Embodied AI Architect - Multi-Agent Architecture Plan

In progress · updated 2026-10-30 · embodied-ai-architect/docs/plan/agent-architecture.md

Date: 2025-11-02

Overview

This document outlines the core architecture for the Embodied AI Architect design system. The system’s value proposition is to analyze Embodied AI models and provide performance and energy consumption benchmarks across different hardware architectures (CPU/DSP, GPU, TPU, DPU, KPU).

Design Goals

• Accept Python prototype models (PyTorch, TensorFlow, etc.)
• Evaluate performance, accuracy, reliability, and robustness
• Generate optimized C++/Rust production implementations
• Benchmark across multiple hardware targets
• Provide cost-benefit analysis for deployment decisions

Proposed Multi-Agent Architecture

Hierarchical Orchestration Pattern

The system uses a hierarchical orchestration architecture with specialized agent roles coordinated by a central orchestrator.

Core Agent Types

1. Orchestrator Agent (Control Layer)

Responsibilities:

• Entry point for the system
• Manages workflow state machine
• Coordinates agent interactions
• Handles user queries and feedback
• Makes high-level decisions about optimization strategies

2. Model Analysis Agent

Responsibilities:

• Parses DNN architectures (PyTorch, TensorFlow, ONNX, etc.)
• Extracts computational graph, layer types, parameter counts
• Identifies memory requirements and compute patterns
• Detects model family (CNN, Transformer, RNN, hybrid)
• Profiles model characteristics (depth, width, sparsity)

Output: Model Intermediate Representation (IR)

3. Hardware Profile Agent

Responsibilities:

• Maintains knowledge base of hardware capabilities:
  • CPU/DSP: SIMD widths, cache hierarchies, instruction sets
  • GPU: CUDA cores, memory bandwidth, tensor cores
  • TPU: systolic array dimensions, quantization support
  • DPU: dataflow architectures, streaming capabilities
  • KPU: specialized AI accelerator characteristics
• Suggests optimal hardware-model pairings
• Estimates theoretical performance bounds

Output: Target hardware recommendations with rationale

4. Code Transformation Agents (per language/target)

Subtypes:

• Python Optimizer Agent: Optimizes Python prototype (vectorization, JIT, etc.)
• C++ Transpiler Agent: Python → C++ with optimization passes
• Rust Transpiler Agent: Python → Rust with safety guarantees
• Target-Specific Compiler Agents: Generate optimized code for each hardware (CUDA, Metal, OpenCL, vendor SDKs)

Output: Optimized source code for target platform

5. Deployment Agent

Responsibilities:

• Handles hardware provisioning (cloud, edge, simulation)
• Manages runtime environments and dependencies
• Configures hardware-specific settings (clock speeds, power modes)
• Handles containerization/virtualization

Output: Running instance on target hardware

6. Benchmark & Profiling Agent

Responsibilities:

• Runs standardized performance tests
• Measures latency, throughput, memory usage
• Monitors energy consumption (via hardware counters, external meters)
• Collects thermal data
• Generates performance profiles (hotspots, bottlenecks)

Output: Performance metrics dataset

7. Validation & Verification Agent

Responsibilities:

• Accuracy: Compares outputs against golden reference
• Reliability: Stress tests, fault injection, edge cases
• Robustness: Adversarial inputs, distribution shifts
• Ensures numerical consistency across platforms
• Validates safety properties for production systems

Output: Correctness and robustness metrics

8. Report Synthesis Agent

Responsibilities:

• Aggregates metrics across all hardware targets
• Generates comparison matrices (performance vs. energy vs. cost)
• Creates visualizations (Pareto frontiers, heatmaps)
• Provides actionable recommendations
• Estimates TCO (Total Cost of Ownership)

Output: Comparative analysis report

Agent Interaction Pattern

User Request
    ↓
Orchestrator Agent
    ↓
    ├─→ Model Analysis Agent ─→ [Model IR]
    ↓                               ↓
    ├─→ Hardware Profile Agent ─→ [Target Recommendations]
    ↓                               ↓
    └─→ [For each target hardware] ─→
            ↓
            ├─→ Code Transformation Agent ─→ [Optimized Code]
            ↓
            ├─→ Deployment Agent ─→ [Running Instance]
            ↓
            ├─→ Benchmark Agent ─→ [Performance Metrics]
            ↓
            └─→ Validation Agent ─→ [Correctness Metrics]
    ↓
Report Synthesis Agent ─→ [Comparative Analysis]
    ↓
User Feedback Loop

Key Design Principles

1. Intermediate Representation (IR)

• Use a common IR (e.g., MLIR, ONNX, or custom) as lingua franca
• Allows model-agnostic analysis and transformation
• Enables progressive lowering to hardware-specific representations

2. Agent Communication Protocol

• Message-passing architecture with well-defined schemas
• Each agent exposes capabilities and requirements
• Async execution with checkpointing for long-running tasks

3. Extensibility

• Plugin architecture for new hardware targets
• Agent registry for dynamic capability discovery
• Versioned APIs for agent interactions

4. Human-in-the-Loop

• Orchestrator can query user for disambiguation
• Interactive exploration of trade-off spaces
• Override mechanisms for expert users

5. Reproducibility

• Capture full provenance (code versions, hardware configs, random seeds)
• Containerized environments for consistency
• Artifact storage for all intermediate results

Suggested Technology Stack

Agent Framework:

• LangGraph, AutoGen, or CrewAI for agent orchestration

Model IR:

• MLIR/ONNX for intermediate representation

Compilers:

• TVM/XLA for multi-hardware code generation

Profiling:

• PyTorch Profiler, NVIDIA Nsight, Intel VTune
• Custom energy monitors

Database:

• PostgreSQL for metrics storage
• Time-series DB for profiling data

API:

• FastAPI for external interfaces

Containerization:

• Docker/Podman for deployment environments

Example Workflow

# User submits an Embodied AI model
request = {
    "model": "path/to/pytorch_model.pt",
    "task": "object_detection",
    "targets": ["cpu", "gpu", "tpu", "edge_tpu"],
    "constraints": {
        "max_latency_ms": 100,
        "max_power_watts": 15
    }
}

# Orchestrator initiates pipeline
orchestrator.process(request)
  → model_analyzer.analyze()
  → hardware_profiler.recommend_targets()
  → [parallel execution across targets]
      → transpiler.optimize_for_target()
      → deployer.deploy()
      → benchmarker.profile()
      → validator.verify()
  → report_synthesizer.generate_comparison()

# Output
{
    "recommendations": [
        {
            "hardware": "NVIDIA Jetson AGX",
            "latency_ms": 45,
            "power_watts": 12,
            "accuracy": 0.94,
            "cost_per_inference": 0.0001
        },
        # ... more recommendations
    ],
    "optimal_choice": "NVIDIA Jetson AGX",
    "tradeoffs": "Pareto frontier chart"
}

Next Steps

1. Define detailed agent interfaces and message schemas
2. Select specific agent orchestration framework
3. Implement Model Analysis Agent (Python prototype)
4. Create Hardware Profile knowledge base
5. Build basic Orchestrator with simple workflow
6. Develop benchmark harness for initial hardware targets
7. Create Report Synthesis templates

Open Questions

• Which agent framework best suits our needs? (LangGraph vs AutoGen vs CrewAI)
• Should we use ONNX or MLIR as primary IR?
• What’s the minimum viable set of hardware targets for initial release?
• How do we handle proprietary hardware SDKs and access?
• What level of human-in-the-loop interaction is optimal?