Architecture

ARCOS is designed as a modular, microservices-based ecosystem that scales to handle large, complex projects while staying flexible enough to integrate with diverse development environments (cloud, on-prem, hybrid). The key building blocks—ARCOS Core, Agent Microservices, and a Unified Data Layer—collectively empower each specialized AI Agent to perform its function with minimal overhead.

  ┌─────────────────────┐
              │     ARCOS Core      │
              │ (Orchestration Hub) │
              └─────────────────────┘
                     ↑       ↑
                     │       │
   ┌─────────────────┬───────┴─────────────────────┐
   │                 │                             │
   │                 │                             │
   ↓                 ↓                             ↓
┌───────────────┐ ┌───────────────┐        ┌───────────────┐
│   CodeSynth    │ │   DocSavvy    │  ...   │    DataWiz     │
└───────────────┘ └───────────────┘        └───────────────┘

Additional Agents: EmailMaestro, TestGenius, PerfGuard, ConfigWizard

ARCOS Core ARCOS Core is the orchestration and coordination engine of ARCOS. It is responsible for: 1. Task Scheduling & Load Balancing

• Receives requests (e.g., “run performance tests,” “translate codebase,” “generate docs”) from developers or automation scripts, and delegates them to the appropriate AI Agent.

• Dynamically scales available resources to handle spikes in usage (e.g., running multiple PerfGuard instances for large-scale load testing). 2. Authentication & Authorization

• Manages user accounts, API keys, and role-based access controls.

• Provides granular permissions (e.g., certain developers can only trigger test generation, while leads can trigger production deployments). 3. Agent Coordination

• Facilitates communication across Agents: For instance, DocSavvy updates documentation after CodeSynth refactors code, or TestGenius automatically re-tests modules that changed.

• Ensures Agents can share insights or logs seamlessly, creating a fully autonomous workflow when desired.

Agent Microservices: Each AI Agent—such as CodeSynth, DocSavvy, DataWiz, EmailMaestro, TestGenius, PerfGuard, or ConfigWizard—is an independent microservice, meaning:

• Independent Deployability: You can spin up, update, or scale any Agent without affecting the rest of the platform.

• Custom Configuration: Each Agent can be configured with specialized settings (e.g., syntax style guides for CodeSynth, acceptance criteria for TestGenius).

• Resource Optimization: If your team heavily uses DataWiz for analytics, you can allocate more computing power to DataWiz while keeping other Agents at minimal capacity. Unified Data Layer

The Unified Data Layer provides:

1. Consistent Data Storage & Versioning

• Maintains snapshots and metadata for code changes, enabling easy rollback or diffs.

• Tracks environment-specific settings (dev, staging, production) to ensure Agents perform contextually correct tasks.

1. Audit Logging & Compliance

• Records each Agent’s actions (e.g., bug fixes, test runs, environment config changes) for traceability.

• Critical for enterprises with stringent compliance requirements (e.g., GDPR, SOC 2, ISO 27001).

1. Advanced Data Indexing

• Facilitates quick lookups of repetitive bug patterns or commonly used code snippets.

• Employs vector stores (FAISS, Annoy, Pinecone, Milvus) to handle large-scale code similarity searches and machine-learning-driven suggestions. Technology Stack

ARCOS leverages a modern, cloud-native tech stack to ensure robustness, scalability, and flexibility:

1. Backend Languages

• Python, Rust, and Go power the core services and Agents.

• Mix-and-match approach allows each Agent to choose the language best suited to its domain (e.g., Rust for performance-critical tasks, Python for ML-driven features).

1. Frontend & User Interface

• React.js or Next.js for a responsive, feature-rich web dashboard.

• Optionally provides CLI and SDKs for headless or script-based usage.

1. AI & ML Frameworks

• TensorFlow and PyTorch for deep learning-based code analysis.

• OpenAI API or custom LLMs to generate or refine code, documentation, and test suites.

1. Data & Vector Stores

• PostgreSQL for relational data (metadata, configurations).

• FAISS, Annoy, Pinecone, Milvus for high-dimensional vector similarity searches (useful in code pattern matching).

1. Containers & Orchestration

• Docker to package each Agent with its dependencies.

• Kubernetes for orchestration, scaling, and resilience of these containerized Agents.

1. CI/CD Pipelines

• GitHub Actions, Jenkins, or GitLab CI for automating builds, tests, and deployments.

• ARCOS Agents can integrate directly into pipeline stages to run code analysis or environment scans on each commit.

1. Monitoring & Metrics

• Prometheus and Grafana to track system health, resource usage, and performance metrics.

• Real-time dashboards help DevOps teams identify issues quickly and maintain service-level objectives (SLOs). |