AI-Native Architecture

DreTech.ai

Architecting the Agentic Enterprise

DreTech.ai is building two systems and the loop between them: a tool-portable Agent OS that defines what an agent is, and Personal AI, a self-hosted multi-agent runtime. The next step connects them — using the Agent OS to train and tune the runtime into a measurable AI harness.

Explore the Vision View Lab

Thesis

AI-native architecture is not optional

Most enterprise AI efforts bolt models onto legacy workflows. Copilots assist. Wrappers abstract. But none of them restructure. The next generation of enterprise systems must be designed from the ground up — with agency, context, and autonomy as first-class primitives.

Beyond copilots

Copilots are a transitional pattern. They optimize human workflows without changing the underlying architecture. Agentic systems operate independently within defined constraints, executing multi-step reasoning across organizational boundaries.

Beyond wrappers

API wrappers commoditize model access but add no structural intelligence. Real enterprise AI requires orchestration layers that manage state, enforce policy, and route context — not just forward prompts.

Context as infrastructure

Enterprise decisions depend on context that lives across systems, teams, and timelines. An agentic enterprise treats context as a first-class infrastructure layer — indexed, versioned, and accessible to autonomous agents.

Architecture over automation

Automation replaces tasks. Architecture replaces paradigms. DreTech.ai focuses on the structural patterns that make autonomous enterprise systems reliable, auditable, and composable at scale.

Focus Areas

Core build areas

Each focus area maps to a concrete system under active development — and to the training loop that ties them together.

01

Portable Agent Definitions

The Agent OS — a tool-portable system of Markdown files defining an agent's identity, context, skills, memory, and connections. Canonical source lives once; per-tool adapters compile it for Codex, Claude Code, OpenClaw, and others.

02

Self-Hosted Multi-Agent Runtime

Personal AI — a self-hosted assistant on macOS, fronted by Discord and orchestrated by the OpenClaw gateway. Seven specialized agents reach Google Workspace, iMessage, Perplexity, and a Neo4j knowledge graph.

03

Policy-Based LLM Routing

A self-hosted routing layer (LiteLLM plus a policy router) that classifies every request and picks the best backend — Claude, Perplexity, or local Ollama — with hard guardrails so secrets stay local and tool-use never goes to a weak model.

04

AI Harness Training

The next build: feed Agent OS definitions into the Personal AI runtime, measure behavior with an evals harness, and iterate — turning hand-authored agent specs into a repeatable, measurable training loop.

Current Direction

Training the harness

The two systems were built independently. The next phase joins them into a single feedback loop.

AI harness training loop: Agent OS definitions compile into the Personal AI runtime, the evals harness measures behavior, and the signal refines the definitions and routing policy. Agent OS identity · context skills · memory connections tool-portable spec Personal AI OpenClaw · 7 agents policy router Claude · Perplexity · Ollama self-hosted runtime Evals Harness routing quality behavior signal measured feedback compile measure refine definitions + routing policy

The Agent OS already captures what an agent should be — identity, context, skills, memory, and connections — in tool-neutral Markdown. Personal AI already runs agents live, with policy-based routing across Claude, Perplexity, and local models, and an evals/ harness that measures routing quality.

The upcoming project closes the loop: compile Agent OS definitions into the Personal AI runtime, exercise them through the harness, and use the evals signal to refine both the definitions and the routing. The result is an AI harness training solution — a repeatable way to take a written agent specification and tune it into measured, reliable behavior.

Built in public. Every entry on the Lab reflects an active line of work — refined through iteration, not theory.