Apiiro introduces AI-powered Software Graph for threat modeling

Agentic RAG + MCP: A Practical Blueprint for Modular, Compliant Retrieval Building RAG systems that pull from many sources usually implies some level of agency. especially when choosing which source to query. Here’s a clean way to evolve that pattern using Model Context Protocol (MCP) while keeping the system modular and compliant. 1) Understand & refine the query Route the user’s prompt to an agent for intent analysis. The agent may reformulate the prompt (once or iteratively) into one or more targeted queries. It also decides whether external context is required to answer confidently. 2) Retrieve external context (when needed) If more data is needed, trigger retrieval across diverse domains, for example: Real-time user or session data Internal knowledge bases and documents Public/web sources and APIs Where MCP adds leverage: Domain-owned connectors: Each data domain exposes its own MCP server, defining how its data can be accessed and used. Built-in guardrails: Security, governance, and compliance are enforced at the connector boundary, per domain. Plug-and-play growth: Add new domains via standardized MCP endpoints—no agent rewrites, enabling independent evolution across procedural, episodic, and semantic memory layers. Open interfacing: Platforms can publish data in a consistent way for external consumers. Focus preserved: AI engineers concentrate on agent topology and reasoning, not bespoke integrations. 3) Distill & prioritize context Consolidate retrieved snippets and re-rank them with a stronger model than the embedder to keep only the most relevant evidence. 4) Compose the response If no extra context is required, or once context is ready, have the LLM synthesize the answer (or propose actions/plans) directly. 5) Verify before delivering Run a lightweight answer critique: does the output fully address the intent and constraints? If yes → deliver to the user. If no → refine the query and loop again. ♻️ Repost to help others become better system designers. 👤 Follow Kathirvel M and turn on notifications for deep dives in system architecture, scalability, and performance engineering. 💬 Comment with your MCP/Agentic RAG lessons or questions. 🔖 Save this post for your next architecture review. #AgenticRAG #MCP #ModelContextProtocol #RAG #LLM #AIEngineering #MLOps #SystemDesign #SoftwareArchitecture #Scalability #PerformanceEngineering #EnterpriseAI

🚀 𝗗𝗲𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗠𝗖𝗣: 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 𝗮𝗻𝗱 𝗧𝗵𝗲 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗙𝗹𝗼𝘄 The Model Context Protocol (MCP) architecture enables AI agents to use external tools securely and reliably. It operates on a foundation of three core parts and a precise, multi-step communication loop. Ⅰ. 𝗧𝗵𝗲 𝗧𝗵𝗿𝗲𝗲 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 𝗼𝗳 𝗠𝗖𝗣 The architecture separates responsibilities for scalability and security: ✨ 𝗔. 𝗧𝗵𝗲 𝗛𝗼𝘀𝘁 (𝗔𝗜 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻) The chatbot or agent that decides which tool is needed but does not handle the low-level communication. 🧠 🛠️ 𝗕. 𝗧𝗵𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 (𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗧𝗼𝗼𝗹) The service (e.g., GitHub, Slack, Drive) that executes the actual task. 🤝 𝗖. 𝗧𝗵𝗲 𝗠𝗖𝗣 𝗖𝗹𝗶𝗲𝗻𝘁 (𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗼𝗿) The dedicated middle layer. Crucially, each Server requires its own Client (a one-to-one relationship) to keep communication channels decoupled. 🔗 Ⅱ. 𝗧𝗵𝗲 𝗦𝘁𝗲𝗽-𝗯𝘆-𝗦𝘁𝗲𝗽 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗙𝗹𝗼𝘄 Here is how a request (e.g., "Check for new commits") travels through the system: * User to LLM: The User asks the Host. The Host sends the prompt to the LLM. 🗣️ * LLM Decision: The LLM realizes the answer requires an external Server (GitHub). 🧭 * Host to Client: The Host sends a high-level request to the designated MCP Client. ➡️ * Client Translation: The Client converts the request into the JSON-RPC language for the Server. ✍️ * Execution: The Client sends the structured request to the Server (GitHub), which performs the task. ✅ * Server Response: The Server returns a structured MCP response to the Client. ↩️ * Client Interpretation: The Client translates the Server's structured data back into a format the Host and LLM can understand. 📖 * Final Answer: The LLM uses the data to generate the final answer for the User. ✨ This architecture ensures the LLM focuses only on reasoning, while the Client manages all the complex tool-specific communication. What external tool in your workflow would benefit most from this standardized approach? 👇 #AIArchitecture #MCP #AIAgents #LLMs #TechDeepDive

𝗔𝗣𝗜 𝗪𝗼𝗿𝗹𝗱 𝟮𝟬𝟮𝟱: 𝗔𝗣𝗜𝘀 𝗮𝗿𝗲𝗻’𝘁 𝗲𝗻𝗱𝗽𝗼𝗶𝗻𝘁𝘀, 𝘁𝗵𝗲𝘆’𝗿𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗽𝗼𝗶𝗻𝘁𝘀 APIs used to be plumbing. Now they’re how AI thinks, acts, and ships. I spent two packed days at API World + CloudX + DataWeek in Santa Clara, and the through-line was clear: AI is rewriting the API stack, design, governance, testing, security, performance, even how interfaces are generated. 𝗧𝗵𝗿𝗲𝗲 𝘀𝗵𝗶𝗳𝘁𝘀 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗮𝗰𝘁 𝗼𝗻 1) 𝙎𝙥𝙚𝙘𝙨 → 𝙤𝙧𝙘𝙝𝙚𝙨𝙩𝙧𝙖𝙩𝙞𝙤𝙣. OpenAPI + Arazzo, overlays, example mapping. Specs aren’t paperwork, they’re instructions for agents. Teams generate docs/tests/mocks from the contract and catch breakage early. 2) 𝘼𝙜𝙚𝙣𝙩𝙨 𝙞𝙣 𝙩𝙝𝙚 𝙧𝙚𝙦𝙪𝙚𝙨𝙩 𝙥𝙖𝙩𝙝. MCP + orchestration are real. If autonomous calls hit prod, guardrails, zero-trust at the API layer, and policy-as-code must live on every request, not buried in a wiki. 3) 𝙋𝙧𝙤𝙤𝙛 𝙤𝙫𝙚𝙧 𝙥𝙧𝙤𝙢𝙞𝙨𝙚. RAG/agent evaluation, lineage/integrity, and network observability decide what scales, especially across multi-cloud. 𝗙𝗼𝗿 𝗹𝗲𝗮��𝗲𝗿𝘀 (𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 & 𝗿𝗶𝘀𝗸) 𝙎𝙥𝙚𝙘 𝙖𝙨 𝙨𝙩𝙧𝙖𝙩𝙚𝙜𝙮 → clearer ownership, fewer stalls, faster delivery. 𝙍𝙞𝙨𝙠 𝙞𝙣 𝙩𝙝𝙚 𝙧𝙚𝙦𝙪𝙚𝙨𝙩 → enforce policy at runtime with immutable logs. 𝙁𝙪𝙣𝙙 𝙚𝙫𝙞𝙙𝙚𝙣𝙘𝙚 → budget evaluation + observability alongside features. 𝗙𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗲𝗿𝘀 (𝗶𝗻𝗶𝘁𝗶𝗮𝗹 𝘀𝘁𝗲𝗽𝘀) 𝙃𝙖𝙧𝙙𝙚𝙣 𝙤𝙣𝙚 𝙘𝙪𝙨𝙩𝙤𝙢𝙚𝙧 𝘼𝙋𝙄 𝙛𝙤𝙧 𝙖𝙜𝙚𝙣𝙩𝙨 (examples, constraints, error semantics, Arazzo flows). 𝙄𝙣𝙡𝙞𝙣𝙚 𝙜𝙤𝙫𝙚𝙧𝙣𝙖𝙣𝙘𝙚 (authZ each call, policy-as-code, replay/defend; red-team agent flows). 𝙈𝙚𝙖𝙨𝙪𝙧𝙚 𝙩𝙝𝙚 𝘼𝙄 𝙙𝙚𝙡𝙩𝙖 (DX: PR cycle time, time-to-first-call; Reliability: change-failure, MTTR; Eval: agent/RAG KPIs). 𝗟𝗶𝘁𝗺𝘂𝘀 𝘁𝗲𝘀𝘁𝘀 ✔️ If your spec can’t instruct an agent, it’s not AI-ready. ✔️ If governance isn’t in the request path, it won’t be followed. ✔️ If you can’t observe it, you can’t scale it. This isn’t a tooling hype, it’s a shift in thinking. Contracts become executable instructions. Security shifts into the runtime. Engineers ship evaluation and observability as first-class features. Which “agent-ready” upgrade will you ship this quarter; contracts, governance, or telemetry? #APIWorld2025 #APIs #AI #OpenAPI #APISecurity #DeveloperExperience

The MCP Prompts feature is a standardized mechanism within Anthropic’s Model Context Protocol (MCP) for creating and reusing structured prompt templates. Core Function: Defined in JSON format, MCP Prompts act as "recipes" that tell an LLM (like Claude) exactly how to process dynamic input data and what structured output to return. This standardizes how LLMs interact with external tools and servers (e.g., for data extraction, classification, or code review). Mechanism: Creation: A prompt template is defined with a unique ID, version, instructions, and placeholders (e.g., {input_text}). Invocation: An MCP server or client calls the prompt ID, supplying the necessary dynamic inputs. Execution: The LLM runs the standardized prompt (often integrated with MCP Sampling) and generates a consistent, structured output (e.g., JSON). Key Benefits: The feature ensures consistency and efficiency by eliminating repetitive manual prompt engineering. It offers traceability (via version IDs) and enables seamless interoperability across different MCP-compatible systems, automating complex, LLM-driven workflows. Use Case: Automatic Ticket Classification A customer service platform (MCP Server) receives a new support ticket. Instead of routing it manually, the server invokes a stored MCP Prompt named prompt/ticket-classifier, supplying the ticket text {ticket_text} as input. The LLM processes the standard prompt instructions (e.g., "Classify this text as 'Bug', 'Feature Request', or 'Question'") and returns a structured JSON output (e.g., {"type": "Bug", "urgency": "High"}). This classification is instant and consistent, allowing the server to automatically route the ticket to the correct queue. Reference: For technical specifications and documentation on the Model Context Protocol (MCP), consult Anthropic's official GitHub repository or developer documentation pages related to Claude. (Specific URL requires knowledge of Anthropic's internal documentation structure; search for "Anthropic Model Context Protocol" or "Claude MCP".)

Design notes: SDKs and APIs for explainable graph + multi-agent systems We treat integrations as part of the explainability surface. Interfaces are designed so that answers always travel with their evidence. Principles • Evidence as a first-class payload: citations, lineage, and the exact subgraph used • Deterministic replay: same inputs/versions → same subgraph → same answer • Model-agnostic contracts: retrieval/generation constrained to validated subgraphs • Governance by default: policy checks, audit artefacts, versioned endpoints Interface shape • REST/GraphQL with typed schemas (versioned /v1) • Core objects: answer, evidence, subgraph_snapshot, audit, metrics • Delivery patterns: request/response plus event streams for alerts and audits Assurance and control • Identity and policy at the edge (service accounts, scoped tokens) • Optional mTLS/IP allowlists; field-level redaction and PII tags • Headers for cost/latency; structured uncertainty alongside confidence This is how we think about delivery: interfaces that carry proof, not just text. #knowledgegraphs #multiagent #API #SDK #explainableAI #governance

Here is a short cheat sheet for the watsonx Orchestrate CLI, designed for both local development and remote configuration. This resource covers essential components, including: - Environments - Agents - Connections - Import LLM (watsonx.ai) via AI Gateway The CLI is a part of the "watsonx Orchestrate Agent Development Kit (ADK)" 😉 Explore the full cheat sheet: https://lnkd.in/eFh5WEcG #watsonxorchestratecli #connection #environment #aigateway #agent #watsonxorchestrate

Large #Enterprise #Schemas = Large Confusion Even #GraphRAG needs boundaries to keep #LLMs focused. Here’s how to fix it. Use #Memgraph’s Fine-Grained #AccessControls. They make GraphRAG more accurate and explainable at enterprise scale. Josip Mrđen explains how this is so 👉 https://lnkd.in/gsiGAQr6 #ContextEngineering

What feels scrappy today will feel fragile tomorrow. A customer once described their internal homegrown document processing system to us as a “rat’s nest.” It hit a chord with us, because we realized it perfectly visualizes the problem our managed ETL+ platform is built to solve. What starts as a DIY scrappy effort to cut costs and move fast by building in-house, over time becomes unwieldy: - Stopgap scripts and one-off connectors multiply across teams - Schemas drift, breaking pipelines in subtle ways - Access controls loosen, audits fall behind - What once felt scrappy now feels fragile And then the security debt surfaces: shadow automations, misconfigured buckets, missing lineage. The quick hacks that helped you move fast quietly become more surface area posing security risks. And scaling will always create complexity. Even once you've overcome the basic challenges with horizontal scaling to meet peak load demands, there's still the question of integrity SLAs — are all your files actually making it through? What happens when the pipelines go down? What about smart syncing so you're not piling up unnecessary cloud infra costs? The art is untangling such a system without slowing growth—standardizing, securing, and orchestrating data pipelines so they can scale cleanly. Don’t let fragile pipelines hold you back. With Unstructured, you can process unstructured data confidently, ensuring security, integrity, and smooth scaling across your organization. #StructuredData #DataQuality #RAG #AI #GenAI #ETL #UnstructuredData #LLM #MCP #TableTransformation #DocumentAI #VLM #EnterpriseAI #RAGinProduction #Transformation #Quality #LLMready #SourceConnectors #Parsing #Unstructured #TheGenAIDataCompany

Completing another weekend/holiday project . 🚀 Project : Advance Document ChatBot Empowering internal teams with intelligent, secure, and context-aware document conversations. 🧠 Tech Stack Overview Ollama → Running local LLMs efficiently. LLM Models → Qwen (can be any available LLM) LangChain & LangGraph → Building dynamic retrieval and conversation logic using chains and graphs. Vector Stores → 🧩 Chroma – Lightweight and fast for small datasets. ⚡ Qdrant – Scalable for large document collections. Guardrails → Enforcing safe, validated, and policy-compliant outputs. structlog → Structured and contextual logging for full traceability. SQLite → Managing users, OTPs, and chat histories. FastAPI → Backend ,routes and API management. 💡 Key Features 🔐 1. Secure OTP-Based Login Email verification for access control. Domain validation (e.g., @ramesh.com) for internal use. 🏢 2. Department-Specific Access Separate document sets and databases for each department (HR, IT, Finance, etc.). 📁 3. Document Management Supports multiple formats: PDF, DOCX, XLSX, TXT. Automatic ingestion into Chroma or Qdrant based on department or global scope. 🔍 4. Intelligent Querying Choose between: Department – Scoped to internal department specific documents. Common – General chat or knowledge queries. 🧩 5. Guardrails Integration & Quality Control Filters and validates every LLM output. Protects against: Hallucinations & irrelevant responses Sensitive data exposure Jailbreak or topic bypass attempts Profanity & toxicity 🧱 6. Robust Logging & Monitoring Every system and user event is logged with structlog. Detailed tracking for retriever, guardrail, and LLM outputs. 🧰 Architecture Overview User Login (OTP) → SQLite DB → Access Granted ⬇️ Document Upload → Vectorstore (Chroma/Qdrant) ⬇️ Query Processing → LangChain + LangGraph → LLM (Ollama/Qwen) ⬇️ Guardrails → Validate & Sanitize Output ⬇️ Response Display → Web UI with Real-Time Logs 🌐 Goal: To provide a secure, scalable, and context-aware internal chatbot that enhances knowledge access across departments — making it easy to retrieve information from documents instantly, instead of manually searching through hundreds of pages. #AI #LangChain #LangGraph #Ollama #Qwen #RAG #FastAPI #Guardrails #Qdrant #Chroma #LLM #ChatbotDevelopment #EnterpriseAI #WeekendProject