avfranco/ea4all_agentic_companion

Title

Empower people with ability to harness the value of Enterprise Architecture with Generative AI to positively impact individuals and organisations.\n

Problem to solve

Enterprise Architecture teams struggle with limited resources, fragmented processes, and poor collaboration between business and technology. They remain reactive and slow to adapt, hindered by manual work, knowledge silos, and a steep learning curve.

• —Lack of shared understanding between business and technology stakeholders.
• —Slow, manual, and fragmented architecture documentation processes.
• —Inability to quickly adapt architecture to strategic or market changes.
• —Limited collaboration and accessibility of architectural knowledge.
• —Steep learning curve and delayed exposure to proven design patterns.
• —Architecture teams constrained by Enterprise Technology budget (small teams)
• —Project oriented and reactive architecture teams

Architect Agentic Companion

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Background

• —Trigger: How disruptive may Generative AI be for Enterprise Architecture Capability (People, Process and Tools)?
• —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.
• —Ability: Exploit my carrer background and skillset across system development, business accumen, innovation and architecture to accelerate GenAI exploration while learning new things.

That's how the EA4ALL-Agentic system was born and ever since continuously evolving to build an ecosystem of Architects Agent partners.

Benefits

• —Empower individuals with Knowledge: understand and talk about Business and Technology strategy, IT landscape, Architectue Artefacts in a single click of button.
• —Accelerate learning: gain quick access to new architectures design, patterns, and best-practices.
• —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.
• —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.
• —Increase collaboration: democratise architecture work and knowledge with anyone using natural language.
• —Cost optimisation: intelligent allocation of architects time for valuable business tasks.
• —Business Growth: create / re-use of (new) products and services, and people experience enhancements.
• —Resilience: assess solution are secured by design, poses any risk and how to mitigate, apply best-practices.
• —Streamline: the process of managing and utilising architectural knowledge and tools in a user-friendly way.

Knowledge context

Synthetic datasets are 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

• —Bring Your Own Data: upload your own IT landscape data
• —Application Portfolio Management
• —xlsx tabular format
• —first row (header) with fields name (colums)

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

Architecture Demand Management

• —Provide project resource estimation for architecture work based on business requirements, skillset, architects allocation, and any other relevant information to enable successful project solution delivery.

AWS & Microsoft Official Documentation Question and Answering

• —Access to official documentation and diagram generation for AWS services. MCP service-based.

Log / Traceability

For purpose of continuous improvement, agentic workflows are logged in.

Architecture

<italic>Core architecture built upon Python, Langchain, Langgraph, Langsmith, and Gradio.<italic>

• —Python
• —Pandas
• —Langchain
• —Langgraph
• —Huggingface
• —CrewAI

• —RAG (Retrieval Augmented Generation)
• —Vectorstore

• —Prompt Engineering
• —Strategy & tactics: Task / Sub-tasks
• —Agentic Workflow

• —Models:
• —OpenAI
• —Meta/Llama
• —Google Gemini

• —Hierarchical-Agent-Teams:
• —Tabular-question-answering over your own document
• —Supervisor
• —Visual Questions Answering
• —Diagram Component Analysis
• —Risk & Vulnerability and Mitigation options
• —Well-Architecture Design Assessment
• —Vision and Target Architecture
• —Architect Demand Management
• —AWS & Microsoft Official Documentation

• —User Interface
• —Gradio

• —Observability & Evaluation
• —Langsmith

• —Hosting
• —Huggingface Space

Check out the configuration reference at spaces-config-reference