How to add AI to your backend without rebuilding it

🧱 Pluggability, Discoverability & Composability — The 3 Superpowers of MCP 🎯 Scenario You add a new “Slack Notifier” tool without touching agent code — it just appears automatically. 📖 Definition MCP makes AI systems pluggable, discoverable, and composable so that agents can dynamically access new tools and data sources. 🧠 Analogy Like adding a new Chrome Extension — your browser (Agent) instantly recognizes it. 💼 Real-Time Example An MCP Server registers new modules: “Read Kafka,” “Post Slack,” “Get TestMetrics.” Any MCP Client can immediately use them. ⚙️ Usage QA can test scalability by dynamically adding/removing tools during runtime. 💡 Tip & Trick Use dependency mocks to validate discovery flow before production registration. 🧠 Memory Trick PDC = Plug, Discover, Compose. 🔚 Conclusion MCP’s modularity transforms AI development into enterprise-grade, testable, and scalable architecture. #ModularAI, #QATools, #MCP, #EnterpriseAutomation, #AIIntegration

𝐎𝐩𝐬 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 Leads, tickets, and payments don’t need “a place to check”—they need to come find you. — Webhook ingests → enrich with Clearbit-like data → route by ICP score. — If value ≥ threshold, auto-create a 3-line brief + DM the owner. — If value < threshold, archive with a human-readable log. Outcome: fewer tabs, faster replies, happier P&L. Ops isn’t another tool; it’s decisions pre-made. #n8n #Automation #NoCode #WorkflowAutomation #APIs #Ops

𝗣𝗮𝗿𝘁 𝟲: 𝗗𝗶𝘀𝗮𝘀𝘁𝗲𝗿 𝗥𝗲𝗰𝗼𝘃𝗲𝗿𝘆, 𝗖𝗼𝘀𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁, 𝗮𝗻𝗱 𝗙𝘂𝘁𝘂𝗿𝗲 𝗣𝗿𝗼𝗼𝗳𝗶𝗻𝗴 ⚙️ Building enterprise-grade MCP systems is not only about performance but also about resilience, efficiency, and adaptability. 🧩 𝗗𝗶𝘀𝗮𝘀𝘁𝗲𝗿 𝗥𝗲𝗰𝗼𝘃𝗲𝗿𝘆 Failures can happen anytime. Ensure business continuity with: ➥ Automated daily backups and geo replication ➥ Regular restore tests to validate data integrity ➥ Clear RTO and RPO targets with documented recovery steps ➥ Regular export of n8n workflows and credentials for quick restoration after incidents 💰 𝗖𝗼𝘀𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Optimize resources without losing speed or reliability: ➥ Right-size instances and use auto scaling ➥ Archive cold data to cheaper storage ➥ Tag resources for accurate cost tracking ➥ Set budgets, alerts, and analyze spending trends 💡 𝘛𝘪𝘱: Self-hosting n8n reduces integration costs by centralizing workflows and monitoring. ⚡ 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲 Utilize caching with Redis or Memcached, along with asynchronous processing, to achieve faster responses. n8n webhooks and queue triggers can handle long-running tasks efficiently in the background. 🚀 𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗦𝘆𝘀𝘁𝗲𝗺 ➥ Support multiple API versions ➥ Use abstraction layers for flexibility ➥ Add extensibility through plugins, webhooks, and community nodes n8n’s modular design helps integrate new tools easily without rewriting the core system. 📚 𝗧𝗲𝗮𝗺 𝗮𝗻𝗱 𝗚𝗿𝗼𝘄𝘁𝗵 Encourage detailed documentation, internal wikis, code reviews, and knowledge sharing. Participate in the n8n community for templates and workflow ideas. 📈 𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 Track developer productivity, faster AI feature delivery, cost savings, and improved user satisfaction. Continuous improvement keeps innovation strong. The future of AI development depends on scalable and standardized systems like MCP. Build strong today to stay ahead tomorrow. 🔗 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗯𝗹𝗼𝗴: https://lnkd.in/dBBicZeZ #AI #Automation #MCP #n8n #AIDevelopment #TechInnovation #CloudComputing #DisasterRecovery #CostOptimization #FutureProofing #Techstuff

*🔧 How to Build a Custom MCP Server to Power AI Agents with Smarter Memory & Control* Building your own *Modular Control Plane (MCP) Server* allows you to orchestrate multi-agent workflows, manage context-rich memory, and unlock advanced agentic capabilities in GenAI applications. 🚀 *Steps to Build Your Custom MCP Server:* 1. *Set Up Backend Infrastructure* Use *Node.js*, *FastAPI*, or *Express* for a lightweight server. 2. *Define Agent Interfaces* Set up endpoints for interaction between agents, tools, and LLMs. 3. *Implement Memory Storage* Integrate *Redis*, *Weaviate*, or *Pinecone* for vector-based semantic memory. 4. *Session & Task Routing* Use a router to manage conversations, multi-turn tasks, and workflows. 5. *Integrate Toolchains* Connect with APIs, databases, and custom tools via modular plugins. 6. *Secure & Scale* Add auth (JWT/OAuth2), logging, and containerization (Docker/K8s) for deployment. 🎯 *Boost Memory & Autonomy With:* - *LangGraph* for stateful graph-based agent flow - *AutoGen* for custom agent behaviors - *AgentOps* for logging, feedback, and monitoring #GenAI #AIInfrastructure #MCPServer #AgenticAI #LangGraph #AutoGen #AIagents #LLMOps #MemoryOrchestration

We’ve always built software around a simple triangle: frontend → APIs → database. But architecture is shifting. Today, systems are moving towards agent-based orchestration where your frontend doesn’t just hit rigid REST flows but interacts with an intelligent layer that can call capabilities dynamically. MCP (Model Context Protocol) is emerging as a standard to expose your existing services, DB access, external APIs, and even RAG knowledge sources as simple “tools” that AI agents can stitch together when needed. This doesn’t mean everything is an LLM call or that all requests should route through AI. Deterministic operations can still be direct / cached / normal. But where interpretation, reasoning and flexible workflow selection is required agents generate flows on demand and use MCP tools to execute. This is where modern architecture is headed, and it’s opening a new direction for system design that blends classical microservices with AI-native orchestration. #AI #Architecture #SoftwareEngineering #MCP #AIAgents #RAG #FutureOfSoftware #LLM #Microservices

📢Langchain are introducing agent middleware. A flexible way to inject custom logic before and after model calls, modify requests on the fly, and manage state in a composable fashion. This: Gives developers more control over context and tool orchestration without discarding the agent abstraction Enables built-in patterns (summaries, human feedback loops, caching) as middleware components Helps unify many advanced agent styles (reflection, swarms, supervisors) under a shared architecture If you’re building agents for production, or planning to, this is a significant leap forward. try the 1.0 alpha and the sample middleware available #AI #LangChain #AgentMiddleware #AIDevelopment

A practical AI win in my daily workflow I recently enabled JMX monitoring on one of our Tomcat servers to track performance metrics. The system collects statistics every ten minutes and emails me a summary at day's end. What caught my attention: Copilot's "Summarize" button in Outlook has become genuinely useful here. It quickly digests the data, highlighting key insights like heap usage trends, peak times, and significant changes—saving me from manually parsing through metrics. It's one of those quiet AI integrations that just works. No fanfare, just practical value added to an existing workflow.

🎉https://lnkd.in/gJb-Z4vy github.com /deepak-101-dev /dynatrace-mcp-managed-version TL;DR: Built an open-source MCP Server for Dynatrace Managed Version that lets you control, query, and automate your Dynatrace environment directly from Cursor IDE using natural language. I was honestly tired of googling every single thing while trying to monitor services, understand anomalies, or dig through problems in Dynatrace Managed environment. Switching between dashboards, APIs, and docs just to answer a simple “why is this service slow?” started to feel inefficient, especially when I was already using AI in my workflow daily. But the problem was clear: There was no intelligent layer or MCP server that could sit in front of Dynatrace Managed and interact with it contextually, without me googling the concept and guiding AI to help me further. So I decided to build one. Here's my experiment to build something open source that goes beyond code assistance and start managing real systems. Something "Truly Agentic" Introducing Dynatrace MCP Server (Managed Version) - a Model Context Protocol server that connects your Cursor IDE directly to Dynatrace Managed Version. It brings intelligent, local, and secure control over your environment. With it, you can: - Fetch and triage problems intelligently - Query and analyze performance metrics - Manage dashboards - Enable maintenance events - Automate monitoring operations - all this through natural language The server exposes 38+ tools across 7 categories, runs fully locally, and is open source. Essentially, it gives your AI assistant real control and context over Dynatrace - instead of just guessing or generating generic advice. It’s part of my ongoing journey experimenting with AI agents, trying to bridge the gap between monitoring data and intelligent decision-making inside development workflows. #AI #Dynatrace #Observability #MCP #DeveloperTools #CursorIDE #Monitoring #OpenSource #Automation #AIAgents

Typing what you want and getting a working automation is no longer a demo—it’s shipping. n8n has released an AI Workflow Builder that generates workflows from plain-English prompts. Built into v1.115.0, it helps non-technical teams prototype and share automations faster while letting developers refine logic with code when needed. For product and ops teams, this could cut handoff time and make experimentation safer inside the same governed platform. Sources: https://lnkd.in/ervx2Cun https://blog.n8n.io Would you prefer using the n8n MCP Server?

Why do most enterprise modernization projects stall before they start? In our latest insight, Devox Software’s CEO Kostiantyn Gitko unpacks the real-world strategy behind transforming massive legacy systems, without halting business-critical operations. What you’ll learn: ▪️ Why full rewrites are a trap, ▪️ How to untangle legacy safely, one slice at a time, ▪️ Where AI fits into the modernization equation, ▪️ What high-trust, high-visibility teams do differently, ▪️ The mindset shift that turns legacy into leverage. 📖 Ready to navigate the legacy minefield with confidence? 👉 Read the full article https://lnkd.in/e9d2gcSe https://lnkd.in/eKGhZDQ6 #LegacyModernization #DevoxSoftware #CTOPlaybook #AIinEngineering #EnterpriseSoftware #TechStrategy #SoftwareArchitecture #FeatureFlags #DevOps #Observability #StranglerPattern