I build systems where AI carries real production load—and I have spent 15+ years shipping backends and platforms that stay predictable, observable, and operable when usage grows.
AgentStack.tech is a patented, AI-first Universal Processing System: full backend, admin surfaces, and a unified integration plane—TypeScript/Python/React SDK, REST, and MCP with 149+ catalog actions spanning projects, auth, RBAC, billing, wallets & buffs, scheduler, webhooks, analytics, AgentSocial, SSE messenger, RAG & agent memory, game economy, marketplace hooks, and server-side logic.
Role: Founder & Chief Architect—product architecture, protocol & SDK design, multi-tenant data model, AI instruction systems, and end-to-end delivery (dual-shell SPA, TanStack Query, SSE + WebSockets, PostgreSQL, Stripe, observability). Through-line: performance discipline from AAA-style game engineering applied to AI-native infrastructure.
AgentStack is designed so humans and AI agents share the same contract: typed SDK, REST, and MCP—with discovery, documented workflows, and governance baked in—not bolted on.
Most “AI backends” glue an LLM to CRUD. AgentStack is inverted: a multi-tenant universal processing core with Field Access Policy, Protein Command System, Logic Engine, RAG, commerce, social, and realtime—then a first-class MCP catalog (149+ actions) so agents can operate the same platform developers do.
Connect time for new products is measured in minutes because migrations, rollback, RBAC, billing, and observability are part of the architecture—not weekend glue code.
Service orchestration with a universal DNA data model—same conceptual backbone across SaaS, games, social, and commerce verticals.
O(1)-style routing with aggressive caching philosophy—documented target <0.001s on the router hot path; built for horizontal scale.
Universal command bus aligned with automation, internal tooling, and MCP-shaped execution—one vocabulary for scripts and agents.
Role- and user-scoped field masks and triggers—secure exposure for REST, SDK, and LLM-mediated access.
Server-side event-driven rules and processors—business logic without shipping a new microservice for every branch.
agentstack.execute with 149+ actions, discovery, health, and bundled AI prompts (workflows + use-case packs).
Separate user vs developer/admin experiences (mobile-first), TanStack Query server state, 51+ UI components, i18n.
SSE for messenger & social streams; WebSockets for operational/build telemetry—clean separation of product vs ops channels.
How AI is constrained to behave well in production—not only which model you call.
Great AI systems are interfaces + data + policy. Models change weekly; contracts and governance stay.
ai_prompt bundles and use-case packs (game, SaaS, ecommerce, social).Quantified engineering outcomes where documented—plus shipped scope that demonstrates architectural ownership.
These figures appear in AgentStack engineering documentation as consolidation and neural-architecture targets—they summarize intentional core unification, not incidental tuning.
| Dimension | Outcome | Notes |
|---|---|---|
| Core consolidation | ~87% fewer services | Documented reduction (15 → 2 primary core services) to cut coordination and ops drag. |
| Memory footprint | ~76% reduction | Documented unified-core target (822MB → 200MB class targets in architecture docs). |
| Neural Router | <0.001s · O(1) routing | Hot-path latency budget; paired with neural cache discipline. |
| Neural events | 200,000+ events/s | Throughput class for event-driven orchestration paths. |
| Neural cache | 95%+ hit-rate target | Designed into routing and read-heavy platform paths. |
| MCP surface | 149+ actions | Single discovery-oriented catalog for agents—same platform as humans. |
| Product UI kit | 51+ components · i18n | Shipping velocity for admin + dashboard surfaces. |
Figures cited from internal/platform engineering documentation (architecture guides, neural router targets). Treat as engineering claims tied to design intent—always happy to discuss methodology on a call.
Backend (FastAPI, Pydantic, SQLAlchemy, 8DNA / Neural Router patterns), frontend (React, Vite, Tailwind, dual-shell SPA, TanStack Query), data (PostgreSQL, Redis), LLM application architecture (MCP catalog, RAG, agent memory, agentic workflows), commerce (Stripe, wallets, buffs), real-time (SSE messenger, WebSocket ops). Observability: Loguru, OpenTelemetry. Unified SDK (TypeScript @agentstack/sdk, React hooks). Cloud-native, multi-tenant, event-driven.
FastAPI, Data processing, AI/ML integration, Automation
React, Node.js, Real-time systems
Game development, Backend services, ASP.NET Core
Enterprise architecture, Microservices, System design
PostgreSQL, Complex queries, Database optimization
REST APIs, async/await, MCP server, AgentStack core
ASGI server, WebSocket support, production deployment
WebSocket: build status, dashboards. SSE: messenger & real-time social (AgentStack)
Data validation, settings, API schemas (v2)
Event-driven architecture, Real-time communication
Enterprise applications, Microservices
Rapid development, Data processing
Hooks, concurrent features, dashboard & builder UI
Strict mode, SDK types, frontend and tooling
Build tooling, HMR, React plugin, base path
SPA routing, nested routes, loaders
Server state, caching, invalidation (AgentStack)
Utility-first, design tokens, responsive UI
Node-based UIs, logic builder, diagrams
Internationalization, locale switching
@agentstack/sdk, React package, protein-style API consumption
Frontend unit/integration tests, Vite-native runner (AgentStack)
ORM, migrations, async engine, AgentStack core
Async PostgreSQL driver, high performance
Async HTTP client, outbound API calls
Schema design, Complex queries, Performance tuning, JSONB
Caching, Session management, Real-time data, Pub/Sub
Document storage, Flexible schemas
Real-time databases, Authentication, Cloud functions
Full-text search, Log aggregation
Authentication, token validation, password hashing
Payment gateway, subscriptions, AgentStack billing
API keys, sensitive data, TLS
Structured logging, rotation, AgentStack core
Traces, instrumentation (FastAPI, HTTP)
Backend and frontend testing, async tests
Code quality, type-check, build pipeline
Orchestration of LLMs and agents, multi-agent patterns, MCP at scale (149+ catalog actions), discovery, workflow docs, use-case prompts (game, SaaS, ecommerce, social). RAG, collections, semantic search, agent long-term memory. AI instruction design: system prompt bundles, structured workflows (understand → plan → build → publish), prevention-over-cure. Providers: OpenAI, Anthropic, local LLMs (Ollama, vLLM). Frameworks: LangChain, LangGraph, custom orchestrators, function calling.
API integration, function calling, structured outputs
Local LLM deployment, model management, self-hosted
Chains, agents, multi-agent workflows, memory
Vector DB, semantic search, RAG context
agentstack.execute, 149+ actions, discovery, AI prompt bundle
Knowledge-augmented generation, retrieval, evals
System prompts, workflows, MCP ai_prompt / use-case bundles, AI Builder
Containerization, Image optimization, Multi-container setup
Orchestration, Scaling, Deployment
EC2, Lambda, RDS, S3, CloudFront, API Gateway
Version control, CI/CD, Complex workflows
Aligned with shipped platform work — multi-tenant core, MCP catalog, social & messenger (SSE), RAG, billing, unified SDK, observability.
| Skill | Level | Experience | Context |
|---|---|---|---|
| System Architecture Design | Expert | 10+ years | Game servers, microservices, distributed systems |
| Microservices / multi-tenant SaaS | Advanced | 8+ years | Per-tenant clusters, migrations, AgentStack production |
| API Design (REST/GraphQL) | Expert | 10+ years | RESTful principles, GraphQL implementation |
| Database Schema Design | Advanced | 10+ years | PostgreSQL, MongoDB, normalization, optimization |
| Real-time Systems Architecture | Expert | 8+ years | WebSocket, multiplayer backends, live data, AgentStack build status |
| Pydantic / API Schemas | Advanced | 3+ years | Validation, settings, OpenAPI, AgentStack core |
| SQLAlchemy (async) | Advanced | 3+ years | ORM, migrations, PostgreSQL, AgentStack |
| Field Access Policy (FAP) | Advanced | 2+ years | Role/user field masks, triggers, secure data exposure |
| Protein command bus / DNA ops | Advanced | 2+ years | Universal automation surface, integrations |
| Technology | Level | Experience | Context |
|---|---|---|---|
| React (hooks, concurrent) | Advanced | 5+ years | Dashboard, builder UI, AgentStack frontend |
| TypeScript | Advanced | 6+ years | Strict mode, SDK types, frontend and tooling |
| Vite | Advanced | 2+ years | Build, HMR, React plugin, AgentStack |
| TanStack Query | Advanced | 2+ years | Server state, caching, AgentStack |
| Tailwind CSS | Advanced | 3+ years | Utility-first, responsive UI |
| React Flow | Advanced | 2+ years | Node-based UIs, logic builder |
| Dual-shell SPA / audience routing | Advanced | 1+ years | User vs dev surfaces, mobile-first admin (AgentStack) |
| SSE & real-time UX | Advanced | 2+ years | Messenger streams, social presence patterns |
| Language | Level | Years | Current Use |
|---|---|---|---|
| Python | Advanced | 5+ | FastAPI, Pydantic, AgentStack core, AI/ML |
| TypeScript/JavaScript | Advanced | 6+ | React, Vite, TanStack Query, AgentStack frontend |
| C# | Expert | 10+ | Legacy game dev, ASP.NET |
| Java | Advanced | 5+ | Enterprise systems, Architecture |
| SQL | Expert | 10+ | Complex queries, optimization |
| Technology | Level | Experience | Specialization |
|---|---|---|---|
| Ollama | Advanced | 2+ years | Local LLM deployment, model management |
| LangChain | Advanced | 2+ years | LLM chains, prompt engineering, memory |
| ChromaDB/Vector DBs | Advanced | 2+ years | Semantic search, embeddings storage |
| MCP Protocol | Expert | 2+ years | 149+ actions, discovery, workflow & use-case prompts |
| RAG Systems | Advanced | 2+ years | Collections, semantic search, agent memory (production) |
| LangGraph / orchestration | Advanced | 2+ years | Multi-step agents, graphs, eval loops |
| Area | Level | Experience | Context |
|---|---|---|---|
| JWT / bcrypt / Auth | Expert | 8+ years | Token validation, password hashing, AgentStack |
| Stripe / wallets / buffs | Advanced | 2+ years | Subscriptions, trials, multi-wallet economy |
| Loguru / OpenTelemetry | Advanced | 2+ years | Structured logging, traces, AgentStack |
| pytest / Jest | Expert | 8+ years | Backend and frontend testing, async, AgentStack |
10+ years in backend and distributed systems; game development, full-stack and AI/LLM. AgentStack — Universal Processing System: MCP (149+ actions), social & messenger, RAG, commerce, unified SDK, dual-shell product UI.
Tech: Python, FastAPI, PostgreSQL, Docker, Kubernetes/Compose, queues, Stripe and payment rails. React, TypeScript, Vite, Vitest. MCP, RAG, WebSockets (ops), SSE (chat). Observability: Loguru, OpenTelemetry. IDE/agent plugins: Cursor, Claude, GPT, VS Code.
Dreamfrost Studio
Led development of Skybringer: Idle RPG—adaptive UI, core mechanics, SDK expansion—shipping on mobile and desktop-class targets.
World4Play
Indie studio work: game mechanics, multiplayer systems, cloud-backed services, and cross-platform delivery—architecture ownership end to end.
Toadman Interactive (Stockholm)
Multiplayer FPS Block N Load; hardcore action-RPG Immortal: Unchained; built Chronos data analytics platform. Shipped on PC & consoles (PlayStation, Xbox).
ArtPlant (Oslo)
Shipped MMORPG Entropy, cooperative survival Grimm: Dark Legacy, and mechanics prototypes—networking-heavy gameplay across PC & consoles.
Consulting and collaboration: AI systems architecture, MCP integration (large action catalogs), RAG & agent memory, multi-tenant BaaS, billing, real-time social — and AgentStack ecosystem.
AI Systems Architect · Full-Stack · Founder, AgentStack.tech
Location
Praia Grande, São Paulo, BrazilPhone
+55 (13) 99800-4433Telegram
@LanceW4P