Architecture Overview

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System Architecture

Shannon is built as a distributed microservices system designed for production AI agent orchestration:

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Core Components

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Gateway (Port 8080)

Technology: Go Purpose: REST API layer for external clients The Gateway provides:

• HTTP/JSON API interface
• Authentication and authorization (API keys)
• Rate limiting per user
• Idempotency support
• SSE and WebSocket streaming
• OpenAPI specification

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Orchestrator (Port 50052)

Technology: Go + Temporal Purpose: Central workflow coordination The Orchestrator handles:

• Task routing and decomposition
• Cognitive pattern selection (CoT, ToT, ReAct)
• Budget and token usage enforcement
• Session management
• OPA policy evaluation
• Multi-agent coordination

Key Technology: Temporal - Provides durable, deterministic workflows that can be replayed for debugging.

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Agent Core (Port 50051)

Technology: Rust Purpose: Secure execution layer The Agent Core provides:

• WASI (WebAssembly System Interface) sandboxing
• Secure Python code execution (CPython 3.11 in WASI)
• Tool registry and execution
• Result caching (LRU with TTL)
• Circuit breakers and rate limiting

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LLM Service (Port 8000)

Technology: Python + FastAPI Purpose: Multi-provider LLM gateway The LLM Service handles:

• Multi-provider abstraction (OpenAI, Anthropic, Google, etc.)
• Intelligent caching with SHA256-based deduplication
• MCP (Model Context Protocol) tool integration
• Web search integration (Exa, Perplexity, etc.)
• Embeddings and document chunking

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Data Flow

Here’s how a task flows through Shannon:

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Persistence Layer

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PostgreSQL

Stores:

• Task metadata and execution history
• Session state and context
• User and API key data
• Workflow history

Schema: Relational storage for tasks, sessions, and agent metadata

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Redis

Provides:

• Session caching (TTL: 3600s)
• LLM response caching
• Rate limiter state
• Pub/sub for events

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Qdrant

Vector database for:

• Semantic memory retrieval
• Session-scoped vector collections
• MMR (Maximal Marginal Relevance) for diversity

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Observability

Shannon includes comprehensive observability:

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Metrics (Prometheus)

Each service exposes metrics:

• Orchestrator: :2112/metrics
• Agent Core: :2113/metrics
• LLM Service: :8000/metrics

Metrics include:

• Request rates and latency
• Token usage and costs
• Cache hit/miss rates
• Error rates by type
• Circuit breaker status

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Tracing (OpenTelemetry)

Distributed tracing across all services with context propagation via traceparent headers.

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Desktop Application

Native Tauri/Next.js desktop client providing:

• Active tasks and workflows (Runs view)
• Event streams (Run Details)
• Basic system and task-level insights

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Temporal UI (Port 8088)

Native Temporal interface for:

• Workflow visualization
• Execution history
• Replay debugging
• Worker status

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Design Principles

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1. Reliability

• Temporal workflows ensure durability - workflows survive service restarts
• Circuit breakers prevent cascading failures
• Graceful degradation when services are unavailable

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2. Security

• WASI sandboxing isolates untrusted code execution
• OPA policies enforce fine-grained access control
• Multi-tenancy with tenant isolation

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3. Cost Control

• Token budgets prevent runaway costs
• Intelligent routing to cheaper models when appropriate
• Learning router improves cost efficiency over time (85-95% savings)

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4. Observability

• Prometheus metrics for monitoring
• OpenTelemetry tracing for debugging
• Deterministic replay via Temporal

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Scalability

Shannon scales horizontally:

• Stateless services: Gateway, Orchestrator, Agent Core can scale independently
• Temporal workers: Add more workers to increase throughput
• Database: PostgreSQL with read replicas, Redis cluster, Qdrant distributed mode

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Next Steps