AgenticAI System Requirements Document

Introduction

AgenticAI is a project focused on developing a "Multi-Agent System Simulator and Monitor". The system aims to visually demonstrate how distinct LLM agents collaborate on technical tasks with highly explicit event streams. This document outlines the system requirements for AgenticAI, including functional and non-functional requirements, user personas, core user flows, and design concepts.

System Overview

AgenticAI will consist of a visual React interface for task visualization and event logging, a modular backend with REST endpoints and state machine logic, and a database to track tasks, logs, and metrics. The system will ensure robust state transition testing to prevent deadlocks or invalid states.

Source Content Inventory

• Frontend: Visual React interface with a Kanban-style pipeline and event log stream component.
• Backend: Modular backend with state machine logic and REST endpoints.
• Database: Tracks 'tasks', 'agent_logs', and 'efficiency_metrics'.
• Quality Assurance: Unit tests for state transitions.

Functional Requirements as Story Points

• As a User, I should be able to view live task statuses moving across a Kanban-style pipeline.
• As a User, I should be able to see an event log stream that prints simulated log traces of AI agent communications.
• As a Developer, I should be able to initiate a new task simulation via REST endpoints.
• As a Developer, I should be able to pull active status of tasks via REST endpoints.
• As a System, I should dynamically calculate simulated "token usage", "cost", and "time saved".
• As a QA Engineer, I should be able to verify state transitions of tasks to ensure no deadlocks or invalid states.

User Personas

• User: Interested in monitoring and understanding the collaboration of AI agents on tasks.
• Developer: Responsible for initiating task simulations and interacting with the backend.
• QA Engineer: Ensures the system's robustness by testing state transitions.

Core User Flows

• User views dashboard -> sees task statuses in Kanban -> observes event log stream.
• Developer accesses REST API -> initiates task simulation -> retrieves task status.
• QA Engineer runs unit tests -> verifies state transitions -> confirms no deadlocks.

Visuals Colors and Theme

• primary: #1E3A8A (Deep Blue)
• primary_light: #3B82F6 (Light Blue)
• secondary: #F59E0B (Amber)
• accent: #EF4444 (Red)
• highlight: #F97316 (Orange)
• bg: #F3F4F6 (Light Gray)
• surface: rgba(255, 255, 255, 0.8) (White)
• text: #111827 (Dark Gray)
• text_muted: #6B7280 (Muted Gray)
• border: rgba(209, 213, 219, 0.5) (Light Gray)

Signature Design Concept

Interactive Kanban Pipeline

The homepage will feature an interactive Kanban pipeline where tasks move dynamically across different stages: "Backlog", "In Architecture Design", "Code Review", and "QA Testing". Each task card will animate as it transitions between stages, providing a visual representation of progress. Users can click on a task to expand details, including agent communications and efficiency metrics. The event log stream will scroll continuously, simulating real-time agent interactions.

Landing Hero Motion Brief

The landing page will feature a dynamic illustration of AI agents collaborating on a task. The animation will show inputs (tasks) being processed by agents, transforming through various stages, and resulting in a completed outcome. The animation will loop every 10 seconds, with interactive elements allowing users to hover over agents to see their roles and contributions.

Interaction Model & Motion Direction

• Intended Interaction Model: Animated
• The landing page will feature moderate scroll-triggered reveals and hover transitions, enhancing the interactive experience without overwhelming the user.

Non-Functional Requirements

• The system must handle concurrent task simulations without performance degradation.
• The event log stream should update in real-time with minimal latency.
• The system should be scalable to accommodate an increasing number of tasks and agents.

Tech Stack

• Frontend: React for Web
• Backend: Python with FastAPI
• Database: MySQL or MariaDB
• AI Models: Not specified
• AI Tools: Not specified
• Local Orchestration: Docker, docker-compose
• Server-side Orchestration: Kubernetes

Assumptions and Constraints

• The system will be deployed in an environment that supports Docker and Kubernetes.
• The database will be hosted on a secure server with access control measures in place.
• The frontend will be optimized for modern web browsers.

Glossary

• LLM: Large Language Model
• Kanban: A visual workflow management method
• REST API: Representational State Transfer Application Programming Interface
• State Machine: A computational model used to design algorithms and workflows

This document outlines the requirements and design concepts for AgenticAI, ensuring a comprehensive understanding of the system's functionality and design direction.