sys.init() — Interactive

Architecting
Intelligence.

AI systems architect. I build the scaffolding around models — agent harnesses, deep agents, skills runtimes and LLM orchestration — that turns raw capability into reliable autonomous systems.

Explore Agents arrow_forward download Download CV

◇ Agent-native

⚙ Harness-driven

∞ LangGraph · MCP

sys.status == OK

v.1.0_LIVE

AGENT_ARCHITECTURE // HARNESS · DEEP-AGENTS · SKILLS

Agent Systems

Reference and exploratory architectures for autonomous agents — the harnesses, skill runtimes and orchestration graphs that make language models dependable in the loop.

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hub

CORE

Agentic Harness

The control loop that wraps a model with tools, retries, guardrails and structured I/O — the substrate every reliable agent runs on.

Harness Tools

memory

DEEP

Deep-Agent Orchestrator

Long-horizon planner that decomposes a goal, spawns sub-agents and supervises them over shared, typed state until the objective is met.

DeepAgent Planner

extension

RUNTIME

Skills Runtime

Composable, sandboxed capabilities the agent loads on demand — versioned, testable and discoverable, exposed over MCP.

Skills MCP

schema

LOOP

Tool & Eval Loop

Closed feedback loop — act, observe, evaluate, correct — with offline evals gating every change before it ships.

Evals Feedback

hub

GRAPH

Multi-Agent Graph

Stateful graph of specialised agents (planner, critic, executor) over typed memory — LangGraph for orchestration and control flow.

LangGraph Memory

add

INITIALIZE_NEW_GRAPH

Awaiting input parameters...

AGENT_STACK

SYS.OP: HARNESS · SKILLS · MEMORY · ORCHESTRATION

Core Stack

Py 99

Python

Agent core

Lg 95

LangGraph

Orchestration

An 93

Anthropic API

Models / harness

Mcp 90

MCP

Skills / tools

Hn

Harness

Mem

Memory / RAG

Ev

Evals

Aws

AWS

Capability Log

capability.log

>> LOADING CAPABILITY ARC...

sys@mac:~$ whoami

ai_systems_architect

sys@mac:~$ cat capability.log

systems & cloud-native foundations

LLM integration — RAG, tools, structured output

agent harnesses — guardrails, retries, eval loops

deep agents — planning, skills, memory, orchestration

sys@mac:~$ status

STATUS: SHIPPING. OPEN TO HARD AGENT PROBLEMS.

sys@mac:~$ _

System Status: Active

Spatial & Multimodal.

Where agents meet space and perception: experiments in 3D, spatial interfaces and multimodal interaction — extending autonomous systems beyond text.

Enter the Lab arrow_forward

Augmented reality architectural workspace

[ENV_01] Spatial Mapping

Holographic Workspace Protocol

Conceptualizing depth-aware interfaces for architectural modeling. Using hand-tracking gestures to manipulate complex structural nodes in a zero-gravity digital environment.

view_in_ar METRICS

Render Telemetry

Polygons/Sec 12.4M

Latency 4ms

Spatial Anchor Precision 99.8%

OBJ // ARCH_NEXUS

Structural Topology Scan

Real-time point-cloud rendering of the proposed vertical ecosystem.

touch_app

Haptic Feedback Integration

Bridging the gap between the ethereal and the tactile. Exploring micro-vibration patterns to simulate physical resistance when interacting with virtual architectural materials.

download Download Specs

CAPABILITY_ARC // PRACTICE

Capability Arc

How the practice compounded — from systems foundations to deep-agent architecture.

[01 · FOUNDATIONS]
Foundations — full-stack systems & cloud-native delivery.
[02 · CLOUD]
Cloud — scaled processing and infrastructure on AWS.
[03 · LLM]
LLM integration — embeddings, RAG, tool use, structured output.
[04 · AGENTS]
Agent harnesses & deep agents — skills, memory, eval loops, LLM orchestration.

OPEN_CHANNEL // CONNECT

Building agents, harnesses or LLM orchestration? Open a channel — transmissions reach the operator.

ACTIVE_NODES

2/5 Sockets Open

cloud_sync

AWS-Production

Operational

us-east-1 // IAM 12ms latency

monitoring

LangSmith-Dev