Jarvis — Deterministic Multi‑Agent Operating System (MCP‑Native)

Jarvis is a deterministic, multi‑agent operating environment built on the Model Context Protocol (MCP).
It orchestrates:

• Curator OS
• Trading Agent OS
• Red Team
• Knowledge Bases
• Environment Scans
• Multi‑agent workflows
• Cloud inference (Together AI: Qwen → Llama → DeepSeek)
• Local tools, resources, and apps

Jarvis is designed as a unified intelligence platform for analysis, planning, regeneration, and adversarial QA — all routed through a single MCP server.

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Architecture Overview

Jarvis consists of four layers:

1. Foundation Layer — MCP Server

The MCP server is the router and contract surface for the entire system.

It provides:

• tool registry
• resource registry
• prompt registry
• task execution
• STDIO + HTTP/SSE transports
• deterministic sampling parameters
• logging + tracing
• configuration management

All agents, KBs, and OS modules plug into this server.

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2. Content Layer — Knowledge Bases

All KBs live in GitHub and are exposed as MCP resources:

• platform-kb/ — IBKR Platform KB
• trading-kb/ — Trading KB
• environment-scan/ — ENV_SCAN definitions
• voice-model/ — voice templates

These KBs are regenerable, diffable, and version‑controlled.

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3. Intelligence Layer — OS Modules

Each OS module is implemented as a suite of MCP tools:

• Curator OS — formatting, regeneration, patching, spec maintenance
• Trading Agent OS — environment scans, sector scans, catalyst detection, playbooks
• Red Team — adversarial prompting, drift detection, contradiction detection
• KB Ingestion — deterministic ingestion + regeneration

All reasoning flows through the cloud inference tool.

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4. Coordination Layer — Multi‑Agent Runtime

Jarvis includes a multi‑agent runtime with:

• agent registry
• agent roles
• agent handoff
• arbitration
• agent memory
• agent logs
• agent‑to‑agent messaging

Agents coordinate through the MCP server and share a unified reasoning engine.

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Cloud Inference (Together AI)

Jarvis uses a three‑model stack hosted on Together AI:

Primary Model — Qwen3‑Next‑80B‑A3B‑Instruct

• deterministic reasoning
• schema fidelity
• long‑context
• cost‑efficient
• ideal for ENV_SCAN → PLAN → EXECUTE pipelines

Fallback Model — Llama 70B Instruct

• stable
• predictable
• widely supported

Red‑Team Model — DeepSeek 70B/100B

• adversarial reasoning
• hypothesis challenge
• code refactoring
• creative ideation

All calls pass through: