
2
2
Bangladesh
5+ years of experience
- Asif Iqbal, 38, a tech enthusiast from Dhaka, Bangladesh. - COO at a startup, now focusing on "Meta Together," a blockchain venture. - Skilled in AI, blockchain, and leadership. - Aims to create tech-driven companies and support loved ones.

The Problem: AI trading agents today operate as "black boxes" requiring full private key access. One hallucination, one compromise, and funds are gone. Current safety tools are advisory-only—they warn but don't stop bad trades. The Solution: Vertex Sentinel introduces a production-grade, 3-layer security architecture that makes unauthorized trades mathematically impossible: Intent Layer: Agents construct TradeIntents (pair, volume, maxPrice, deadline) and sign them using EIP-712 typed data signing—completely off-chain. No private key delegation is ever required. Sentinel Layer: The RiskRouter.sol smart contract intercepts every intent and enforces: signature verification via ECDSA.recover(), agent authorization via ERC-8004 identity registry, deadline validation, and circuit breakers preventing volume limit violations. Execution Layer: Only trades with TradeAuthorized events reach the exchange. Any failure triggers CriticalSecurityException—system halts, funds protected. Live Proof: We executed 4 real BTC/USD trades on Kraken with 100% success rate. Every trade cryptographically signed. Every decision auditable. Full P&L tracking demonstrated. Key Technical Achievements: - Deployed RiskRouter on Sepolia: 0xd6A6952545FF6E6E6681c2d15C59f9EB8F40FdBC - ERC-8004 compliant AgentRegistry with on-chain reputation scoring - Model Context Protocol (MCP) integration with Kraken CLI - Immutable audit trail in logs/audit.json with reasoning and signatures - Open-source SDK for rapid AI agent integration The Vertex Gap: Unlike centralized "trust the company" solutions (ARMA, Mamo, ZyFAI), Vertex Sentinel delivers "trust the contract"—verifiable, transparent, and immutable security enforced by code. We're building the trust infrastructure for the agentic economy. Risk management first. Automation second.
12 Apr 2026

Core Architecture The system is built on three primary layers: Distributed Intelligence Layer Implements triple redundancy using three independent LLM nodes Each node runs a quantized, space-optimized language model Independent RAG (Retrieval Augmented Generation) modules per node Isolated memory and processing resources Individual vector databases for context retrieval Knowledge Management Layer Consensus Layer Advanced NLP-based response similarity analysis Majority voting with semantic understanding Automatic anomaly detection and filtering Graceful degradation under node failures Key Innovations Semantic Consensus Protocol Novel approach to comparing LLM outputs Handles natural language variance Maintains reliability under partial failures Lightweight but capable inference engine Distributed RAG Implementation Synchronized vector databases Consistent knowledge access Redundant information retrieval Failure Recovery Automatic node health monitoring Self-healing capabilities Graceful performance degradation Zero-downtime recovery Implementation Details Docker-based containerization for isolation gRPC for high-performance inter-node communication FAISS for efficient vector similarity search Sentence-BERT for response embedding Custom consensus protocols for LLM output validation The system is specifically designed to operate in space environments where traditional AI systems would fail due to radiation effects, resource constraints, or hardware failures. It provides mission-critical reliability while maintaining the advanced capabilities of modern LLMs.
9 Feb 2025