AI agent technical lead and full-stack engineer with a strong foundation in architecture and product delivery. I lead LLM capabilities into real workflows, building auditable, recoverable and continuously evolving agent products and cross-platform clients, with hands-on depth in Skills, Memory, RAG, evaluation governance, Vite+, Electron and Rust.
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AI Engineering
A complete engineering loop across Skills, Harness, Memory, and RAG
Encapsulating model capabilities into Skills, connecting external tools via MCP, and managing session memory in an observable harness — the foundation for shipping reliable, production-grade agents.
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Agent Skills & Tools
Encapsulate domain actions into reusable agent skills and connect external capabilities via the Model Context Protocol (MCP). Design tool-calling layers with approval workflows, structured output, and streaming interactions, leveraging sub-agents to break down complex tasks.
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Agent Harness & Architecture
Treat the harness as the product layer that decides how much of a model's capability actually ships — the same engineering path Anthropic (Claude Code) and Cursor bet on. Build production-ready agent runtimes covering session management, state-machine scheduling, lifecycle hooks, approval-gated tool execution with per-profile allowlists, event sourcing, and automatic recovery of interrupted runs.
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Memory & Context Engineering
Manage LLM contexts efficiently. Utilize short-term memory, graph-based context, prompt caching, and token budget control to balance multi-hop reasoning quality with computational costs.
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RAG & Retrieval
Construct multi-dimensional knowledge retrieval pipelines. Combine text chunking, vector embeddings, full-text search, and inverted indexes, optimized with LLM reranking strategies to ensure the accuracy and traceability of generated content.
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Selected Work
Selected Work & Engineering Delivery
A curated selection of production-proven systems, spanning agentic knowledge bases, AI coding tools, encrypted communication clients, and desktop trading systems.
Project lead / agent full-stack engineer · 2026.03 - present
Led a hotel procurement and contract-review platform from architecture through delivery, covering bid intake, scoring, contract compliance, award and archival. Owned the agent runtime, backend, data governance and React workbench, turning expert methodology into an auditable, evaluated and progressively deployable decision system.
Designed a collaboration architecture of a supervisor agent plus multiple specialist review sub-agents, each owning one of the technical, commercial and contract-compliance review chains.
Worked with domain professors to encode supply-chain, negotiation and legal-compliance review methodology into each sub-agent's scoring rubric and expert-role persona, so every sub-agent reviews from an explainable, discipline-specific perspective.
Built a Slock-style 'group-chat' collaboration layer for the sub-agents: each is a persistent member with its own identity and memory that joins a shared channel, exchanges structured messages over a shared context, and is coordinated by the supervisor through turn-taking and @-mention handoffs — so the specialist agents deliberate over a single bid in one room and converge on a review verdict.
A local-first desktop agent workbench built around a robust, recoverable agent harness — visual chat, workspace tools, approvals, observable run records — from which I distilled a standardized, reusable desktop application architecture.
Designed and implemented the core agent runtime (harness layer), separating model calls, context assembly, tool execution, approvals, sub-agent delegation, and event logging into observable, recoverable modules, with multi-provider support (Anthropic, OpenAI, AI Gateway — including an API-mode toggle and a proxy-aware network layer).
Deliberately narrowed the workspace tool surface to a minimal, auditable set (read / ls / grep / edit / write, with writes gated by approval), and paired it with secret-path filtering and secret redaction before any tool data is persisted (API keys and tokens auto-scrubbed), keeping execution risk contained and auditable.
Built the Run Inspector and a durable pending-approvals list: each assistant message opens that run's tool calls and event timeline, and pending approvals resume across app restarts (auto-expiring after 7 days).
Vite+ · Electron · React · Hono · AI SDK · SQLite · Rust CLI
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Internal knowledge agentic system
Agent Engineer · 2025.01 - present
A personal knowledge management and spaced repetition system featuring agent-assisted chat, an advanced retrieval backbone, graph-based context, and cross-platform rich-text editing.
Designed a Mastra agent-driven architecture that safely executes business-data mutations through sandboxed tool calls with approval gates (data-mutating tools require approval; read-only tools stay ungated).
Architected a multi-path retrieval engine combining LLM query rewriting, full-text search, title match, tag inverted indexes and pgvector embeddings, enhanced by a precise LLM reranking strategy.
Developed a knowledge graph engine using xyflow, converting references and tags into high-density context to improve multi-hop reasoning across extensive notes.