EQengineered

Orchestrating the Future: Mastering Agentic AI in Business (developed with AI) #AI #agenticenterprise #dataarchitecture #datareadiness #EQengineered https://lnkd.in/gb54kCN5

This EQengineered green paper lays out a comprehensive framework for C-level executives to navigate the change required ahead. The paper offers a detailed roadmap across five enterprise domains including strategy, architecture, deployment, governance, and operating models. A phased 24-month execution plan is provided. The recommendations are not a speculative vision; it is a practical blueprint grounded in our extensive experience gained through client delivery, industry insight, and technical acumen. The time for experimentation has passed. Agentic AI is here. The enterprise that learns to harness it first with focus, discipline, and integrity will lead the next decade of digital performance. #techleaders #enterpriseleaders #csuite #digitaltransformation #EQengineered

Three Takeaways for CEOs and Enterprise Leaders 1. Autonomy without judgment is risk without control. The future of AI leadership lies in dynamic governance that adapts as systems evolve. 2. Strategic AI doesn’t eliminate people, it elevates them. The most effective enterprises empower human leaders to oversee, refine, and evolve AI decisions. 3. EQengineered builds oversight into autonomy. We help organizations calibrate AI for speed, scale, and safety, without compromising human alignment. #csuite #leadership #responsibleAI #EQengineered https://lnkd.in/g_6trkp4

In the past year, coding agent models have changed a great deal, and they are becoming a powerful part of how software gets built. If you work at a company that writes or maintains code, you may soon be working with one of these agents yourself. This post will walk with you through three ideas: -1) How agentic tooling works as the way you interact with these models. -2) How to think about the model that serves as the “coding brain.” -3) How to look at benchmarks and leaderboards so you can make a fair comparison. EQengineered Author: Dakota Kim, Director of Engineering, Applied AI https://lnkd.in/g3RXwDqc

As the use of coding agents has become fully mainstream, it is now time to help teams on large projects work together to make their AI use more effective, rather than continuing to leave individuals to improve results by themselves. For a while now, developers have been working on their own, crafting prompts and paying attention to what is in the context of those prompts. At best, each has access to an instruction file for the project, such as Claude md, Gemini md, or copilot instructions md. They are usually generated by the agent itself after an initial review of the codebase. But even if you generate one big instruction file and place it into your repository, it tends to be forgotten quickly. Like all other documentation, it becomes outdated as the code changes. Problems in agent results are always seen as weaknesses in prompting or in the model, as no one remembers that the instructions file has an outdated pattern that’s causing confusion when applied to code that now does things differently. Even if you wanted to update the big markdown file, it can be intimidating. There is often a lot in it, from code samples to descriptions to architectural principles. Since these files have no defined structure, it can be a messy job to update it. And it is much easier to ignore that file and refine your own prompts to solve your generated code quality problems. In fact, many developers have their own highly-specialized techniques and tooling. They may even have multiple specialized agents with instructions that go beyond the one big file everyone has. Yet leaving AI effectiveness and improvements up to individuals results in wildly different rates of success and adoption on the team. Worse, non-standard code creeps into projects due to differences in techniques, too-fast PR reviews, and the amount of experience a developer has in a particular area of the code. When someone is working in code they do not know well, it is far more likely they will submit code that is not what the team lead wanted. A good way to get the team collaborating and improving results together is to move away from reliance on one instruction file and toward templates that tell agents exactly how to generate specific components. EQengineered Author: Ed Lyons, Principal AI Solutions Architect #AI #softwareengineering #softwarearchitecture #EQengineered https://lnkd.in/gdttcAMX

"The default way we interact with AI today is conversational. We type prompts into a chat box, get a useful answer, and move on. Prompts are treated as disposable. That works fine for ad hoc questions. But some prompts do more: they encode domain knowledge, processes, and standards in a repeatable way. By definition, software isn’t just code that runs on a machine. It’s instructions that guide execution, plus the design documents and specifications that explain how those instructions should work. Seen through that lens, well-crafted prompts qualify as software artifacts. And like software, they deserve to be saved, versioned, and improved. This post explores what it means to treat prompts as micro-apps, how modern tools like GitHub Copilot and Claude Code make this practical, and why combining prompts with traditional scripts creates workflows that are both reliable and intelligent." EQengineered Author: Dakota Kim, Director of Engineering, Applied AI #softwareengineering #AI #AIprompts #digitaltransformation #EQengineered https://lnkd.in/gsrkycZi