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. Personal OS Studio now connects them — a macOS trainer that compiles the Agent OS into live harnesses.

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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

Shipped: Personal OS Studio, a native macOS app that compiles Agent OS definitions into live harnesses — OpenClaw, Hermes, Codex, Claude Cowork — authors, validates, versions, and vaults them, harvests runtime learning back, and measures compiled behavior with an evals layer. The loop is closed.

Current Direction

Training the harness

The two systems were built independently. Personal OS Studio now joins them — the full feedback loop, compile → measure → refine, is complete and in daily use.

AI harness training loop: Personal OS Studio compiles Agent OS definitions 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 Studio 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.

Personal OS Studio is the connector, shipped as a native macOS app with its full roadmap complete. A pluggable adapter framework compiles canonical definitions into the exact files each harness reads — OpenClaw workspaces, Hermes instruction files, Codex's repo-scoped AGENTS.md, Claude Cowork paste blocks — with data designations (PII / Enterprise / Public) enforced at every destination. The definitions are authored by agent interview, validated against their schema checklists, versioned with diff review, and protected by an encrypted vault for the PII that deliberately never enters git. And the return arc is live: harness drift flows back as human-reviewed canonical proposals, while an evals layer — cases generated from the spec itself, deterministic assertions before an LLM judge, regression history, failures feeding refine interviews — turns the written specification into measured, reliable behavior. The loop in the diagram above is no longer aspiration; it's the product.

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