The Inflectra Blog contains articles on all aspects of the software lifecycle.
October 5, 2026
As generative AI and autonomous coding tools sweep through the software industry, engineering teams are experiencing an unprecedented surge in development speed. Developers can now generate thousands of lines of code in minutes. However, this rapid acceleration has exposed a critical challenge for engineering leadership: How do we ensure that AI-generated code is production-ready, secure, and fully compliant?
In a recent joint webinar, Inflectra, Axiom Studio, and Rhythmic Technologies tackled this issue head-on. Featuring Adam Sandman (Founder & CEO, Inflectra), Ranjan Parthasarathy (Founder & CEO, Axiom Studio), Chris Daniluk (Founder & CEO, Rhythmic Technologies), and hosted by Bill Brown (Axiom Studio), the expert panel delivered a live, step-by-step demonstration on taking software from an initial backlog requirement all the way to production-ready code without sacrificing quality, security, or auditability.
Read MoreOctober 5, 2026
The Ultimate Shortcut to Executive Approval
If you are currently contemplating the modernisation, standardisation, transformation, or consolidation of your Software Development/System Deployment Lifecycle (SDLC), Test Orchestration, or Program Portfolio Management (PPM), you know that getting budget approval requires airtight justification.
To help you secure executive sign-off, we have compiled the core data points, competitive advantages, and real-world proof of value into a pre-populated business case framework. Use this guide to satisfy procurement, security, your CTO, and prove immediate Return on Investment (ROI).
Read MoreOctober 1, 2026
A recent Associated Press report published by PBS NewsHour highlights growing calls for stronger, independent evaluation of advanced AI systems. As AI agents take on more complex tasks and interact with real systems, successful outputs alone may not tell the whole story. What should teams evaluate before an AI agent reaches production?
For enterprise teams, the growing discussion around AI safety and evaluation has practical implications today. AI agents can use tools, interact with external systems, maintain information across multiple steps, and take actions within larger workflows. Software quality and engineering teams therefore need ways to determine whether those agents are ready for production.
Read MoreSeptember 28, 2026
Between the rapid rise of AI agents and the pressure for faster release cycles, modern testing teams are standing at a major turning point. At STARWEST 2026 in Anaheim, Inflectra took the floor as a Gold Sponsor to help enterprise software leaders navigate this transition safely. Our goal for the week was simple: help QA organizations move past the AI hype and adopt real-world solutions that keep systems trustworthy.
Here are the biggest highlights from our time on the Expo floor and in the session halls.
Read MoreSeptember 22, 2026
I recently had the opportunity to attend the Gartner Application Innovation & Business Solutions Summit in London, bringing together application, software engineering, and technology leaders to discuss how AI is reshaping the way organizations build and operate software.
Unsurprisingly, AI was everywhere. What I found particularly valuable, however, was that the conversation had moved beyond simply asking what generative AI can do. Much of the discussion focused on the harder questions: How do we build reliable AI systems? How do we govern agents without preventing innovation? How should our architectures evolve? How do we test systems that are inherently non-deterministic? And, perhaps most importantly, how do we turn individual productivity gains from AI into sustainable improvements across an entire software engineering organization?
Those questions are also very relevant to what we have been working on at Inflectra as we evolve Spira, Rapise, SureWire, and Inflectra.ai for an increasingly agentic software development lifecycle. Across the sessions I attended, several recurring themes stood out around context, architecture, testing, governance, observability, and the changing nature of software engineering productivity.
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