Hands-on AI learning at The AI Alliance's Developer Workshop

🚀 #Day08 of becoming an #AITester ** Today’s focus — MCP Integration 🤖 Shifting from traditional testing and exposing Promptfoo’s evaluation tools to AI Agents via the Model Context Protocol (MCP). This enables seamless collaboration between automated agents and structured evaluation frameworks. 🔹 Objective: To integrate Promptfoo’s eval capabilities into Claude Desktop (Anthropic) through MCP, allowing agents to run, analyze, and interpret test evaluations directly, thereby bridging the gap between AI testing tools and AI reasoning systems. 🧠 What I learned today - Configured Promptfoo eval tools as MCP-compatible endpoints for agent access. - Connected Claude Desktop to interact with Promptfoo evaluations in real time. - Setting up MCP servers and giving access to the required tools. - Observed how AI agents can initiate and interpret test runs, making testing workflows conversational and adaptive. - Understood how MCP unlocks interoperability and allows frameworks, tools, and agents to collaborate in the same testing ecosystem. 🧩 Next step: Explore multi-agent testing orchestration — where one agent designs prompts, another evaluates results, and Promptfoo serves as the testing core. 📁 Repository: https://lnkd.in/g_CnaEY9 💬 Have you tried connecting Promptfoo or other eval tools through MCP? Would love to hear how you’re bridging AI agents and testing workflows.

90% of AI projects fail to reach production, but how the top 1% succeed. - Start with the business problem, not the model.  - Secure a sponsor - Use production data from day 1. - Build MLOps early (MLflow, Kubeflow, monitoring). - Deploy a "minimum viable model" fast → iterate. - Automate retraining & drift detection #AIProjects #MLOps #MachineLearning #AIDeployment #DataScience #AITips #AITransformation #MLFlow #Kubeflow #AIDrift #AIInnovation #AIBestPractices #AIProductization #AIEngineering #AIMaturity #AIinAction #AIDrivenBusiness #DigitalTransformation

Experimenting with MCP: Built a simple MCP server that connects Promptfoo (testing framework in VS Code) with Claude Desktop. The goal was to see if I could make the testing workflow a bit more interactive by letting Claude access and work with test cases directly. It showcases how MCP enables AI systems to connect directly with developer tools — making the workflow interactive, contextual, and automated. MCP (Model Context Protocol) allows AI models to: • Access external tools, data, and APIs securely • Communicate seamlessly between local test environments and AI clients • Turn static chat into dynamic, tool-driven execution Instead of just chatting, the AI now runs real tasks — fetching test cases, validating them, and learning from the context. AI isn’t just a co-pilot — it’s a co-developer, deeply integrated with your ecosystem. What I practiced in this project: Working with MCP (Model Context Protocol). Building integrations between different systems. Exploring AI-assisted testing workflows. Setting up custom tool connections. #ClaudeAI #MCP #Promptfoo #VSCode #AI #DevTools #Automation #Experiment #Innovation #LLMOps #DeveloperExperience #LLM Evaluation

💡 Day 2 at The Complete GenAI Launchpad: Building a RAG Application from Scratch (Hosted by Product Space | Mentor: Arun Nandewal) If Day 1 was about understanding how AI agents think and act, Day 2 was about making them smarter and more factual through RAG (Retrieval-Augmented Generation). Here’s what I learned 👇 1. Why RAG matters --> LLMs can generate, but not always ground their responses. -->RAG bridges that gap by retrieving relevant, factual data before generating output, combining reasoning with context. 2. How it works --> Understood the workflow of embeddings -> vector databases ->retrieval chains. -->Built hands-on RAG prototypes using LangChain and Chatbase, connecting custom data to dynamic answers. 3. Key reflection --> RAG isn’t just a backend improvement, it’s a trust layer for AI systems. -->It’s what transforms an AI chatbot into a reliable knowledge engine. Every session in this launchpad is helping connect AI concepts ->architecture -> product impact. Excited for Day 3: Prompt Engineering Masterclass! 🚀 #GenAI #RAG #ArtificialIntelligence #AIagents #ProductSpace #LearningJourney #AIApplications

Missed our webinar on Rapise 8.6? The full recording and a concise recap of all the major updates are now available! This release delivers cutting-edge AI enhancements and powerful new features for building scalable, efficient test frameworks: 🧠 Seamless AI Integration: Native, secure, and context-aware generative AI features from Inflectra.ai, including AI-powered data and code generation. ✨ Enhanced Organization: New features for Global Definitions and shared assets, plus hierarchical Page Object organization for large frameworks. ⚙️ CI/CD Ready: Improved Rapise Launcher with JUnit XML reporting for seamless integration and a preview of the Rapise MCP Server for programmatic control. Don't miss out on seeing how Rapise 8.6 can transform your test automation 👉 https://ow.ly/jhtB50XqHBA #TestAutomation #AIinTesting #SoftwareTesting #Webinar #Tech #InflectraSoftware

Meet MetaGPT by DeepWisdom - the AI framework that runs like a full software company! From idea → product plan → code → documentation, all handled by AI agents working together. One prompt = an entire project. Build faster, collaborate smarter, and turn ideas into production-ready solutions in minutes. The future of software development is now powered by AI teams. #MetaGPT #DeepWisdomAI #AIFramework #AIAgents #SoftwareAutomation #AIProductDevelopment #IntelligentAutomation #FutureOfWork #TechInnovation #AgenticAI #Harivikashhh #Zenxai

The next evolution in AI infrastructure is here. Introducing  Storm MCP,  the open-source gateway that bridges Large Language Models with real-world enterprise systems. With Storm MCP, teams can: ✅ Connect LLMs directly with internal tools, APIs, and RAG data sources ✅ Maintain consistent communication through Anthropic’s Model Context Protocol ✅ Enable shared context for smarter and more reliable responses ✅ Scale AI workloads securely across enterprise environments Built for developers, designed for enterprises, Storm MCP makes LLM integration faster, cleaner, and production-ready. Explore what’s possible: https://tryit.cc/zb5T3 #AI #Developers #LLM #StormMCP #EnterpriseAI #OpenSource

Day 76 of Learning: Deep Dive into MCP (Model Context Protocol) The Model Context Protocol (MCP) is transforming how AI models, especially LLMs, connect and collaborate with real-world tools. Think of MCP as a universal language that allows AI systems to communicate with apps, APIs, and databases efficiently — making them smarter and more context-aware. What Exactly Is MCP? MCP provides a standardized way for AI models to access and use contextual data. Instead of relying only on a user’s text prompt, it enables LLMs to understand the environment they’re operating in — like your files, project structure, or app state. How MCP Works 1. Context Sharing: Apps expose context (e.g., code repo, documents, APIs). 2. Structured Access: The AI model requests specific context via MCP. 3. Intelligent Action: The model uses that context to take informed actions or provide precise responses. Where It’s Used • Developer tools (AI copilots that can navigate your files intelligently) • Data-driven assistants (that understand your workspace context) • Multi-agent systems (where several AI agents share information seamlessly) Example Imagine an AI code assistant that can see your project structure, analyze open files, and run tests directly — that’s MCP in action. Key Takeaway MCP bridges the gap between AI intelligence and real-world context, turning passive LLMs into active, context-aware agents. #Day76OfLearning #MCP #AIIntegration #LLM #AIAgents #ModelContextProtocol #TechLearning #Automation

I attended dAGI Summit 2025 in San Francisco—part of Open Source AI Week—and it delivered exactly what builders and enterprise leaders need right now: real patterns for agentic, interoperable, and governable AI systems. My key takeaways Agentic architectures, not monoliths. Multi-agent patterns (planner/solver/critic, tool-use, memory) beat single-LLM apps for reliability, latency control, and parallelism. Interoperability & controllability by design. Strong push for open standards (Linux-Foundation/PyTorch leaders) to make agents portable across runtimes, with typed tools, policies, and auditable traces. LLM systems engineering is now a discipline. Talks focused on throughput-aware inference, prompt/compiler hygiene, eval harnesses, and online guardrails—not just model choice. RAG is not the only hammer. Retrieval must be paired with stateful agents, workflow graphs, and data contracts; otherwise you hit ceilings on accuracy/observability. Governance, provenance, and safety moved from slideware to specs: event logs, signed tool calls, and policy-as-code to meet enterprise and public-sector requirements. Why it mattered: The Summit framed a choice between closed “digital feudalism” and open, verifiable AGI tooling—with concrete roadmaps (standards + evals + ops) to ship production-grade agents this year. #AI #AgenticAI #MultiAgentSystems #AIEngineering #MLOps #AIGovernance #OpenSourceAI #Observability #TrustAndSafety #LLMEvals #AGI

2022: The Year n8n Leveled Up! From smarter expressions to full-on API power, n8n took automation to a new level — making complex workflows faster, more visual, and incredibly collaborative. Here are the top highlights that reshaped how creators and teams build with automation 👇 1️⃣ Inline Expression Editor — Write logic faster, right inside node parameters. No extra pop-ups, no delays. 2️⃣ Undo/Redo — Finally! Reverse mistakes in a single shortcut (Ctrl/Cmd + Z). 3️⃣ Schema View — Visualize complex data structures effortlessly. 4️⃣ Data Pinning + Drag & Drop Mapping — Freeze, reuse, and visually connect data between nodes like a pro. 5️⃣ User Management & Workflow Sharing — Real-time collaboration with your team. 6️⃣ Community Node Repository — Discover and install nodes built by the global n8n community. 7️⃣ Sticky Notes (Markdown Supported) — Comment, organize, and document your workflows. 8️⃣ LDAP Support — Enterprise-level user access made simple. 9️⃣ Public API for n8n — Automate n8n with n8n. 🔟 Log Streaming — Manage logs from your favorite monitoring tools with ease. ✨ All these features combined made n8n one of the most powerful open automation platforms out there. 💡 At Asema AI, we simplify automation and help teams build smart systems that actually work. Subscribe https://lnkd.in/dBkGp7Ku for more real-world automation tutorials built in n8n, AI-powered workflows, and community insights. #Automation #n8n #AIWorkflow #OpenSource #Productivity #AsemaAI