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Technical Implementation

Technical Implementation – Part 1: Core Simulation Execution

This document provides the detailed implementation blueprint for the first set of features in the Penverse Intelligent Simulation Framework, specifically covering prompt parsing, DAG creation, agent execution, and MCP interaction flow.

1. Feature Set Covered (Part 1)

  • Prompt-to-DAG Transformation

  • Agent DAG Construction and Planning

  • Agent Execution Lifecycle

  • UI ↔ MCP Communication Loop

  • Contextual Feedback Loop

2. Sequence Diagram: Prompt to Execution

sequenceDiagram
  participant User
  participant UI
  participant MCP Server
  participant DAG Planner
  participant Agent A
  participant Agent B

  User->>UI: Enter simulation prompt
  UI->>MCP Server: Send prompt via REST/WebSocket
  MCP Server->>DAG Planner: Interpret and tokenize prompt
  DAG Planner->>MCP Server: Return agent DAG with execution metadata
  MCP Server->>Agent A: Initialize with input context
  MCP Server->>Agent B: Initialize (dependent on A)
  Agent A->>MCP Server: Emit output and status
  MCP Server->>Agent B: Pass output context from A
  Agent B->>MCP Server: Emit final output
  MCP Server->>UI: Stream live metrics, logs, status

3. Architecture Block Diagram

graph TD
  A[UI: Simulation Playground] --> B(MCP API Gateway)
  B --> C{Prompt Interpreter}
  C --> D[DAG Planner]
  D --> E[Execution Context Manager]
  E --> F[Simulation Agent Pool]
  F --> G1[Agent A: NN Graph]
  F --> G2[Agent B: Accuracy Monitor]
  F --> G3[Agent C: Parameter Tuner]
  G1 --> H[Trace Logs/Outputs]
  G2 --> H
  G3 --> H
  H --> I[Live Feedback to UI]

4. DAG Planning Module (Execution Design)

4.1 Input:

  • Prompt: "Simulate a neural network with dropout tuning on MNIST dataset"

4.2 Components Extracted:

  • Model Type: Neural Network

  • Dataset: MNIST

  • Feature: Dropout Optimization

4.3 DAG Constructed:

[Prompt Parser] → [DataLoaderAgent] → [NNGraphAgent] → [DropoutAgent] → [AccuracyMonitor] → [LoggerAgent]

4.4 DAG Metadata:

  • Dependencies

  • Input/output schema for each agent

  • Estimated execution order and priorities

  • Replanning flags based on metric thresholds

5. Agent Execution Lifecycle

Lifecycle Phases:

  1. Initialize: Receive input context and metadata from MCP.

  2. Run: Execute logic with local state or remote compute.

  3. Emit: Output metrics, logs, events.

  4. Wait/Re-trigger: Pause for dependent results if needed.

  5. Complete: Signal termination or raise feedback flag.

Communication Protocol:

  • All agents register capabilities via agent_manifest.json

  • Input/output handled via JSON + event bus or gRPC + WebSocket

  • Agents are stateless; MCP manages context and versioning

6. UI Feedback Loop

Agent Cards (UI Component):

  • Agent name

  • Runtime state (Running, Waiting, Failed, Completed)

  • Metrics block (accuracy, loss, temp, time)

  • Logs stream viewer

  • Parameter injection overlay (if modifiable)

Live Communication:

  • WebSocket channel from MCP to UI: agent_status_feed

  • JSON patches to update card state and DAG edges

  • Replan feedback messages pushed as overlay actions

7. Simulation Versioning Engine

Purpose:

  • Preserve full traceability of each simulation run.

  • Enable rollback, audit, and iterative refinement.

Design Components:

  • Version Metadata Manager:

    • Stores run UUID, timestamp, user ID, DAG hash.

  • State Snapshot Store:

    • Serializes all agent states, context memory, and DAG edges at key checkpoints.

  • Diff Tracker:

    • Compares agent output changes between versions to show evolution.

Storage Options:

  • Local volume (dev mode)

  • IPFS for decentralized access

  • S3-compatible storage for high-throughput export

8. Simulation Replay & Restoration

Replay Workflow:

  1. User selects a previous run version from simulation history.

  2. MCP pulls DAG snapshot and agent output state.

  3. DAG is reconstructed, but agents run in "replay" mode (no external effect).

  4. Users can annotate or fork this replay into a new prompt.

Use Cases:

  • Diagnosing incorrect results

  • Demonstrating reproducibility

  • Benchmarking changes across agents

9. Output Export and Analysis

Output Types:

  • Structured JSON:

    • All final agent outputs + metrics

  • PDF/Markdown Reports:

    • Auto-generated summaries for publication or sharing

  • Visual Exports:

    • Graph renderings of DAG, timelines, accuracy plots

  • Serialized Models:

    • e.g., PyTorch .pt, ONNX .onnx, scikit .pkl

Export Triggers:

  • Manual (UI button)

  • Automated at completion (based on user preferences)

Integration Hooks:

  • Export events can trigger webhooks or external APIs

  • Metadata indexed in Supabase or decentralized ledger for audit

10. Reusable Experiment Templates

Concept:

  • Users can turn a simulation DAG + agents into a template

  • Templates are stored as composable JSON descriptors

  • Any prompt can instantiate a template with override options

Implementation:

  • template_manifest.json defines:

    • Required agents and ordering

    • Optional/default parameters

    • Visualization hints for UI rendering

UI Capabilities:

  • Fork Template

  • Modify Parameters

  • Publish to Marketplace

11. Agent Plugin Framework

Goals:

  • Enable community and institutional contributions

  • Allow integration of domain-specific agents

Plugin Definition:

Each plugin must:

  • Include a valid agent_manifest.json file

  • Conform to agent interface contracts:

    • initialize(), run(context), emit()

    • Input/output schema (JSON)

  • Be compiled as a WASM binary or containerized microservice

Plugin Registry:

  • Central registry maps plugin name → binary → docs

  • MCP fetches verified plugin agents on-demand

  • Security sandbox (WASM limits, timeouts, memory caps)

Example Custom Plugins:

  • GenomicsSimAgent

  • PolicyImpactAgent

  • SatelliteDataIngestor

  • EconomicModelRunner

Architecture Additions (Diagrams)

graph TD
  A[UI: History Panel] --> B[Version Metadata Store]
  B --> C[Snapshot Engine] --> D[MCP Replay Engine]
  D --> E1[Replay DAG Render]
  D --> E2[Replay Agent Logs]

  F[Export Module] --> G1[PDF Generator]
  F --> G2[Model Export Engine]
  F --> G3[Webhook Dispatcher]

  H[Plugin Developer] --> I[Plugin Builder CLI]
  I --> J[Agent Registry]
  J --> K[MCP Agent Loader]

12. Semantic Memory Layer

Purpose:

Enables memory persistence and contextual learning across multiple simulation runs.

Implementation:

  • Memory Store: Key-value store attached to user profiles.

  • Entity Graph: Indexed map of key scientific terms, agents used, and outcomes.

  • Query Adapter: When a new prompt is entered, MCP consults memory for prior relevant agent flows, parameter configurations, and outcome statistics.

Benefits:

  • Improves relevance of future DAG generation.

  • Allows long-term user profiling.

  • Enables auto-suggestion of optimizations.

13. AI Co-Pilot Integration

Core Modules:

  • Auto-Explainer Agent:

    • Converts agent output + logs into readable summaries.

    • Offers step-by-step explanation of DAG logic.

  • Suggestion Engine:

    • Based on previous runs and memory context.

    • Recommends agent additions, deletions, or parameter tweaks.

    • Surfaces questions like: "Do you want to re-run with higher dropout to reduce overfitting?"

UX Integration:

  • Inline suggestions next to agent cards.

  • AI chat overlay for deeper analysis.

  • Natural language editable flowcharts.

14. Prompt-Aware Replanning

Intelligent Replanning Lifecycle:

  1. MCP builds initial DAG from prompt.

  2. As agents run, metrics are compared with prior similar runs.

  3. If thresholds deviate, replanning module updates DAG.

  4. Semantic difference is logged and replayable.

Example:

  • Prompt: "Simulate COVID-19 spread under different mobility rates."

  • After 10% of simulation, MCP recognizes insufficient spread modeling.

  • Reinjects MobilityAgent + updated PopulationAgent without user intervention.

15. Multi-User Collaboration

Features:

  • Shared canvases for real-time simulation editing.

  • Role-based access (viewer, editor, runner).

  • Per-agent comment threads.

  • Co-authoring on simulation reports.

  • Activity timeline and simulation forks.

Sync Infrastructure:

  • WebSocket room channels with YJS/CRDT backend for real-time syncing.

  • Snapshot-based locking for DAG operations.

  • Agent-scoped permission schema.

16. Intelligent DAG Editor

Visual Intelligence Enhancements:

  • Agent Suggestions: Shown contextually when hovering on edges.

  • Anomaly Detection Alerts: DAG nodes with anomaly outputs are color-coded.

  • History-Aware Branching: UI shows version paths when editing a previously saved DAG.

Editing Capabilities:

  • Natural language: "Add a new dropout layer after this node."

  • Right-click options: Replace agent, fork path, run in sandbox mode.

17. Architecture Enhancements

graph TD
  A[User Prompt] --> B[Prompt Interpreter]
  B --> C[Semantic Memory Adapter]
  C --> D[DAG Planner]
  D --> E1[Co-Pilot Module]
  D --> E2[Suggestion Engine]
  D --> E3[Live DAG Renderer]

  F[Agent Feedback Loop] --> G[Replanning Engine]
  G --> D

  H[Multi-User Sync Layer] --> I[Realtime DAG Editor]
  I --> J[Canvas UI]

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