Core Concepts

Agent

An Orien agent is a trading worker running in a Kubernetes pod. It is not just a model session. It is an execution unit that binds strategy logic, risk controls, exchange access, and reporting into one runtime.

• Each agent is bound to one Skill
• Each agent trades one or more pairs such as BTC/USDT
• Each agent runs in repeated cycles
• Each agent reports actions, trades, positions, and reasoning traces back to the platform

Agent Lifecycle

created → deploying → running ⇄ paused → stopped → removed

Trading Modes

Agent Types

Pipeline Agent

The flagship agent type and the most important product layer.

A pipeline agent is an Orien-native agent that runs a structured trading pipeline on its own.

Strengths

• highest automation level
• deterministic orchestration
• integrated risk and monitoring
• best fit for repeatable strategy execution

Tradeoffs

• requires stronger runtime and strategy design
• more operational structure than simple chat workflows

Integrated Agent

An integrated agent is a third-party agent framework, such as OpenClaw or Hermes, that is hosted and operated inside Orien.

Strengths

• useful when you want a familiar external agent framework inside the Orien platform
• can still reuse Skills and platform execution primitives

Tradeoffs

• less native to the Orien pipeline model
• more framework-dependent than pipeline agents

AI Chat

AI Chat is the natural-language interface layer.

Strengths

• easiest way to access analysis and tools
• good fit for interactive operator workflows
• natural home for the MCP ecosystem

Tradeoffs

• not the ideal final home for fully autonomous execution
• should stay as a thin interface over the shared trading core

Skill

A Skill is a packaged strategy module used by agents. It defines how an agent observes the market, plans actions, evaluates risk, and executes orders.

• SKILL.md — Metadata and human-readable documentation
• orien.runtime.yaml — Runtime contract and tool mapping
• scripts/ — Strategy implementation files

Skill Types

MCP

MCP is the tool ecosystem for AI Chat.

It provides:

• reusable tool servers
• installable tool capabilities
• a marketplace and management model for chat-side extension

If Skills are strategy modules, MCP servers are tool modules.

Skill Phases

Each trading cycle moves through four phases:

1. Observe — Gather prices, K-lines, order book data, indicators, and account context
2. Plan — Convert observations into candidate actions
3. Risk — Validate the candidate action against platform and strategy limits
4. Execute — Submit the approved order flow

Cycle

One cycle is one complete decision loop. A cycle typically produces:

• Action — LONG, SHORT, CLOSE, or HOLD
• Signal strength — Strategy confidence or action weight
• Trace — Structured reasoning across observation, planning, risk, and execution

Trading Concepts

The current platform centers around practical trading workflows:

• Backtesting — replay strategy logic on historical data
• Factor combinations — combine multiple analytical signals into a strategy model
• Risk-gated execution — validate before order submission
• Monitoring — inspect decisions, trades, positions, and outcomes

Architecture

The product is organized around 3 Layers, 2 Ecosystems, and 1 Core:

1. Three Layers — Operator Interfaces, Strategy Engine, and Trading OS
2. Two Ecosystems — MCP for AI Chat, and Skill for pipeline agents and integrated agents
3. One Core — Agent Trading

This structure keeps the execution core reusable while allowing different operator experiences and extension models to evolve on top of the same system.

Risk Controls

Risk management is treated as a first-class product primitive. Controls include:

[!WARNING] CLOSE actions always bypass risk controls so agents can always exit positions to limit losses.

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