Teams running Claude or a similar assistant across dozens of sessions hit a familiar wall. The artificial intelligence (AI) forgets decisions made two prompts earlier and reintroduces bugs it already fixed, and the reasoning behind an architecture choice disappears with it. OpenSpec keeps agents aligned by storing that intent in version-controlled proposals, delta specs, designs, […]
AI coding assistants are now ubiquitous across software engineering teams, yet relatively few organizations have transformed how engineering itself operates. Teams write code faster while continuing to struggle with bottlenecks in requirements, specifications, testing, verification, compliance, and engineering governance. That’s because becoming an AI-native engineering organization requires changes across the entire engineering lifecycle. This article introduces the AI adoption maturity model, a practical framework for understanding […]
We see engineering teams adopting artificial intelligence (AI) coding agents, who hit a wall they didn’t expect. Their agents handle single functions cleanly, but the moment work spans multiple files, output falls apart because the agents have no memory of the architecture they are building against. Isolated functions are manageable, while multi-file infrastructure work requires […]
Artificial intelligence is transforming how engineering teams develop products, analyze requirements, and identify risks. But for organizations developing safety-critical or highly regulated products, the conversation is about doing so responsibly. Many engineering leaders face the same challenge: How can we introduce AI into engineering workflows while maintaining control over the development process? The answer isn’t simply choosing the right AI model. Successful […]
Artificial intelligence (AI) coding assistants can make individual coding tasks appear faster, while organizational delivery metrics lag. The speed may show up in commits and pull requests (PRs), while customer-visible delivery stays flat. Those gains get absorbed somewhere between the individual keyboard and the production release, lost to downstream bottlenecks. Vice Presidents (VPs) and Directors […]
One of the challenges I often hear from our automotive customers is that a large portion of time disappears into tasks that are both very manual as well as very tedious. An example of one of these tasks is scanning standards or regulatory documents, finding consistencies both between the standards and their internal requirements, then tracing them […]
Agent-first software workflows can begin with an empty git repository and a team of coding agents. By shipment, application logic, tests, continuous integration (CI) configuration, documentation, and internal tooling may all come from agents. The shift moves engineering work to a different layer. The job now is to design the specs, constraints, tools, and feedback […]
Requirements, code, and verification artifacts often live in different systems. Engineers switch between tools to understand requirements, implement functionality, and document traceability. By the time an audit arrives, teams may spend days or weeks proving that what they built matches what was specified. That gap shows up often in regulated engineering environments. Security requirements span […]
AI for Engineering Managers: Where It Helps and How to Start Six months into a major program, an engineering manager discovers that three separate teams have been building against different requirement baselines. Nobody catches the misalignment until integration testing, when two subsystems fail to communicate. Rework costs the program six weeks and an entire sprint’s […]
Imagine a state agency deploys an AI tool to help draft procurement requirements. The tool is fast, the outputs look good, and program staff start using it across multiple projects. Six months later, the inspector general requests documentation showing how a specific contract requirement was developed, who approved it, and how it connected to the original policy […]
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