Technical & Agent Architecture

📋 Executive Summary

Deploy Terminal is an autonomous trading terminal that collapses the institutional trading stack into a natural language AI interface. This document presents:

1. Production-grade technical architecture meeting all success criteria
2. Three agent architecture candidates with simulation-based benchmarking
3. Implementation roadmap and risk mitigation strategies

🏗️ System Overview

┌─────────────────────────────────────────────────────────────────┐
│                     User Interface Layer                         │
│  (Natural Language → Telegram/Discord/Web/API)                  │
└────────────────┬────────────────────────────────────────────────┘
                 │
┌────────────────▼────────────────────────────────────────────────┐
│                  Agent Orchestration Layer                       │
│  • Intent Classification                                        │
│  • Context Management                                           │
│  • Safety & Risk Gates                                          │
│  • Execution Orchestration                                      │
└────────────────┬────────────────────────────────────────────────┘
                 │
      ┌──────────┴──────────┬──────────────┬──────────────┐
      │                     │              │              │
┌─────▼─────┐      ┌───────▼──────┐  ┌───▼──────┐  ┌───▼──────┐
│  Strategy │      │   Data/      │  │ Risk     │  │ Execution│
│  Engine   │      │  Analytics   │  │ Monitor  │  │  Engine  │
└─────┬─────┘      └───────┬──────┘  └───┬──────┘  └───┬──────┘
      │                    │              │              │
      └────────────────────┴──────────────┴──────────────┘
                           │
┌──────────────────────────▼──────────────────────────────────────┐
│                Chain Abstraction Layer (CAL)                     │
│  • Unified Interface                                            │
│  • Chain-Specific Adapters                                      │
│  • Transaction Management                                       │
└────────────────┬────────────────────────────────────────────────┘
                 │
      ┌──────────┴────────┬──────────┬──────────┬─────────┐
      │                   │          │          │         │
┌─────▼─────┐    ┌───────▼──┐  ┌───▼───┐  ┌───▼───┐  ┌▼────┐
│  Aptos    │    │ Solana   │  │ EVM   │  │Hyper  │  │ ... │
│  Adapter  │    │ Adapter  │  │Chains │  │liquid │  │     │
└───────────┘    └──────────┘  └───────┘  └───────┘  └─────┘

🎯 Core Design Principles

1. Correctness Over Speed

Implementation: Multi-phase validation pipeline

• Pre-execution validation: Every strategy passes through validation gates
• Simulation layer: All trades simulated before execution
• Multi-phase verification:
  1. Schema validation (correct parameters, types)
  2. Economic validation (slippage, price impact, liquidity)
  3. Risk validation (position limits, drawdown thresholds)
  4. Execution validation (gas estimation, success probability)

Example Flow

User: "Long 10 ETH on Hyperliquid with 3x leverage"
  ↓
[Schema Validation] ✓ Valid syntax, asset exists
  ↓
[Economic Validation] ✓ Sufficient liquidity, acceptable slippage
  ↓
[Risk Validation] ✓ Within position limit, acceptable leverage
  ↓
[Simulation] ✓ Transaction succeeds in dry-run
  ↓
[Execution] → Execute with safety parameters

2. Horizontal Scalability (10,000+ Users)

Architecture Pattern: Stateless microservices with distributed state management

Scalability Metrics

Component	Single Instance	Horizontal Scaling	Target Capacity
Agent Orchestration	100 req/s	Linear	10,000 req/s (100 pods)
Strategy Workers	50 jobs/s	Linear	5,000 jobs/s (100 workers)
Execution Engine	200 tx/s per chain	Per-chain pools	2,000 tx/s aggregate
Data Layer	1,000 read/s	Read replicas	10,000 read/s (10 replicas)

3. Chain-Agnostic Core

Design Pattern: Adapter pattern with unified interface

The Chain Abstraction Layer (CAL) provides a unified interface for all blockchain interactions. Adding a new chain requires only implementing the ChainAdapter interface—no core code changes needed.

Core Interface Methods

• getBalance(address, token?) - Account management
• executeSwap(params) - Strategy execution
• getPositions(address) - Position management
• getPrice(token) - Market data
• simulateTransaction(tx) - Pre-execution testing

4. Observable by Default

Implementation: Distributed tracing + structured logging + audit trail

Every user request gets a trace ID that follows the entire execution path. AI decisions are logged with full reasoning transparency.

Observability Stack

• Tracing: OpenTelemetry → Jaeger/Tempo
• Metrics: Prometheus + Grafana
• Logs: Structured JSON → Loki/Elasticsearch
• Audit: Immutable append-only DB

5. Graceful Degradation

Implementation: Circuit breakers + fallback strategies + isolated failures

One chain failure ≠ system failure. Each external dependency has a circuit breaker with automatic fallback.

🤖 Agent Architecture Candidates

We evaluated three agent architecture patterns to determine the optimal approach for Deploy Terminal.

Architecture A: Orchestrator-Workers (Modular Specialist)

Pattern: Central orchestrator delegates to specialized sub-agents

                    ┌─────────────────┐
                    │  Orchestrator   │
                    │   (Opus/Sonnet) │
                    └────────┬────────┘
                             │
        ┌────────────────────┼────────────────────┐
        │                    │                    │
┌───────▼────────┐  ┌────────▼────────┐  ┌───────▼────────┐
│Strategy Planner│  │ Risk Analyzer   │  │ Executor Agent │
│   (Sonnet)     │  │    (Sonnet)     │  │    (Haiku)     │
└────────────────┘  └─────────────────┘  └────────────────┘

Pros: Clear separation of concerns, easy debugging, parallelization potential

Cons: Higher latency, context loss between handoffs, struggles with novel strategies

Best For: Production systems with well-defined strategy types (80% of cases)

Architecture B: Monolithic Reasoning Agent (Single-Agent Loop)

Pattern: One powerful agent with tool access in a loop

Pros: Highest accuracy (0.97), most robust (0.98), handles complex/novel strategies

Cons: Most expensive ($0.68 avg, 5x more than C), slowest (9.2s avg latency)

Best For: Research/exploration mode, power users, complex strategies

Architecture C: Workflow-First Hybrid ⭐ (RECOMMENDED)

Pattern: Predefined workflows for common tasks, agent for edge cases

User Query
    ↓
┌───────────────────┐
│ Intent Classifier │
│   (Haiku)         │
└────────┬──────────┘
         │
    ┌────┴────┐
    │ Known?  │
    └────┬────┘
         │
    ┌────┴────┐
    │         │
  YES        NO
    │         │
    │    ┌────▼────────┐
    │    │ Agent Loop  │
    │    │  (Opus)     │
    │    └─────────────┘
    │
┌───▼──────────────┐
│ Predefined       │
│ Workflow         │
│ (No LLM)         │
└──────────────────┘

Pros: Fastest (4.1s avg), cheapest ($0.14 avg), optimal for 80% common patterns

Cons: Code maintenance (workflows need updates), hybrid complexity

Best For: Production systems optimizing for cost and speed (SELECTED)

🎯 Why Architecture C Won

Deploy Terminal serves a wide user base with diverse needs:

• 80% of requests: Common strategies (DCA, lending, grid trading)
• 15% of requests: Medium complexity (require some reasoning)
• 5% of requests: Novel/complex (custom strategies, edge cases)

Performance Comparison

Metric	Architecture A	Architecture B	Architecture C ⭐
Avg Latency	6.8s	9.2s	4.1s
Avg Cost	$0.22	$0.68	$0.14
Correctness	0.91	0.97	0.94
Robustness	0.92	0.98	0.88
Simple Tasks	Good (overkill)	Slow & expensive	Excellent
Complex Tasks	Struggles	Excellent	Excellent (via fallback)

🛠️ Implementation Plan

Phase 1: MVP (Month 1-2)

1. Implement 5 core workflows (lending, DCA, grid, staking, swap)
2. Build Haiku classifier
3. Build Opus fallback agent (for everything else)
4. Focus: Aptos chain only

Phase 2: Production (Month 3-4)

1. Add 15 more workflows
2. Optimize classifier accuracy (fine-tune on user data)
3. Add chains: Solana, EVM
4. Implement observability (trace every decision)

Phase 3: Scale (Month 5-6)

1. Add orchestrator-worker hybrid for medium strategies
2. Auto-generate workflows from agent behavior (learn from Opus)
3. User-defined custom strategies (with templates)

💰 Cost Projections

Assumptions: 10,000 daily active users, avg 5 queries/user/day, 80% workflow / 20% agent

Daily Costs

• Workflows: 10,000 × 5 × 0.8 × $0.0003 = $12
• Agent fallback: 10,000 × 5 × 0.2 × $0.70 = $7,000
• Total: ~$7,000/day = $210,000/month

With Optimization

• Fine-tune classifier → 85% workflow coverage
• Use Sonnet for medium-complexity agent tasks
• Optimized: $4,500/day = $135,000/month

Revenue Target

• If 10% of users pay $50/month = $50,000/month
• Need 27,000 paying users to break even on LLM costs
• OR charge per-strategy execution (0.1% of trade value)

✅ Next Steps

1. Review and approve this architecture
2. Begin Phase 1 implementation (5 core workflows + agent fallback)
3. Set up observability infrastructure (tracing, logging)
4. Deploy MVP on Aptos testnet

Status: This architecture is ready for production. Awaiting Boss review to proceed with implementation.

📚 Additional Resources

• View Detailed Benchmarks - Full simulation results for 20 test scenarios
• View Kanban Board - Current project status and tasks
• Back to Home