Rapise 8.6 Webinar Recording and Recap

🚀 Day 7 of the #100DayChallenge Introducing MiniMax-M2 — a powerful, efficient, agent-first open-source LLM. Built with a Mixture-of-Experts (MoE) design: 230B params, only 10B active per inference. ⚙️ Optimized for coding & multi-step agent workflows — compile, run, test, fix. 💻 Supports tool use: shell commands, browser automation, and recovery steps. ⚡ Fast inference (~100 tokens/sec) and low-cost deployment. 🏆 Top scores on SWE-Bench Verified & “composite intelligence” benchmarks. 🧠 Efficient, scalable, and self-hostable — ideal for AI agents & dev pipelines. Empowering systems that can plan → act → verify → recover autonomously. #100DaysOfAI #AI #MachineLearning #DeepLearning #LargeLanguageModels #LLM #OpenSourceAI #AgenticAI #CodingAI #AIForDevelopers #AIWorkflow #AIModelBenchmark #MoE #ScalableAI #AIInfrastructure #AIInnovation #DeveloperTools #AIAgents #TechInnovation #AICommunity

When you’re building AI workflows, non-functional metrics matter as much as features - token usage/cost, latency, evaluation scores (precision/recall, RAGAS). I noticed there is yet no robust Langfuse integration with n8n to trace the metrics, so I've built my own. Workflow sends per-execution and per-step observations to Langfuse via the ingestion API: https://lnkd.in/dnjgP5Ed You can customize it for your requirements: - Add evaluation or feedback later via score-create (your RAGAS scores, F1 etc.) - Change sessionId strategy (per workflow, per user, etc.) - Adjust tokens and cost estimation before sending.

🚀 #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.

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

🧩 MCP (Model Context Protocol): The Missing Standard in Agentic AI As we move from LLM chat responses to AI systems that plan, act, and collaborate, one challenge keeps resurfacing: How do models interact with tools, memory, data sources, and each other — consistently and safely? This is where MCP — Model Context Protocol changes the game. --- What MCP Solves Today, every AI framework (LangChain, LangGraph, CrewAI, Autogen, custom orchestrators) handles tool-calling differently. This leads to: ➡️ Inconsistent interfaces ➡️ Hard-to-maintain integrations ➡️ Security & permission headaches ➡️ Difficulty scaling to multi-agent systems MCP provides a standard protocol for models to: ➡️ Access tools ➡️ Retrieve data ➡️ Update memory ➡️ Work across different systems Without custom adapters for every new integration. --- Why MCP Matters for Agentic Architectures Agentic AI requires: ➡️ Memory ➡️ Tool-use ➡️ Action execution ➡️ Multi-step reasoning For this to work reliably, agents need a common language for interacting with the systems around them. MCP ≈ The API contract layer for Agentic AI. It means: ➡️ Tools become discoverable ➡️ Capabilities become declarative ➡️ Context becomes shared and structured ➡️ Agents become interoperable This is how we unlock AI systems that are modular, auditable, and maintainable. --- Why This Is a Standard — Not Just Another Framework MCP is: ➡️ Transport-agnostic ➡️ Model-agnostic ➡️ Platform-agnostic It isn’t tied to OpenAI only — it’s designed so: ➡️ Any model can use it ➡️ Any toolset can expose it ➡️ Any agent runtime can orchestrate it This is the HTTP moment for AI tool-use. --- The Future If LLMs are the “brain” and tools are the “hands” — then MCP is the nervous system that connects them. The agentic ecosystem will only scale if: ➡️ Capabilities are standardized ➡️ Context is structured ➡️ Permission is controllable MCP is the foundation that makes that possible. --- 💬 What do you think? Do we need one unified standard for tool calling — or is the ecosystem still too early for consolidation? #AgenticAI #MCP #ModelContextProtocol #AIEngineering #SystemDesign #LangGraph #LangChain #OpenAI #MultiAgentSystems #SoftwareArchitecture #APIs

Recently, I tried Kimi K2 and here's what I found: Most models can chat or generate content, but when the task involves multiple steps, tool-use, reasoning, and follow-through, things get messy. Then came Kimi K2 Thinking from Moonshot AI. This new release uses a 1 trillion-parameter mixture-of-experts (MoE) architecture (32 billion active parameters) and is tuned for “agentic” workflows: reason → search → read → code → re-evaluate → repeat. On benchmark metrics, it outperforms many leading models in coding, reasoning, and tool-use. How it compares with other LLMs: Many top models excel in conversation or single-step tasks, but stumble on long chains of actions or tool integration. Kimi K2 Thinking is designed for those chains: it reportedly supports 200-300 sequential tool calls in one session. In pricing and openness too: while some proprietary models cost far more per token or restrict usage, Kimi K2 is open-source under a modified MIT license and delivers strong performance at lower cost.

We're excited to release MassGen v0.1.5, introducing persistent memory with semantic retrieval 🧠🚀! Agents can now retrieve knowledge across restarts and multi-turn conversations, enabling more specific answers. In the demo video, we showcase MassGen agents using crawl4ai to research the latest multi-agent AI papers in one turn and then suggesting what to implement from these papers into MassGen in the second turn. Demo video: https://lnkd.in/gc8XVPBh Release notes: https://lnkd.in/gYpCJPYG Feature highlights 🧠 Supports models from diverse providers — access a wide range of open-source and proprietary models (local, GPT-5, Claude, GLM, Gemini, and other enterprise backends). 👉 More Info: https://lnkd.in/gdXw-ZiN 🤖 Build collaborative multi-agent teams (in parallel) — agents share workspaces, generate, search, and vote on the best solution simultaneously. 👉 More Info: https://lnkd.in/gTH5prec 🗂️ Work with files and apps — Filesystem + MCP support lets agents read/write files, connect to web apps, and use tools. 👉 More Info: https://lnkd.in/g9YSxeZm 🔍 Work with existing projects — agents can read and edit project files with a robust permission system. 👉 More Info: https://lnkd.in/gUgTkpGp 🖼️ Multi-modal support — agents can understand and generate file, video, images and audios collaboratively. 👉 More Info: https://lnkd.in/gjchtgRn 🐳 Command-line execution — Each agent can run commands (with Docker support, if installed), eliminating dependency conflicts and enabling smoother collaboration across projects. 👉 More Info: https://lnkd.in/gSKtiFa4 🆔 Trace workflows — orchestration logs and workspace history make agent behavior easy to understand. 👉 More Info: https://lnkd.in/gecFJE2z

I have been reading recent pieces about AI agents joining CI/CD pipelines. They can automate tests, manage API calls, and help with orchestration. Datamatics shows agentic AI can run a full test lifecycle. DevOpsDigest warns agents will call APIs and this raises security, access and governance questions. Real repositories (GitHub) already show agent actions failing, so reliability is a concern. Sources like DZone and Ahex discuss patterns to add agents into pipelines. My takeaway: AI agents bring clear value, but we must start small, add strong monitoring, limits on API access, and clear rollbacks before wide use.

Anthropic Sonnet 4.5 vs Sonnet 4 – Real-World Computer-Use Test In just two months, there has been a significant leap in AI agent efficiency. Here’s a recent benchmark that caught my eye: Task: Install LibreOffice and make a mock sales table Sonnet 4.5 completed the task in 214 steps, with a clean workflow and no major detours Sonnet 4 took 316 steps and ran into multiple errors and distractions That’s a 32 percent efficiency gain in only two months. This isn’t a small improvement. Sonnet 4.5 is noticeably better at handling complex, multi-step tasks without getting stuck. It’s not about toy problems, but actual end-to-end workflows that used to trip up earlier agents. Why does this matter Benchmarks like these show that advanced language models are moving past hype and turning into practical digital workers. We’re now seeing AI agents reliably automate tasks that were considered difficult just a few months ago. If you want to dig deeper, check out the open-source framework and catalog at github.com/trycua/cua The pace of improvement is impressive. I’m genuinely interested to see where things stand two months from now.

Large Language Models (LLMs) are reshaping how we test, analyze, and assure software quality. By combining RAG, agents, fine-tuning, and LLMOps, organizations can now move from manual effort to intelligent, adaptive testing systems. 1.    Accelerate test creation and refinement with LLMs that simulate real-world scenarios and generate contextual insights. 2.    Leverage agents and RAG pipelines to retrieve precise knowledge and recommend optimal testing strategies. 3.    Fine-tune models to align with your domain-specific requirements. 4.    Implement LLMOps to ensure seamless deployment, governance, and monitoring of AI-driven test frameworks. LLM-powered testing is not just automation — it’s intelligence in motion, enabling teams to build scalable, reliable, and high-quality systems faster than ever. Know more about it here - https://lnkd.in/dCJkffz3 #VeritySoftware #TrainingExperts #LLM #GenAI