# System Architecture This document outlines the proposed system architecture for the next generation of the ADAM platform. It is designed to support a highly scalable, dynamic, and collaborative multi-agent system. ## Core Concepts The new architecture is built around three core concepts: 1. **Parallel Agent Swarming:** Instead of a single agent working on a task, we will deploy swarms of specialized agents that can work in parallel to solve complex problems more efficiently. 2. **Dynamic Workflows:** Workflows will no longer be static and predefined. Instead, the system will dynamically generate and adapt workflows based on the task and the available agents. 3. **Repositories as Nodes:** We will treat entire code repositories as nodes in a graph, allowing agents to reason about and interact with code at a much higher level of abstraction. ## Parallel Agent Swarming Parallel Agent Swarming is a paradigm where multiple agents collaborate to solve a problem that is too complex for any single agent. This approach enhances speed, robustness, and scalability. ### Agent Roles Within a swarm, agents can assume specialized roles: * **Coordinator Agent:** Responsible for decomposing the main task into smaller sub-tasks, assigning them to specialist agents, and monitoring the overall progress. * **Specialist Agents:** These are expert agents trained for specific tasks (e.g., code generation, testing, documentation, security analysis). A swarm can have multiple instances of the same specialist agent type working on different sub-tasks in parallel. * **Reporter Agent:** Gathers the outputs from all specialist agents, synthesizes them into a coherent whole, and presents the final result. * **Quality Assurance Agent:** An agent dedicated to reviewing the work of other agents, checking for errors, inconsistencies, and adherence to standards. ### Communication Protocol Agents within a swarm communicate via a shared, structured message bus. Messages will conform to a defined schema, including: * `task_id`: A unique identifier for the sub-task. * `sender_id`: The ID of the agent sending the message. * `receiver_id`: The ID of the intended recipient (can be a specific agent or a broadcast to a role). * `payload`: The content of the message, which could be code, data, or instructions. * `status`: The current status of the task (e.g., `PENDING`, `IN_PROGRESS`, `COMPLETED`, `FAILED`). ### Coordination Strategy The coordination of the swarm is managed by the Coordinator Agent, which uses a dynamic planning approach. The initial plan is a high-level graph of dependencies between sub-tasks. As agents complete their tasks, the Coordinator updates the plan, re-assigning tasks as needed and potentially spawning new agents to handle unforeseen problems. This allows the swarm to adapt to the evolving state of the problem. ## Dynamic Workflows Dynamic Workflows are constructed and modified at runtime, providing the flexibility to adapt to the specific needs of a task and the results of intermediate steps. ### Workflow Engine The core of this capability is the Workflow Engine. This engine is responsible for: * **Graph-Based Representation:** Workflows are represented as a Directed Acyclic Graph (DAG), where nodes are tasks and edges represent dependencies. * **Dynamic Generation:** The initial workflow is generated by an Orchestrator Agent based on the user's high-level goal. The Orchestrator selects from a library of "task templates" to build the initial graph. * **Runtime Adaptation:** The workflow is not static. Based on the output of a task, the Orchestrator can modify the downstream parts of the graph. For example, if a testing agent finds a critical bug, the Orchestrator can dynamically insert a "debugging" task into the workflow. * **State Management:** The engine tracks the state of each task in the workflow (`PENDING`, `RUNNING`, `SUCCESS`, `FAILED`) and the outputs produced. ### Orchestrator Agent's Role The Orchestrator Agent is the primary user of the Workflow Engine. Its responsibilities include: * **Goal Interpretation:** Translating a high-level user request into a concrete, machine-executable workflow graph. * **Task-to-Agent Mapping:** Assigning the right agent swarm to each task in the workflow graph. * **Exception Handling:** When a task fails, the Orchestrator decides on the next course of action, which could be to retry the task, assign it to a different agent, or modify the workflow to try an alternative approach. ## Repositories as Nodes The "Repositories as Nodes" concept treats entire codebases as individual, high-level objects within a larger graph of software projects. This allows for powerful cross-repository analysis and operations. ### Graph Representation We will construct a graph where: * **Nodes:** Each node represents a code repository. The node's metadata will include its URL, a summary of its purpose (generated by an agent), a list of technologies used, and its current health score. * **Edges:** Edges represent dependencies between repositories. For example, if `Repo A` uses a library defined in `Repo B`, there will be a directed edge from `A` to `B`. The edge can be weighted by the importance of the dependency. ### Agent Interaction with Nodes Agents will interact with repository nodes in several ways: * **Repository-Level Reasoning:** Agents can query the graph to understand the dependency landscape. For example, an agent could ask, "Which of our services will be affected by a vulnerability in this shared library?" * **Codebase-as-a-Tool:** Instead of just reading and writing files, agents can use an entire repository as a tool. For example, an agent could pass a high-level task like "add a new feature to the user service" to the node representing the user service repository. The node itself, powered by its own swarm of agents, would be responsible for implementing the feature. * **Cross-Repository Operations:** An agent could perform operations across multiple repositories simultaneously. For example, an agent could be tasked with "upgrading all of our microservices to use the latest version of our internal logging library." ### Benefits This approach provides several key benefits: * **Scalability:** It allows us to manage and reason about a large ecosystem of interconnected projects. * **Abstraction:** It provides a higher level of abstraction for agents, allowing them to focus on high-level goals instead of low-level file manipulations. * **Automated Maintenance:** It enables the automation of complex, cross-repository maintenance tasks that are currently performed manually.