Qyrus

Speed isn’t the problem anymore. Disconnected testing is. Teams today are running workflows across mobile, web, and APIs. But when data gets stuck in one step or visibility is limited to a single project, quality starts to fall behind. That’s what we focused on in our March release. With our latest updates to Test Orchestration, you can now move extracted data seamlessly across test steps and gain unified visibility across projects. From passing dynamic values across workflows to analyzing results across multiple projects in one view, it’s built to keep your testing connected end to end. On the API Testing side, we’ve added more control and reliability where it matters most. You can stop performance tests on demand, rely on accurate JSON path extraction for pre-request data, and automatically attach execution reports to Jira tickets so your team has the full context from the start. It’s all about removing friction between systems, teams, and workflows so you can focus on delivering quality at scale. 👉 Swipe through the carousel to see what’s new 👉 Explore the full release notes: qyrus.com/v6-16-0-march-… 👉 Ready to see it in action, book a personalized Qyrus Demo - qyrus.com/contact-us/?ut… #Qyrus #SoftwareTesting #TestAutomation #QualityEngineering #APITesting #DevOps #Automation #ReleaseNotes

Data issues rarely announce themselves. They slip into dashboards. They distort reports. They quietly influence the decisions that move your business forward. By the time someone notices, the impact is already real. Today’s enterprises don’t just manage massive volumes of data. They manage complex pipelines, layered transformations, and evolving business rules. And when visibility is limited, trust erodes fast. That’s where Qyrus Data Testing comes in. It brings AI-powered, no-code validation across your entire data lifecycle so you can detect issues early, validate logic confidently, and scale testing without adding complexity. With Qyrus Data Testing, teams can: • Validate data from source to target • Test transformation logic with precision • Verify business rules automatically • Support compliance and audit readiness • Execute up to 60% faster with significantly lower manual effort The result? Cleaner pipelines. Faster releases. Stronger decision-making. Because reliable data is not just a technical requirement. It is the foundation of digital trust. Explore how modern enterprises are transforming data quality: qyrus.com/solutions/data… #DataTesting #DataQuality #EnterpriseData #AITesting #DigitalTrust #Qyrus

@PythonPr This sheet may become a beginner's best friend, thanks for sharing.

How to build AI agents from scratch

@Ronald_vanLoon @Python_Dv This is a great reference sheet! Thanks for sharing.

#AgenticAI Terminologies by @Python_Dv #LLM #GenerativeAI #ArtificialIntelligence #MachineLearning

AI is accelerating development. But in enterprise environments, especially banking, speed without control is a risk you cannot afford. We are now in the era of the Hybrid Developer, where vibe coding is reshaping how applications are built. The real shift is not just in how fast code is generated, but in how intelligently it is tested. In Episode 1 of our Qyrus webinar series, we introduce a powerful new concept: Vibe Testing alongside Vibe Coding. Every new API. Instantly tested. No manual effort. This is how testing evolves from a bottleneck into a built-in, continuous layer of intelligence within development. Speakers: Ravi Sundaram, Ameet Deshpande, and Yadvendra R. 🗓️ March 25, 2026 ⏰ 8:30 PM IST | 10:00 AM EST | 7:00 AM PST If you are leading QA, engineering, or digital transformation, this is where you see the future of testing unfold. Save your spot: qyrus.com/post/the-hybri… #FinTech #VibeCoding #SoftwareTesting #AIinTesting #Qyrus #ShiftLeft #DigitalTransformation

Great teams don’t just grow. They evolve with the right people. At Qyrus, we’re excited to welcome four incredible professionals who bring fresh perspectives, deep expertise, and a shared passion for building what’s next. 👋 Meet our newest team members: Dhanabal Venkatapathy – Head of IT Sachin Petkar – Sr. Manager, Info Security 𝐒𝐨𝐰𝐦𝐲𝐚 𝐌 – Sr. Platform Engineer Dona Zacharias – Technical Content Writer From leadership in IT and info security to creativity in content and innovation in platform engineering, this group reflects the kind of diverse thinking and capability that drives real impact. We’re excited about what lies ahead and the role each of them will play in shaping the future of Qyrus. Join us in giving them a warm welcome 👋 #LifeAtQyrus #QyrusCrew #WelcomeAboard #TeamQyrus #Leadership

Enterprise QA teams don’t have a testing capacity problem. They have an 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Too many teams are still stuck in testing environments that look like this: • Manual-heavy QA processes • Slow, repetitive testing cycles • Fragile automation that constantly breaks • Separate tools for UI, API, mobile, and data • Rising costs with little increase in coverage And the default response has always been the same: 𝐀𝐝𝐝 𝐦𝐨𝐫𝐞 𝐭𝐞𝐬𝐭𝐞𝐫𝐬. But that approach doesn’t scale. What modern enterprises need isn’t more people doing the same work. They need 𝐬𝐦𝐚𝐫𝐭𝐞𝐫 𝐭𝐞𝐬𝐭𝐢𝐧𝐠 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐭𝐡𝐚𝐭 𝐜𝐫𝐞𝐚𝐭𝐞, 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐞, 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐞 𝐭𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭𝐥𝐲. In this video, Raoul breaks down why the future of enterprise QA is shifting toward: • AI-led test creation and orchestration • Unified testing across UI, API, mobile, and data • Reusable, self-evolving test assets • Cloud execution that runs thousands of tests instantly This is exactly the shift we’re enabling at Qyrus. If your team is still battling brittle automation, fragmented tools, and slow coverage growth, this perspective is worth watching. Explore Qyrus and see how modern QA teams are scaling coverage without scaling complexity. 👉 Request a demo: qyrus.com/contact-us/ 👉 Learn more: qyrus.com #SoftwareTesting #QualityEngineering #TestAutomation #AITesting #DigitalQuality #Qyrus

@khushabu_27 Thanks for sharing! It's 2026 and people need to be able to understand and utilize AI in order to stay ahead.

@e_opore Clear, easy to understand roadmap! Great share, thanks for this.

AWS (Amazon Web Services) Learning Roadmap |-- Cloud Computing Fundamentals | |-- What is Cloud Computing | |-- Cloud Models: Public, Private, Hybrid | |-- Service Models: IaaS, PaaS, SaaS | |-- Benefits of Cloud (Scalability, Availability, Cost Efficiency) | |-- Understanding AWS Global Infrastructure (Regions, Availability Zones) (DataCamp) |-- AWS Core Services | |-- Compute: EC2, Lambda | |-- Storage: S3, EBS, Glacier | |-- Databases: RDS, DynamoDB | |-- Networking: VPC, Route 53 | |-- Content Delivery: CloudFront (Naresh i Technologies) |-- Identity, Security & Access Management | |-- IAM Users, Roles & Policies | |-- Security Groups & Network ACLs | |-- Encryption (KMS, SSL/TLS) | |-- Access Control & Permissions | |-- AWS Shared Responsibility Model |-- Infrastructure & Deployment | |-- Infrastructure as Code with CloudFormation | |-- AWS CDK for defining infrastructure using code | |-- Elastic Beanstalk & App Deployment | |-- Environment Configuration & Scaling | |-- Automation of Infrastructure (Wikipedia) |-- DevOps & Automation in AWS | |-- CI/CD with CodePipeline & CodeBuild | |-- Containerization with Docker | |-- Kubernetes with EKS | |-- Monitoring with CloudWatch | |-- Logging with CloudTrail |-- Serverless & Event-Driven Architecture | |-- AWS Lambda Functions | |-- API Gateway | |-- EventBridge & Step Functions | |-- SQS & SNS Messaging Systems | |-- Serverless Application Patterns |-- Data Engineering & Analytics | |-- Data Storage in S3 | |-- Data Warehousing with Redshift | |-- Streaming with Kinesis | |-- Data Processing with Glue | |-- Querying with Athena |-- Machine Learning & AI on AWS | |-- ML Model Training with SageMaker | |-- AI Services (Comprehend, Rekognition, Polly) | |-- Model Deployment & Monitoring | |-- Building AI-powered Applications (Wikipedia) |-- Monitoring, Logging & Optimization | |-- Performance Monitoring with CloudWatch | |-- Distributed Tracing with X-Ray | |-- Cost Monitoring with AWS Budgets | |-- Resource Optimization & Scaling |-- AWS Certifications Path | |-- Foundational: AWS Certified Cloud Practitioner | |-- Associate: Solutions Architect, Developer, SysOps Administrator | |-- Professional: Solutions Architect Professional, DevOps Engineer | |-- Specialty Certifications (Security, ML, Networking, Data Analytics) (GeeksforGeeks) |-- Real-World AWS Projects | |-- Deploy a Full Stack Web Application | |-- Build a Scalable Microservices Architecture | |-- Create a Serverless API | |-- Deploy a Machine Learning Model on AWS | |-- Build CI/CD Pipelines for Cloud Applications |-- Continuous Learning & Growth | |-- Read AWS Documentation & Whitepapers | |-- Practice with AWS Free Tier Labs | |-- Join AWS Community & Developer Forums | |-- Follow New AWS Services & Updates | |-- Build Real-World Cloud Projects Get the AWS Handbook Ebook codewithdhanian.gumroad.com/l/tbpasf

We didn’t just talk about the future of DevOps today—we took it home. 🏆 We are incredibly proud to announce that Qyrus has won the award for 'Generative AI-Driven Transformation' at the 11th DevOps Conclave & Awards 2026! This recognition is a powerful testament to our mission of moving beyond traditional automation and building a new layer of intelligence with Agentic AI. A massive thank you to UBS Forums Pvt. Ltd. for hosting such an electric event in Bengaluru, and to everyone who stopped by the booth to connect with Ameet, Lowell, Shivani, Anamika, Vatsal, and Shreyas. This award belongs to the entire Qyrus team and our partners who are redefining what’s possible in Quality Engineering. The pipeline is evolving, and we are just getting started. 🚀 #DevOpsConclave #Qyrus #GenerativeAI #AgenticTesting #DevOps #DigitalTransformation #AwardWinner #QualityEngineering

CI/CD pipelines are faster than ever, but what do they actually guarantee? Traditional API testing validates structure, not meaning, creating a false sense of security at production scale. At the 11th DevOps Conclave & Awards 2026, organized by UBS Forums Pvt. Ltd., in Bengaluru today, our SVP of Product Engineering, Ameet Deshpande, took the stage to unveil a new frontier: Using Agentic Testing in DevOps Pipelines. The keynote also featured a powerful live demo of SEER in action by Vatsal Saglani, our Data Science Lead. 📉 The API Validation Gap The gap between what your pipeline tests and what actually breaks in production is widening. In fact, 73% of API production failures pass traditional CI. Modern systems live and die by their API layer, and schema-valid simply does not mean correct. 🤖 The Qyrus Solution: SEER API Evaluation Agent Qyrus replaces traditional assertion accumulation with intelligent, context-aware evaluation. SEER operates as an independent evaluation plane that validates business intent, detects semantic regressions, and scores release quality. It acts as a continuous quality gate, shifting from binary pass/fail outcomes to intelligent risk scoring. ⚡ Real DevOps Impact In recent enterprise deployments, Qyrus with SEER has delivered: 👉 67% reduction in production API defects. 👉 82% reduction in manual API review time. 👉 3.2x increase in team-reported release confidence scores. 👉 41% increase in deployment frequency. Check out the video snippet below for a glimpse of Ameet’s keynote on why traditional automation can no longer keep pace with the API-driven enterprise. Agentic Quality is the Next DevOps Layer. If you are at the Taj MG Road today, visit the Qyrus booth to explore SEER and meet Intelligent Application Management (iAM) by Qinfinite. Visit our website to connect with our team and see the future of API quality in action: qyrus.com/contact-us/ #DevOpsConclave #Qyrus #DevOps #DigitalTransformation #AgenticTesting #SoftwareEngineering #BengaluruEvents #QualityEngineering #CICD #AIinTesting #DevOpsCommunity #TestAutomation

The doors are open, the energy is electric, and the 11th DevOps Conclave & Awards 2026, organized by UBS Forums Pvt. Ltd., is officially underway at Taj MG Road, Bengaluru! If you’re navigating the QA complexities of modern enterprise engineering and automated delivery, come find the Qyrus team. As a Platinum Sponsor, we aren’t just talking about the future of testing; we’re showing you how to build it. 📍 Don't Miss the Main Stage Mark your calendars for the highlight of the morning: 🎤 Speaker: Ameet Deshpande, SVP of Product Engineering at Qyrus 🕜 Time: 11:00 AM IST 💡 Topic: Using Agentic Testing in DevOps Pipelines Ameet will be diving deep into how Agentic AI is shifting the paradigm from manual scripting to autonomous assurance within the DevOps ecosystem. If you're looking to achieve faster releases and smarter, self-healing pipelines, this is the session for you. 🤝 Visit the Qyrus Booth Stop by our booth to see our AI-driven orchestration in action. Whether you want a deep-dive demo or just a chat about the evolving DevOps and QA landscape, our experts are on the ground and ready to connect. Let’s redefine what’s possible in Quality Engineering together. See you on the floor! #DevOpsConclave #Qyrus #DevOps #DigitalTransformation #AgenticTesting #SoftwareEngineering #BengaluruEvents #QualityEngineering

@techNmak Really appreciate how you highlighted that everyone's journey is different. It's so true, not everyone is going to need to "get off" on a specific stop where someone else might need to stay longer at a specific stop. Great visual.

Most people try to learn AI randomly. I mapped the entire AI engineering journey into a metro system. The problem with most AI roadmaps: They're linear. Step 1, Step 2, Step 3. As if everyone starts at the same place and wants the same destination. But AI engineering isn't linear. It's a network. → A software engineer skips Python basics, jumps straight to LangChain → A data analyst already knows Pandas, needs Transformers next → A product manager wants RAG and Agentic AI, not CNNs → A researcher needs Ethics & Safety before deployment A metro map captures this reality. Generative AI Hub (Line 4) connects to: → Machine Learning Loop (you need Transformers first) → Applied AI Sector (where RAG becomes chatbots) → Tooling & Deployment (where demos become products) Career Launchpad (Line 8) connects to: → Every other line (skills from any track convert to job offers) Ethics & Safety (Line 7) connects to: → Deployment (you can't ship without guardrails) → Applied AI (real-world projects need fairness and privacy) The 8 lines: 🟠 Foundations - Python, Math, Git (boarding passes) 🔵 Machine Learning - Neural Nets, CNNs, Transformers (the heart) 🟡 Deep Learning Express - LLMs, Fine-Tuning, PyTorch (fast track) 🟢 Generative AI Hub - RAG, Diffusion, LangChain (the magic) 🩷 Applied AI - Agentic AI, Healthcare, Chatbots (real projects) 🟣 Tooling & Deployment - Cloud, Kubernetes, MLOps (production) 🔴 Ethics & Safety - Bias, Privacy, Governance (guardrails) 🟢 Career Launchpad - Portfolio, Interviews, Networking (job offers) You don't take every line. You don't visit every stop. Find where you are. Pick your destination. Transfer as needed. Bookmark this. Start today.

@sijlalhussain Like how you worded this: "It is who designs the system that decides how humans and AI work together." The systems should be designed to promote HUMAN flourishing through the advancement of AI, not a total replacement like so many are worried about.

🎯 The AI workforce debate often focuses on tools. The real shift is how organizations redesign work around humans and AI. Across consulting firms, tech companies, and research institutions, the frameworks are converging on the same principle: AI works best as a structured collaboration system. 1️⃣ Workflow Redesign Most frameworks move from simple automation toward human-AI teaming, where AI handles analysis or execution while humans guide judgment and oversight. 2️⃣ Leadership Blind Spot Many organizations deploy AI tools without redesigning roles, decision rights, or workflows, which limits the value AI can actually deliver. 3️⃣ Execution Risk Without clear human-AI coordination models, companies risk fragmented adoption where AI increases activity but fails to transform performance. The question is no longer whether AI will be used at work. It is who designs the system that decides how humans and AI work together. via Gartner, Microsoft, PwC, Deloitte, MIT, Harvard Business Review @corixpartners @Transform_Sec @Corix_JC @ILoveBooks786 @COSTESLionelEr @ramonvidall @RLDI_Lamy @FrRonconi @timo_vi @Nicochan33 @NathaliaLeHen @TCyberCast @arigatou163 @ricardo_ik_ahau @VivMilanoFSL @bulbi59 @BulbiT3ch @sonu_monika @EduFirst @TalentedLearn @MathildaLoco @quepasachico @faryus88 @ATSPGroup @bbailey39 @StrategyNDigita @emiratidancer

The countdown is almost over.⏳ Tomorrow, the energy begins at the 𝐃𝐞𝐯𝐎𝐩𝐬 𝐂𝐨𝐧𝐜𝐥𝐚𝐯𝐞 & 𝐀𝐰𝐚𝐫𝐝𝐬 2026 in Bengaluru and the Qyrus crew will be there in full force. At 11:00 AM IST, Ameet Deshpande, SVP of Product Engineering at Qyrus, will take the keynote stage to talk about a topic every DevOps team is thinking about right now. 𝐔𝐬𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐧 𝐃𝐞𝐯𝐎𝐩𝐬 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬. If you're working on faster releases, smarter automation, and truly intelligent quality engineering, this is a session you will not want to miss. 📍 Taj MG Road, Bengaluru If you're attending, stop by the Qyrus booth and say hello. We would love to connect, exchange ideas, and talk about what the future of testing looks like. See you tomorrow. 👋 #DevOps #DevOpsConclave #AgenticAI #SoftwareTesting #QualityEngineering #Qyrus

Most QA teams don’t struggle with automation. They struggle with 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐬 𝐢𝐧 𝐬𝐢𝐥𝐨𝐬. But modern software is not built in pieces anymore. It’s built as 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. That’s where Qyrus 𝐓𝐞𝐬𝐭 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 changes the conversation. Instead of managing disconnected scripts, teams can orchestrate 𝐖𝐞𝐛, 𝐌𝐨𝐛𝐢𝐥𝐞, 𝐚𝐧𝐝 𝐀𝐏𝐈 𝐭𝐞𝐬𝐭𝐬 𝐢𝐧𝐭𝐨 𝐨𝐧𝐞 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰. With visual flows, centralized data management, AI-driven decisioning, and built-in resilience patterns, testing becomes structured, reliable, and scalable. The impact is real: • Up to 70% 𝐟𝐚𝐬𝐭𝐞𝐫 𝐭𝐞𝐬𝐭 𝐜𝐲𝐜𝐥𝐞𝐬 • 80% 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐦𝐚𝐧𝐮𝐚𝐥 𝐞𝐟𝐟𝐨𝐫𝐭 • Fewer flaky failures draining engineering time This is not just automation. It’s 𝐠𝐨𝐯𝐞𝐫𝐧𝐞𝐝, 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰-𝐝𝐫𝐢𝐯𝐞𝐧 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 designed for modern DevOps pipelines. If you're thinking beyond scripts and want to understand how orchestration transforms enterprise QA, the full breakdown is in our latest blog 👉qyrus.com/post/ai-test-o… #TestOrchestration #QualityEngineering #DevOps #SoftwareTesting #Qyrus #AIinTesting #EnterpriseIT

@milan_milanovic Fantastic break down! Might have to use this in conversation with friends and family so they don't look at us funny when we explain some of these!

Most developers can name 10 types of testing But only a few can explain when to use each one Here's a quick breakdown: 𝟭. 𝗦𝗺𝗼𝗸𝗲 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Does the app even launch? 𝟮. 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Does each feature give the right output? 𝟯. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Do the modules still work when connected? 𝟰. 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Did your latest commit break something that worked yesterday? 𝟱. 𝗟𝗼𝗮𝗱 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Can the system handle real traffic without choking? 𝟲. 𝗦𝘁𝗿𝗲𝘀𝘀 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 What happens when you push past the breaking point? 𝟳. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Can someone exploit a hole you didn't know existed? 𝟴. 𝗨𝗜 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Does the interface behave the way a user expects? 𝟵. 𝗙𝘂𝘇𝘇 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 What breaks when you throw garbage data at the app? 𝟭𝟬. 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Does the system hold up after running for days, not minutes? Most teams test if features work. Few test if they survive. You don't need all 10 on every project. But knowing when each one matters makes the difference. Pick one that your team doesn't do today. Start there.

@Ai_Vaidehi Distinguishing these key differences is important! It helps others figure out which to use for specific tasks. Thanks for sharing!

Claude Vs ChatGpt 📚📘

The shift from traditional automation to 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 is one of the most exciting leaps we’ve seen in DevOps recently. If you’re heading to the 11𝐭𝐡 𝐄𝐝𝐢𝐭𝐢𝐨𝐧 𝐃𝐞𝐯𝐎𝐩𝐬 𝐂𝐨𝐧𝐜𝐥𝐚𝐯𝐞 & 𝐀𝐰𝐚𝐫𝐝𝐬 in Bengaluru, organized by the team at @ubsforums —you won’t want to miss our very own Ameet Deshpande, SVP of Product Engineering at Qyrus, as he takes the stage. Ameet will be diving deep into how agentic workflows are moving beyond simple "pass/fail" scripts to create pipelines that actually think, adapt, and self-heal. 𝐇𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 "𝐖𝐡𝐲" In a world of complex microservices and rapid-fire deployments, manual oversight is the bottleneck. Ameet’s keynote will explore how Qyrus is leveraging AI agents to: ☑️Predict failures before they hit production. ☑️Generate dynamic test cases that evolve with your code. ☑️Accelerate velocity without sacrificing quality. 📍 Where to find us: Topic:  𝐔𝐬𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐧 𝐃𝐞𝐯𝐎𝐩𝐬 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 Date: 𝐌𝐚𝐫𝐜𝐡 13, 2026 Time: 11:00 𝐀𝐌 𝐈𝐒𝐓 Location: 𝐓𝐚𝐣 𝐌𝐆 𝐑𝐨𝐚𝐝, 𝐁𝐞𝐧𝐠𝐚𝐥𝐮𝐫𝐮 It’s going to be a masterclass in the future of software delivery. Come say hi, and let’s discuss how agentic testing can transform your specific dev cycle. See you in Bengaluru! #DevOpsConclave #Qyrus #AgenticTesting #DevOps #SoftwareTesting #BengaluruTech #ContinuousTesting #AIinTesting

@BharukaShraddha Glad you pointed out this distinction! A lot of people will waste time and mental energy viewing these lanes as competing entities.

RAG, AI Agents, MCP, and A2A aren’t competitors. They’re different layers of the same AI system. Once you see this mental model, modern AI architecture becomes much clearer 👇 1️⃣ RAG = Better answers RAG is about retrieval + grounded generation. Flow: User question → retrieve relevant data → add context → LLM generates answer with sources. Use RAG when you need: • answers from company docs • reduced hallucinations • citations and traceability RAG solves the knowledge problem. Once the model has enough context to answer, its job is done. 2️⃣ AI Agents = Do the work Agents move beyond answering. They plan, act, and iterate. Typical loop: Plan → Observe → Act → Reflect → Repeat Use agents when tasks require: • multi-step decisions • tool usage • executing workflows in real systems • verifying outcomes Agents solve the execution problem. 3️⃣ MCP = Standard tool access MCP (Model Context Protocol) is the plumbing layer. It standardizes how LLMs connect to tools and resources. Think: “One universal interface for AI → tools.” Use MCP when you need: • consistent access to APIs and services • structured connections to SQL, CRM, files, etc. • fewer custom integrations MCP solves the tool connectivity problem. 4️⃣ A2A = Agents working together A2A (Agent-to-Agent) enables multi-agent systems. It handles: • agent discovery • delegation and routing • permissions and handoffs • events and status updates Use A2A when you have: • multiple specialized agents • distributed AI systems • cross-team or partner automation A2A solves the coordination problem. The real takeaway: Modern AI systems often look like this: RAG → grounding knowledge Agents → executing tasks MCP → connecting tools A2A → coordinating agents Different problems. Different layers. One architecture. The real AI shift isn’t better prompts. It’s better systems. #AI #RAG #AIAgents #LLM #AIArchitecture #GenAI