefinition of software engineering roles in the age of generative code.
Maggie Appleton explores the convergence of design and engineering in the AI era. Learn how to leverage agentic coding for rapid prototyping, why physical sketching remains critical for spatial reasoning, and how to define product nouns for complex AI tools.
An executive analysis of how AI-native development shifts roles from coders to product engineers. Covers the collapse of traditional SaaS models, the rise of internal tooling, and the strategic imperative for trunk-based development in the AI era.
Stephen Sinofsky argues that anthropomorphic AI safety language obscures fundamental software engineering failures. By treating AI misalignment as standard bug fixing and implementing rigorous telemetry, the industry can avoid unnecessary regulation and improve operational reliability.
This analysis explores the critical shift from raw coding skills to agent orchestration in enterprise software. It details the DPACT framework for managing agent identity, emphasizing delegation, policy, and auditability to prevent security breaches and operational failures in autonomous systems.
Hari Krishnan discusses the launch of 'Spectrum Development' and the shift toward intent-driven development. The analysis covers token economics, the role of BDD and TDD in AI harnesses, and the strategic implications of autonomous software factories for enterprise engineering teams.
A strategic analysis of Kubernetes' role as an infrastructure schema, the shift from probabilistic AI agents to deterministic zero-token architectures, and the future of developer roles in an automated world.
Matt Pocock explores how AI agents are shifting engineering focus from tactical coding to strategic planning. This analysis covers the impact of legacy software principles on AI workflows, the rise of skill-based distribution, and the new role of developers as process architects.
Olaf Mohlfeld from CircleCI discusses the transition from manual CI/CD to autonomous software factories. The episode explores how AI agents are closing the loop between development and production metrics, enabling FinOps integration and reducing silo friction in enterprise engineering.
This analysis explores the strategic shift from bolting AI onto legacy processes to restructuring software development around dual feedback loops. It highlights the critical role of context engineering in bridging the gap between product ideation and execution, offering actionable frameworks for enterprise teams to maximize AI ROI and reduce organizational friction.
Mark Brooker of AWS discusses the future of agentic software development, emphasizing the shift from code writing to specification building. Key insights include the critical role of deterministic policy languages like Dogwood, the automation of on-call tasks, and the strategic value of agent memory for organizational learning.
An executive analysis of OpenAI's Codex team, covering the strategic decision to build in Rust, the benefits and costs of open-source development, and the shifting dynamics of code review and maintenance in the AI era.
Randy Schaup argues that LLMs shift software engineering from manual coding to harness design. Enterprises must adopt deterministic static analysis and automated evals to match machine-speed code production, mirroring semiconductor and pharmaceutical manufacturing standards.
Kilo Code co-founder Emily Shario discusses the strategic acquisition by Anaconda, the shift from code generation to review, and the new metric of spend per merged pull request. This analysis covers how AI-native organizations leverage agentic workflows to optimize ROI and redefine engineering leadership.
Kevlin Henney argues that AI is a multiplier of existing competence, not a solution to architectural debt. This analysis explores the shift from speed to effectiveness, the danger of failure demand, and the necessity of maintaining human cognitive control in AI-assisted development.
TESOL demonstrates how shifting from manual coding to agentic loops increases PR volume by 850 per week while improving quality. This analysis details the strategic transition from skills to autonomous factories, emphasizing context-centric governance and verifiable standards for enterprise scalability.
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