Architecture
Technical Architecture: Penverse Intelligent Simulation Framework
1. Overview
This document describes the technical architecture of the Penverse Intelligent Simulation Framework, which enables multi-agent scientific experimentation driven by prompt-based orchestration and coordinated by a Model Context Protocol (MCP) server. The UI is based on the existing simulation playground mockup, where each visual card represents an autonomous agent component.
2. Core Components
2.1 Model Context Protocol (MCP) Server
The MCP server is the heart of the orchestration layer. It:
Accepts user prompts and parses them into semantic intents.
Generates and executes a Directed Acyclic Graph (DAG) of simulation agents.
Maintains execution context and memory state.
Enables context-aware re-planning and feedback loops across agents.
Key Subsystems:
Prompt Parser and Semantic Interpreter: Translates prompts into agent task graphs.
DAG Planner: Builds a flow graph with dependencies and execution order.
Context Memory Manager: Maintains shared memory, key-value state, and time-based snapshots.
Execution Router: Coordinates agent activation, monitors runtime status, and reacts to agent feedback.
2.2 Simulation Agents
Each simulation unit on the UI corresponds to a modular agent capable of:
Accepting context-specific inputs.
Running localized computation or data processing.
Emitting output, logs, metrics, or state changes.
Reacting to contextual updates from the MCP.
Types of Agents (based on UI mock):
TimeControllerAgentHyperparamAgentTraceLoggerAgentNNGraphAgentAccuracyMonitorThermalHealthAgentEnvSensorAgentSupplySimAgentCulturalAgentSignalEvaluatorAgentPopulationAgent
Agents may be implemented as WASM modules to support distributed execution.
3. UI Integration (Simulation Playground)
The front-end playground mirrors the DAG execution by visually rendering each agent as a card:
Each card: Represents a live execution window for an agent (status, metrics, visuals).
Interactions: Users can pause/resume agents, modify parameters, view logs, and receive AI suggestions.
Live Feedback: Cards dynamically update based on agent output and DAG reconfiguration.
Contextual Flows: UI shows inter-card connections based on the current DAG and MCP state.
UI communicates with the MCP server via WebSocket and REST endpoints for:
Submitting prompts
Streaming agent state
Receiving DAG updates
Visualizing decision points and re-planning
4. Data Flow Architecture
User Prompt → UI (Playground)
↓
MCP Server
├── Prompt Interpreter
├── DAG Planner
├── Context Manager
└── Agent Coordinator
↓ ↓ ↓
[Agent A] [Agent B] [Agent C] ... (Parallel or dependent execution)
↓ ↓ ↓
Outputs Metrics Triggers
↓ ↑
UI Update ← Feedback Loop5. Execution Lifecycle
Prompt Initiation:
User enters prompt.
MCP parses it and constructs the execution DAG.
DAG Deployment:
Agents are launched (as WASM tasks or native services).
Shared memory and context are initialized.
Real-Time Execution:
Agents run with access to their context.
Outputs are streamed to UI and other agents.
Logs and metrics are captured live.
Feedback Handling:
Agents emit performance signals.
MCP uses rules, ML, or thresholds to re-plan DAG.
DAG can be halted, modified, or resumed.
Completion:
Outputs saved to decentralized storage.
Simulation run is versioned and exportable.
6. Technical Stack (Non-exhaustive)
UI Playground
React + WebSockets + Canvas/TS
MCP Server
Python (FastAPI/Flask) or Rust
Semantic Interpreter
LLM-backed (OpenAI/Claude)
Agents Runtime
WASM + Wasmtime / WasmEdge
Memory/State Sharing
Redis / Shared KV / DAG Store
Storage Layer
IPFS / Arweave / S3 (optional)
7. Extensibility
Agents are pluggable via a registry.
MCP interprets new agents via interface contracts (schema).
New research domains can be supported by simply introducing new agent modules.
Agents can be wrapped with AI assistant interfaces to enable voice/chat guidance.
8. Security and Isolation
Agent execution is sandboxed (WASM isolation).
No agent can directly access another’s memory without mediated context.
All outputs are signed and checkpointed for reproducibility and auditability.
9. Summary
This architecture provides the foundation for a modular, intelligent, and prompt-driven research simulation system. The MCP server ensures context-aware orchestration while the UI delivers an intuitive view into the agentic execution process. Together, they unlock a new class of research tools that are adaptable, visual, and powered by intelligence at every stage.
Last updated