How AI Agents Can Transform Software Development

AI agents are poised to transform the entire software development life cycle.  By acting as proactive collaborators, they can help analyze requirements, generate code, automate testing, and improve documentation. This allows development teams to focus on strategic, high-value work instead of manual, repetitive tasks. Sonar's latest guide explores how to responsibly integrate AI agents into your workflow, address the risks, and empower your developers to build better software, faster. #SDLC #CodeGovernance #CodeQuality #CodeSecurity #StateOfLLMs #DevSecOps #DevOps #SoftwareDevelopment

AI agents are poised to transform the entire software development life cycle.  By acting as proactive collaborators, they can help analyze requirements, generate code, automate testing, and improve documentation. This allows development teams to focus on strategic, high-value work instead of manual, repetitive tasks. Sonar's latest guide explores how to responsibly integrate AI agents into your workflow, address the risks, and empower your developers to build better software, faster. #SDLC #CodeGovernance #CodeQuality #CodeSecurity #StateOfLLMs #DevSecOps #DevOps #SoftwareDevelopment

AI agents are poised to transform the entire software development life cycle.  By acting as proactive collaborators, they can help analyze requirements, generate code, automate testing, and improve documentation. This allows development teams to focus on strategic, high-value work instead of manual, repetitive tasks. Sonar's latest guide explores how to responsibly integrate AI agents into your workflow, address the risks, and empower your developers to build better software, faster. #SDLC #CodeGovernance #CodeQuality #CodeSecurity #StateOfLLMs #DevSecOps #DevOps #SoftwareDevelopment

AI agents are poised to transform the entire software development life cycle.  By acting as proactive collaborators, they can help analyze requirements, generate code, automate testing, and improve documentation. This allows development teams to focus on strategic, high-value work instead of manual, repetitive tasks. Sonar's latest guide explores how to responsibly integrate AI agents into your workflow, address the risks, and empower your developers to build better software, faster. #SDLC #CodeGovernance #CodeQuality #CodeSecurity #StateOfLLMs #DevSecOps #DevOps #SoftwareDevelopment

AI agents are poised to transform the entire software development life cycle.  By acting as proactive collaborators, they can help analyze requirements, generate code, automate testing, and improve documentation. This allows development teams to focus on strategic, high-value work instead of manual, repetitive tasks. Sonar's latest guide explores how to responsibly integrate AI agents into your workflow, address the risks, and empower your developers to build better software, faster. #SDLC #CodeGovernance #CodeQuality #CodeSecurity #StateOfLLMs #DevSecOps #DevOps #SoftwareDevelopment

As AI reshapes the development landscape, we're facing an engineering productivity paradox. The solution isn't just about writing code faster; it's about building a smarter, more resilient development lifecycle. AI agents are moving beyond simple task automation to become genuine collaborators in software development. They offer the potential to proactively analyze requirements, architect modules, and run comprehensive tests. This is the future of the #SDLC! Read Sonar’s latest guide to learn how to embrace AI agents and solve the challenges of an AI-enabled development world. #CodeGovernance #CodeQuality #CodeSecurity #StateOfLLMs #DevSecOps #DevOps #SoftwareDevelopment

⚙️ Artificial intelligence is redefining the entire software development lifecycle (SDLC) — empowering teams to be more agile, efficient, and creative. 💡 In this new edition of our #Blogfest, Guillermo Vasconcelos, Software Development Practice Head, explains how we apply AI across every stage of development to accelerate results, enhance quality, and unlock the innovative potential of our teams 👉 https://bit.ly/4oS9HzB #AI #SoftwareDevelopment #Innovation #DigitalTransformation #Baufest #SoftwareEngineering #Automation #Productivity #DevOps #TechForGood #DigitalFuture

One thing I would add to Guillermo Vasconcelos's insightful piece: before claiming that AI improves productivity, we need a measured baseline. Without it, "AI impact" is just anecdotal. Teams should first capture delivery metrics (e.g. DORA: deployment frequency, lead time for changes, change failure rate, or mean time to recovery). Then apply AI, then re-measure. In my opinion the sequence is non-negotiable (what a strong guy! 🤣): 1. Baseline first: current delivery performance 2. Deploy AI on concrete use cases (stories, IDE agents, async agents, etc.) 3. Re-measure to convert perception into evidence of improvement and perhaps ROI. AI is changing not only how we build, but also how we demonstrate improvement. Without a baseline, ROI is inaccessible.

Is your Software Development Lifecycle (SDLC) ready for AI speed? 🤯 It’s old news that AI is being used to write code faster, but what downstream effects are we not hearing about? AI is blurring the lines between planning, development, and testing, transforming your delivery process into an interconnected, dynamic network. If AI is generating features in minutes, why are your builds and human review cycles still taking hours? The new bottleneck isn't execution.. It's validation. As industry experts, we’ve drawn out what the AI-driven SDLC looks like, why validation is the new competitive edge, and how to redesign your systems to match the machine's pace. Read the full deep dive below 👇 https://lnkd.in/g2Rt2WpB #SDLC #DevOps #CICD #SoftwareDevelopment #CircleCI

Embedding GenAI across our full SDLC - here's what actually worked in my experience (and what didn't): What Worked: - MCP integration (GitHub + Atlassian) for auto-generating architecture docs from legacy code - AI Agents building applications from requirements - GenAI-enhanced Playwright and unit testing improving coverage and speed - Governance framework enabling safe adoption Result: Improved process efficiency What Didn't: - Letting teams adopt AI tools without standards (created chaos) - Assuming AI would "just work" without deeper human involvement - Skipping the governance conversation (security/compliance issues emerged) The lesson: AI augmentation needs engineering discipline, not just enthusiasm. What's your experience with AI in SDLC? What's working for your teams? #AI #SDLC #EngineeringLeadership Here's an interesting article on AI-augmented SDLC. I would add Claude code to the development platforms.