Introducing The Corporate Recon Swarm AI Agent Orchestration

🚀 Built a Production-Style AI Agent System Over the past few days, I focused on going beyond basic LLM apps and built a real AI Agent system using modern GenAI architecture. Instead of just generating responses, this system actually thinks, decides, and acts. 🔍 What I built: - FastAPI backend with structured APIs - LangGraph-based agent workflow - LLM-powered decision making (tool selection) - Custom tools (Calculator, Word Counter) - Session-aware flow (basic memory) - Clean JSON responses with latency tracking ⚙️ Architecture Flow: User → FastAPI → Agent (LangGraph) → LLM Decision → Tool Execution → Response 💡 The key learning for me: Moving from “LLM answers everything” ➝ “LLM decides what to do” This shift is what differentiates: 👉 Basic chatbot vs 👉 Real AI Agent systems 🧠 Tech Stack: Python | FastAPI | LangChain | LangGraph | OpenAI | Pydantic | Uvicorn 📌 Why this matters: Most real-world GenAI systems today are not just prompt-based — they are: - Tool-using agents - Workflow-driven systems - Modular & extensible architectures This project helped me understand how production-grade AI systems are actually designed. Next step: combining this with RAG pipeline + multi-agent workflows #GenAI #AIAgents #LangGraph #LangChain #FastAPI #OpenAI #MachineLearning #ArtificialIntelligence #Python #BackendDevelopment #SoftwareEngineering #AIProjects

𝗦𝗧𝗢𝗣 building AI demos. 𝗦𝗧𝗔𝗥𝗧 building AI Systems. The world doesn't need another "𝘊𝘩𝘢𝘵 𝘸𝘪𝘵𝘩 𝘺𝘰𝘶𝘳 𝘗𝘋𝘍" wrapper. Most AI tools optimise for a "𝘸𝘰𝘸" demo; few optimise for architectural integrity, traceability, and long-term maintainability. Over the past few months, I have been designing and building Living Docs, an AI document intelligence system designed from the ground up around explainable Retrieval-Augmented Generation (RAG), precise character-level citations, and a clean, maintainable backend architecture. It doesn't just respond to natural language questions about your documents; it shows its work at every step, tracing every generated answer back to the exact source chunk, page, and character offset in the original file. For teams that operate in high-stakes environments where accuracy and accountability are non-negotiable, this level of transparency is not a nice-to-have feature 𝗶𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗲𝗻𝘁𝗶𝗿𝗲 𝗽𝗼𝗶𝗻𝘁. 𝗪𝗵𝗮𝘁’𝘀 𝘂𝗻𝗱𝗲𝗿 𝘁𝗵𝗲 𝗵𝗼𝗼𝗱?  1. Clean Architecture & Domain-Driven Design  2. High-Fidelity Ingestion via Unstructured  3. Precise Character-Level Citations  4. Multi-tenant Vector Orchestration  5. Stateful Multi-turn Conversations  6. JWT-based Auth Beyond the LLM, the focus is on a robust, multi-tenant backend built to handle real-world document lifecycles The Tech Stack: 𝗣𝘆𝘁𝗵𝗼𝗻 𝟯.𝟭𝟭 | 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 | 𝗔𝗹𝗲𝗺𝗯𝗶𝗰 | 𝗣𝗶𝗻𝗲𝗰𝗼𝗻𝗲 | 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 | 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲 | 𝗣𝘆𝘁𝗲𝘀𝘁 Do Explore: https://lnkd.in/d8G5atPw I’m looking to connect with anyone working on RAG observability, LLMOps, or High-Performance Backend Systems. Let's talk about building AI that teams can actually depend on. #BackendEngineering #Python #FastAPI #RAG #AIInfrastructure #CleanArchitecture #DomainDrivenDesign #LLMOps #GenerativeAI #DocumentIntelligence

I’ve just published a new portfolio project: AI Workflow Observatory. It is a local-first observability dashboard for AI-assisted engineering workflows. The tool scans local Codex session logs and reconstructs the engineering process behind AI work: - context gathering - planning - implementation - verification - debugging / recovery - handoff quality - estimated cost in USD/EUR/PLN - workflow risk and verification quality The idea is simple: AI coding tools should not only produce code. Engineering teams also need visibility into how the work happened, whether it was verified, where the risk is, and how much iteration/cost was involved. This connects directly to the systems I’m interested in building: practical AI engineering, agent workflows, observability, evaluation, auditability, and operator-facing control layers. GitHub: https://lnkd.in/d8qPz5HB #AIEngineering #GenAI #LLMOps #AgenticAI #Python #FastAPI #Observability #RAG #AIAgents #PortfolioProject

Chiseling AI‑generated code is quickly becoming an essential skill for engineering teams: AI gives us incredible velocity, but it also floods our codebases with “rough drafts” that aren’t ready for prime time. We should treat AI output like a junior developer’s first pass—useful raw material that must be chiseled into shape through deliberate refactoring, clearer abstractions, stronger error handling, and meaningful tests. By making chiseling a first‑class step in our workflow—not an optional tidy‑up—we preserve velocity while protecting code quality, architecture, and long‑term maintainability. #AI #SoftwareEngineering #CodeQuality #CleanCode #LLM #DeveloperExperience #TechLeadership #Refactoring #AICoding #SoftwareArchitecture

Most enterprise AI projects fail because of 'messy' data. 📉 I recently built a Multimodal AI Proof of Concept to solve a specific problem: How do you classify sensitive financial docs (like 16-bit TIFFs and legacy Word files) without compromising security? Using a stack of Python, LangChain, Generative AI and other modern tech, I engineered a solution that: ✅ Normalizes 16-bit scans using NumPy (no more black images). ✅ Uses Pydantic to force AI into strict JSON schemas. ✅ Includes an 80% Confidence Threshold for human-in-the-loop safety. The result? A 75% reduction in manual labor for data migration. Check out the full breakdown in my Featured section! #SalesEngineering #GenerativeAI #Python #PMP #SolutionsArchitect" Shoutout to the LangChain team for the orchestration tools and Streamlit for making PoC deployment so seamless for my latest project.

🚀 Built a Production-Ready Multi-Agent RAG System using LangGraph I recently developed an enterprise-grade AI system that leverages a Hybrid Multi-Agent Architecture to deliver accurate, domain-specific answers from unstructured documents. 🔹 Key Features: Orchestrator-based routing with intelligent domain selection Domain-specific agents (HR, Finance, Compliance) Subquery generation for precise retrieval Vector search using FAISS Agent-to-Agent (A2A) fallback for improved recall Synthesiser agent to merge and validate responses Built using FastAPI + LangGraph + OpenAI 🔹 Architecture Highlights: Parallel agent execution for performance Context-aware answering using embeddings Clean separation of orchestration, retrieval, and synthesis Production-ready design with scalability in mind 🔹 Use Cases: Enterprise knowledge assistants Policy & document query systems Internal AI copilots This project demonstrates how Hybrid Multi-Agent Systems can significantly improve accuracy and reliability in RAG pipelines. #AI #GenerativeAI #LangGraph #RAG #MachineLearning #Python #LLM #SoftwareEngineering #Singapore

I've been running coding agent POCs with enterprise teams for a while now. One thing keeps coming up. The agent works great on small projects. Then you point it at a real codebase and the output quality falls off a cliff. It's not the model. It's what the model can see. The science behind context degradation, how different agents search today, the workflow that actually works for brownfield codebases, and a set of practical recipes you can use straight away with Sourcegraph. #contextengineering #codingagents #mcp #sourcegraph #agenticcoding #softwareengineering #ai #softwarefactory

Great piece (refer below) — your framework for context efficiency maps exactly to a problem I've been solving hands-on for a large .NET case management system (25 repos, 16 databases, FACTS framework). Where our approaches diverge: Sourcegraph MCP gives agents better search over raw code. I built a system that gives agents pre-distilled architectural knowledge — and I think the token economics actually favor distillation. Here's what I mean: an Ollama-powered seeder walks every .cs/.cshtml/.dsm file across all 25 repos, runs role-aware prompts (Controller → extract ENDPOINTS, AUTH, INJECTED_DEPS; Startup → extract MIDDLEWARE, DI_REGISTRATIONS, etc.), and stores structured summaries with embeddings. On top of that, a synthesis layer builds 7 cross-cutting architectural narratives (auth flows, DI wiring, data access patterns, microservice communication, etc.) — knowledge that doesn't exist in any single file. The result: when an agent asks "how does authentication work across services?", retrieval returns ~2,000 tokens of already-understood, labeled, architecture-level context — not raw code that the model still needs to comprehend. By your own "smart zone" framing, pre-distillation keeps you there longer because every retrieved token is pure signal. The whole thing runs as a custom MCP server (4 tools: ask_codebase, analyze_story, analyze_bug, get_status) alongside a separate DB MCP with live SQL access to all 16 databases, plus domain-specialized agents for the framework and batch subsystems. Entirely local, zero cloud cost. Your article nails the thesis — context is everything. I'd argue there's a spectrum: Sourcegraph optimizes retrieval precision, this approach optimizes retrieval density. The ideal is probably both. Would love to compare notes. Ajay Sridhar couldn't respond/shortening this - as comment wouldn't allow it. cc: Suresh Kumar Arunachalam, Vivek Chaudhary, Madhan Rangaswamy

Hot take: strong AI products are usually built on boring engineering discipline. One topic worth paying attention to today: Architecting the AI backbone of intelligent insurance: How to engineer a scalable and performant enterprise AI platform. What stands out to me is that real product quality still comes from architecture, reliability, and clear system ownership. The model may get the attention, but platform design is what usually decides whether a feature survives production traffic. That is why I keep thinking about AI through the lens of backend systems, observability, and execution discipline. https://lnkd.in/eVeCb-tk The gap between a demo and a dependable product is usually system design, not model hype. #SoftwareEngineering #AI #Python #Backend #TechLeadership