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Software engineering has experienced major transitions before. Assembly programming gave way to compilers. Physical infrastructure gave way to the cloud. Each t...
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Agents are becoming increasingly common as AI capabilities make them easier to build and deploy. As a result, a familiar engineering question has returned: not ...
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When an AI system produces poor results, the model is often the first thing blamed. In many cases, however, the issue lies in the context surrounding the model ...
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Most software architectures assume that system behaviour is largely determined before deployment. Agentic systems challenge that assumption. When reasoning, too...
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Enterprise software has traditionally been built on deterministic behaviour. Financial systems, supply chains, customer platforms, and transactional workflows d...
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Building applications with agents involves more than creating individual agents. Developers must also decide how agents communicate, how workflows are coordinat...
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Google's source, build, test, continuous integration, and release systems are under unprecedented pressure as software development scales from tens of thous...
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Most production AI failures are not caused by poor models. They emerge from the systems surrounding them. As organizations move beyond prototypes and deploy AI...
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Enterprises face growing pressure to adopt AI, yet many lack a coherent security and governance architecture that can scale across teams, use cases, and busines...
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Many organizations invest heavily in monitoring and observability for AI agents, assuming that comprehensive visibility will improve safety. In practice, detail...
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The hardest part of building production research agents is not generating impressive answers. It is controlling the environment in which they reason. Many AI p...
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Most advice about coding agents focuses on prompts, context, or learning the fundamentals. In practice, many teams are encountering a different challenge: moder...
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Production AI agent systems can appear healthy by every traditional operational measure while delivering a steadily degrading user experience. Latency remains l...
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Discussions about AI autonomy often arrive at the same question: trust. In many organisations, trust in agentic systems is based either on confidence gained fro...
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Conventional wisdom suggests that adding more models should improve prediction quality. Our experience suggested otherwise. While building a high-fidelity pred...
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Prompting AI may be useful for generating code, but it is not a complete approach to developing and maintaining software applications. As teams gain experience ...
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Autonomous systems force software architects to answer a question that traditional applications rarely had to ask: where should analytical execution happen? As...
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Every senior engineer developed their judgment through years of writing code, debugging failures, and gradually learning what good engineering looks like. That ...
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AI can accelerate software development, but speed alone does not guarantee successful outcomes. As teams adopt AI-assisted development, the challenge becomes ma...
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Once agentic systems reach production, the model quickly stops being the hardest engineering problem. The greater challenge becomes understanding why the system...
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As teams adopt AI assistants for code generation, testing, security, documentation, and release decisions, CI/CD pipelines are becoming a natural place to integ...
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Automated testing and fast feedback loops are widely recognized as essential to software development, yet many teams struggle to adopt and maintain effective te...
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As software development scales from tens of thousands of human developers to millions of machine-speed developers, the assumptions that shaped traditional codeb...
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Most AI agent tutorials focus on capabilities. Production systems fail for entirely different reasons. Agents that perform well in development often encounter ...
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Engineering reliable AI systems requires more than deploying models. It requires the ability to observe, diagnose, and systematically eliminate production failu...
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As organizations explore the idea of software factories, there is growing interest in using AI to industrialize software delivery. However, coding agents introd...
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In the past, software security focused primarily on securing code. AI systems introduce a different challenge: inputs can influence model behaviour in ways that...
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Root cause analysis is one of the most challenging workloads for AI agents. When an incident occurs in a production system, the natural instinct is to gather ev...
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Most model pricing pages present inference costs as a straightforward function of token consumption. Production systems often behave differently. While measuri...
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When the same AI influences both code and tests, a green build is no longer sufficient evidence that software is correct. The code and its tests can reinforce t...
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AI is reshaping software engineering, creating uncertainty about how engineering roles will change as increasingly capable systems take on work that was once pe...
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Most discussions about AI agents focus on how they reason, plan, and call tools. Far less attention is given to what happens after an agent succeeds. In many pr...
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Application modernization is often delayed as teams balance technical debt against competing priorities. AI offers the potential to accelerate modernization eff...
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