How Ascendion's AAVA is revolutionizing enterprise AI with agentic AI.

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘄𝗿𝗶𝘁𝗲𝘀 𝗯𝗮𝗱 𝗰𝗼𝗱𝗲 – 𝘁𝗵𝗮𝘁’𝘀 𝘄𝗵𝗮𝘁 𝗺𝗼𝘀𝘁 𝗖𝗧𝗢𝘀 𝘁𝗵𝗶𝗻𝗸. 𝗔𝗔𝗩𝗔 𝗳𝗹𝗶𝗽𝘀 𝘁𝗵𝗶𝘀 𝗺𝗶𝗻𝗱𝘀𝗲𝘁. In our latest interview, Arun Varadarajan, Chief Commercial Officer at Ascendion, shares how 𝗔𝗔𝗩𝗔’𝘀 𝟰𝟬𝟬𝟬+ 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 co-engineer software with humans, adhering to enterprise-grade standards and producing production-ready code at scale. No hype. Pure next-gen engineering in action. ✅ 𝗧𝗢𝗣 𝗛𝗜𝗚𝗛𝗟𝗜𝗚𝗛𝗧𝗦 1️⃣ AAVA automates 90%+ of the SDLC, blending human oversight with AI-driven execution. 2️⃣ Projects shrink from years to months, at a fraction of the cost. 3️⃣ Effortless integration with existing tools and ecosystems. 4️⃣ SOC 2 compliance and full audit trails keep governance watertight. 5️⃣ Future-ready SDLC metrics: Digital Work Quotient (DWQ) and ROI on autonomy. 🔗 Dive into the full interview: https://lnkd.in/dzECJHMw #SDLC #SoftwareEngineering #Automation #AI #AgenticAI #AAVA #Ascendion #TechIntelPro

Today I'm sharing our structured approach to MLOps maturity transformation. After working with dozens of organizations struggling to operationalize their machine learning initiatives, we've developed a comprehensive framework that addresses the critical barriers to production AI. Our MLOps Maturity Acceleration Program begins with a rigorous assessment of your current ML deployment practices against established benchmarks. This evaluation provides a clear roadmap for improvement and identifies immediate opportunities for enhancement. The integration component connects your ML workflows with existing DevOps infrastructure, creating a unified development and deployment pipeline. This integration has proven particularly valuable for organizations with established software practices seeking to incorporate ML capabilities without creating parallel systems. Model governance—often the most challenging aspect for regulated industries—provides the necessary framework for compliance, explainability, and risk management. Our approach incorporates continuous monitoring with built-in drift detection to maintain model integrity over time. The automation of ML pipelines transforms manual, error-prone processes into reliable, repeatable systems. This automation extends beyond initial deployment to encompass continuous training, validation, and monitoring—essential elements for maintaining model performance in production environments. Performance visualization through custom dashboards translates technical metrics into business outcomes, enabling stakeholders across the organization to understand the impact of ML implementations on key performance indicators. Finally, knowledge transfer ensures your teams develop the internal capabilities needed for long-term success. This investment in human capital has consistently proven to be the differentiator between organizations that experiment with ML and those that create sustainable business value. For those interested in discussing how this framework might apply to your specific challenges, please reach out through our consultation portal. #MLOps #AIMaturity #EnterpriseAI #ModelGovernance #MLPipelines #DataScience #AutomatedML #DevOpsIntegration #MachineLearning #ModelDeployment #AIEngineering #BusinessIntelligence #DataDrivenDecisions #ModelMonitoring #AIStrategy #TechTransformation #AIatScale #ProductionAI #MLGrowth #DigitalTransformation

💡 The rise of “System Orchestrators” — the new full-stack engineer Everyone’s talking about “AI replacing developers.” But the real story? We’re watching the birth of a new engineering archetype — the System Orchestrator. Here’s what that means 👇 1️⃣ Traditional full-stack engineers built end-to-end features: frontend → backend → database. System Orchestrators now design flows: model → API → automation → outcome. 2️⃣ Tools like LangChain, n8n, and CrewAI are shifting value away from writing code lines toward designing logic chains — how systems think, trigger, and respond. 3️⃣ The skill isn’t “Can you build it?” anymore — it’s “Can you connect it, scale it, and measure its impact?” 4️⃣ As enterprises adopt LLMs, they need people who understand both engineering + orchestration — how models, data, and automation link into real-world business value. ⸻ 📈 What this means for engineers • The new full-stack is horizontal, not vertical. • Orchestration skills (API chaining, function calling, tool routing) will define career growth. • Those who can bridge AI reasoning + system automation will shape how every company delivers value. ⸻ 🧭 Action plan to stay ahead • Learn how to design multi-agent or multi-tool flows (LangChain, CrewAI, or n8n). • Focus on system thinking — not just syntax. • Build a portfolio showing workflows that improve efficiency, not just functionality. ⸻ 🔗 Sources • LangChain developer adoption data – Stack Overflow Trends (2025) • Automation platform growth – Gartner AI Infrastructure Forecasts (2024–2026) • Workforce transformation insights – McKinsey Future of Work Report #AI #Automation #SoftwareEngineering #FutureOfWork #LangChain #WorkflowAutomation #AIAgents #CareerGrowth #n8n #TechTrends #EngineeringLeadership #SystemOrchestrators

CIOs, CTOs and other digital trailblazers know the value of documentation, but DevOps teams have long had a love-hate relationship with writing and consuming it.   GenAI is changing that.   I recently spoke with InfoWorld about how GenAI tools are empowering developers, engineers and architects to produce and maintain accurate documentation.   This marks more than a tech upgrade, it’s a game-changing development.   Read the full article to hear why: https://lnkd.in/g58qiAim   #TEKsystemsGlobalServices #TGS #GenAI #TechDocumentation #TechModernization #TechLeadership

The Right Tool for the Right Context Complexity - The value of a system lies not in its cleverness, but in how faithfully its means match its purpose First comes scripted automation — a system that does exactly what you told it to, long after you’ve forgotten why. The next step up the evolutionary ladder is event-driven automation. Here, instead of hard-wiring cause and effect, you wire intent and observation. Agentic systems enter — not as magic AI toys, but as autonomous participants in the platform. Agents can set goals, observe outcomes, and adjust plans. They reason about intent, not just input. A scripted automation asks, “What next?”; an agent asks, “What matters?” The lesson is not that one model replaces another. It’s that each model belongs to a certain level of complexity. - Scripted automations are for the predictable. - Event-driven systems are for the observable. - Agentic systems are for the unpredictable. The wise platform engineer, working with the full gamut of options, doesn’t chase novelty — they choose proportion. https://lnkd.in/ghajAWzA #platformengineering #agentic #aiagents

In many companies, AI projects resemble never-ending lab experiments — teams keep testing and refining models, yet they never reach real users. The main reason? A lack of a clear delivery framework. That’s exactly where DevOps makes the difference: — it automates processes, eliminating manual steps in code building, testing, and deployment; — it ensures quality control through automated testing, monitoring, and rapid issue detection; —it unifies code, data, and models into a single CI/CD pipeline, reducing the time from experiment to release; — it guarantees safe deployment with canary releases, rollbacks, and controlled updates. Together with Devtorium, a software development company, AppRecode prepared an article explaining how CI/CD, IaC, and automation help turn ideas into production-ready solutions faster. Follow the link in the comments to read the full post on our blog and learn how to bring DevOps and AI together in your own projects. #AI #DevOps #Automation #SoftwareDevelopment #CICD

🚀 The Quality Revolution is Here—Are You Ready? The World Quality Report 2024-25 by Capgemini just dropped, and the findings are game-changing: ✅ 68% of organizations are now using or implementing Generative AI in quality engineering ✅ 64% prioritize data quality as mission-critical ✅ 72% report faster automation thanks to Gen AI integration ❌ But only 34% measure sustainability impact in testing The takeaway? Quality engineering isn't just about finding bugs anymore—it's about strategic advantage. Organizations that integrate AI thoughtfully, invest in data quality, and align QE with business outcomes will dominate their markets. Read the full article: https://lnkd.in/dYdYeNqC What's your biggest quality engineering challenge right now? #QualityEngineering #SoftwareTesting #GenerativeAI #TestAutomation #DataQuality #AgileQA #Sustainability #WorldQualityReport #TechLeadership #QA2025

Scaling AI in the enterprise isn't just about models; it's about automation, visibility, and governance. Moving AI from a concept to an enterprise-wide catalyst for innovation requires a new set of tools. GitHub's recent session highlighted a powerful, three-pronged approach to scaling AI effectively and securely. Here are the key technical takeaways for leaders: - Automate the SDLC with Custom Agents   Developers can now create repeatable "agentic patterns" to handle repetitive tasks. This frees them to focus on high-value, creative work instead of boilerplate. Examples include automating A/B test generation, standardizing UI components, or even migrating architecture. Best of all, simple Markdown definitions can empower non-technical staff to contribute to automation. - Measure Adoption with Copilot Metrics   You cannot manage what you do not measure. True enterprise adoption requires visibility. New dashboards and downloadable reports provide crucial insights into how AI tools are being used, tracking user interactions, usage patterns, and the effectiveness of different models and programming languages. - Govern Securely with the Agent Control Plane   As AI agents proliferate, governance becomes non-negotiable. The new Agent Control Plane provides a single pane of glass for managing and auditing all agent activity (both human and AI). This allows enterprises to administer licenses, configure models, and reinforce security and compliance across the board. This combination of specialized agents, clear metrics, and robust governance represents the future of developer and agent collaboration. #AI #GitHub #EnterpriseAI #SoftwareDevelopment #SDLC #DevOps #AIAgents #GitHubCopilot #TechInnovation #DigitalTransformation #AIGovernance

In the era of AI-assisted coding, Spec-Driven Development (SDD) echoes the Waterfall model's emphasis on comprehensive upfront specifications. While intended to harness AI's potential, this approach risks stifling agility and innovation in software teams. Enterprises must balance detailed planning with iterative flexibility to maximize productivity and competitive edge in digital transformation. #EnterpriseAI #DigitalTransformation #AgileDevelopment

The next era of Developer Experience (DevEx) is upon us. AI, automation, and data-driven insights are transforming the way developers work and innovate. Organizations now have the opportunity to reimagine DevOps to enhance speed, collaboration, and resilience. Explore these insights in Rocket Software recent blog: https://shorturl.at/UwwVQ