--- title: Talk to your Multi-Agentic Architect System emoji: 👁 colorFrom: purple colorTo: green sdk: docker pinned: false license: mit --- # Title Empower people with ability to harness the value of Enterprise Architecture through Generative AI to positively impact individuals and organisations.\n ## Overview `Trigger`: How disruptive may Generative AI be for Enterprise Architecture Capability (People, Process and Tools)? \n `Motivation`: Master GenAI while disrupting Enterprise Architecture to empower individuals and organisations with ability to harness EA value and make people lives better, safer and more efficient. \n `Ability`: Exploit my carrer background and skillset across system development, business accumen, innovation and architecture to accelerate GenAI exploration. \n\n > That's how the `EA4ALL-Agentic system` was born and ever since continuously evolving. ## Benefits `Empower individuals with Knowledge`: understand and talk about Business and Technology strategy, IT landscape, Architectue Artefacts in a single click of button. \n `Increase efficiency and productivity`: generate a documented architecture with diagram, model and descriptions. Accelerate Business Requirement identification and translation to Target Reference Architecture. Automated steps and reduced times for task execution.\n `Improve agility`: plan, execute, review and iterate over EA inputs and outputs. Increase the ability to adapt, transform and execute at pace and scale in response to changes in strategy, threats and opportunities. \n `Increase collaboration`: democratise architecture work and knowledge with anyone using natural language.\n `Cost optimisation`: intelligent allocation of architects time for valuable business tasks. \n `Business Growth`: create / re-use of (new) products and services, and people experience enhancements. \n `Resilience`: assess solution are secured by design, poses any risk and how to mitigate, apply best-practices. \n ## Knowledge context Synthetic dataset is used to exemplify the Agentic System capabilities. ### IT Landscape Question and Answering - Application name - Business fit: appropriate, inadequate, perfect - Technical fit: adequate, insufficient, perfect - Business_criticality: operational, medium, high, critical - Roadmap: maintain, invest, divers - Architect responsible - Hosting: user device, on-premise, IaaS, SaaS - Business capability - Business domain - Description ### Architecture Diagram Visual Question and Answering - Architecture Visual Artefacts - jpeg, png **Disclaimer** - Your data & image are not accessible or shared with anyone else nor used for training purpose. - EA4ALL-VQA Agent should be used ONLY FOR Architecture Diagram images. - This feature should NOT BE USED to process inappropriate content. ### Reference Architecture Generation - Clock in/out Use-case ## Log / Traceability For purpose of continuous improvement, agentic workflows are logged in. ## Architecture Core architecture built upon python, langchain, meta-faiss, gradio and Openai. - Python - Pandas - Langchain - Langsmith - Langgraph - Huggingface - RAG (Retrieval Augmented Generation) - Vectorstore - Prompt Engineering - Strategy & tactics: Task / Sub-tasks - Agentic Workflow - Models: - OpenAI - Llama - Hierarchical-Agent-Teams: - Tabular-question-and-answering - Supervisor - Visual Questions Answering - Diagram Component Analysis - Risk & Vulnerability and Mitigation options - Well-Architected Design Assessment - Vision and Target Reference Architecture - User Interface - Gradio - Hosting: Huggingface Space ## Agentic System Architecture ![Agent System Container](images/ea4all_agent_container.png) Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference