Architecting a Three-Layer Agentic Operating System: Implementing High-Efficiency Model Routing with Jev and Structured Memory via Obsidian
The current paradigm of AI interaction is shifting from simple prompt-response interfaces to the development of "Agentic Operating Systems" (Agentic OS). While many focus on the aesthetic appeal of sophisticated dashboards, the true value of an Agentic OS lies in its architectural ability to orchestrates disparate models, manage long-term memory through structured data hierarchies, and codify human workflows into autonomous skill backbones.
This post explores a technical framework for building such a system, utilizing Jev as a high-speed model router, Obsidian as a structured memory layer, and Claude Code/Codex as the execution engine.
The Three-Layer Architecture
A functional Agentic OS is not a single application but a tripartite architecture consisting of a Visual Layer, a Memory System, and a Skill Architecture.
1. The Visual Layer: Orchestration and Interface
The visual layer serves as the interface for human-agent interaction. This can be implemented as a standalone web application or an Obsidian plugin. The primary objective is not merely "UI" but the consolidation of disparate metrics—social media analytics, calendar events, and automation outputs—into a single, actionable dashboard.
Crucially, this layer must support execution through both Claude Code and Codified Codex. By porting the interface into an Obsidian plugin, developers can leverage the desktop environment's native capabilities while maintaining access to a terminal-integrated workflow. This allows for the real-time monitoring of automations and the manual triggering of customized skills.
2. The Memory System: Beyond RAG to Structured Hierarchies
A common misconception in AI development is that an "Agentic Second Brain" relies solely on Retrieval-Augmented Generation (RAG). While RAG is a component, the true efficacy of a memory layer depends on a deterministic file and folder structure that facilitates both human navigation and LLM context retrieval.
Inspired by Andrej Karpathy’s approach to organized knowledge management, this system utilizes an Obsidian Vault structured into three distinct sub-directories:
/raw: The landing zone for unstructured data, web scrapes, and unprocessed research./wiki: The synthesis layer where raw data is transformed into structured, Wikipedia-style Markdown articles. This serves as the "ground truth" for the agent./outputs: The destination for finalized deliverables, such as generated slide decks, reports, or codebases.
By maintaining this hierarchy, we provide the LLM with a clear semantic map. When an agent queries the vault, it does not need to traverse millions of unindexed files; it follows a predictable path from raw input to synthesized knowledge to final output.
3. The Skill Architecture: Codifying Human Workflows
The "Skill Backbone" is the most critical component for achieving true autonomy. The goal is to systematically transition repetitive daily tasks into Skills (discrete, repeatable prompts/functions) and eventually into Automations (scheduled or event-driven executions).
Developing this backbone does not require manual documentation. Instead, we can leverage existing interaction logs from Claude Code or Codex. By instructing the model to analyze its own conversation history over a 30-day period, the agent can identify recurring patterns and propose new skills. Furthermore, using stream-of-consciousness audio input—transcribed via Whisper—allows for the extraction of complex workflow requirements that are often too cumbersome to write manually.
The Engine: Intelligent Model Routing with Jev
The operational efficiency of an Agentic OS is determined by its routing logic. Running every request through a high-parameter model like Claude 3.5 Sonnet or GPT-4o is computationally expensive and introduces unnecessary latency. To solve this, we implement Jev as a specialized model router/classifier.
Unlike standard LLMs that generate conversational text, Jev excels at processing "fuzzy" queries to return specific probabilities and classifications. This allows us to implement a three-tier routing architecture:
| Tier | Request Complexity | Model Assignment | Latency/Cost Profile |
|---|---|---|---|
| Tier 1 | Simple commands (e.g., "Open terminal") | Jev (Direct Classification) | < 100ms / Near-zero cost |
| Tier 2 | Non-complex reasoning/summarization | Small Models (Haiku or Luna) | Low latency / High efficiency |
| Tier 3 | Complex, multi-step agentic tasks | Claude Code / Codex | Higher latency / Full capability |
By using Jev as a classifier, we can route Tier 1 requests without ever invoking an LLM, achieving near-instantaneous response times. For Tier 2, the system utilizes lightweight models like Haiku (in Astra versions) or Luna, ensuring that simple information retrieval does not consume high-value compute resources. Only when a task requires deep reasoning or terminal interaction is the request escalated to the heavy-duty execution engines of Claude Code or Codex.
Edge Implementation: Local Voice and Transcription
To achieve a seamless, "Jarvis-like" experience, the system integrates local, open-source models for audio processing. This ensures privacy and reduces reliance on external APIs for basic sensory input/output.
- ASR (Automatic Speech Recognition): We utilize OpenAI's Whisper running locally to act as the "ears" of the OS, transcribing user voice commands into text with high fidelity.
- TTS (Text-to-Speech): For the output layer, we implement Kokoro, an open-source TTS model, providing a naturalistic, low-latency vocal response that can be customized to any persona.
Conclusion: Scaling the Agentic OS
The ultimate utility of this architecture extends beyond personal productivity. Because the visual layer is decoupled from the execution logic, the system can be packaged as a service for teams or clients. By exposing a simplified dashboard—where complex terminal-based skills are reduced to single-button interactions—we allow non-technical users to harness the full power of an advanced Agentic OS without needing to interface with the underlying CLI or codebase.