How to Build a Multi-Agent Trading System: A Developer's Guide

A multi-agent trading system divides trading work across several specialized agents, such as research, signal generation, execution, and risk oversight, and coordinates them through shared state and a common execution layer. Each agent runs with its own scoped credentials and limits, so a bug or a bad decision in one component cannot take down the whole system. This guide walks through the design step by step: how to split responsibilities, how agents communicate, how to connect them to real markets, and how to keep the combined system inside a risk budget you set.

What is a multi-agent trading system?

The simplest way to let an AI trade is to give one model one prompt and one account: read the market, decide, place orders, repeat. That works for a single narrow strategy, but it couples every failure mode together. If the model misreads data, it also misplaces the order, and nothing independent is watching. If you have read our overview of what agentic trading is, a multi-agent system is the same idea applied with separation of duties: instead of one agent doing everything, you run several agents that each do one thing well and hand structured work to the next.

The pattern mirrors how engineering teams already structure services. A data agent ingests and normalizes information. A signal agent turns information into trade proposals. An allocator converts proposals into target positions. An execution agent turns targets into orders. A risk monitor watches all of it and can stop the system. Each piece is small enough to test on its own, simple enough to reason about, and replaceable without rewriting the rest.

This architecture is not free. You pay for it in coordination overhead, more infrastructure, and new failure modes at the seams between agents. It earns its keep when your strategy has genuinely separable concerns, when you trade more than one market, or when you want an independent component whose only job is to say no. If one agent with one strategy meets your needs, run that instead.

How should you divide labor between agents?

A division that works well in practice assigns each agent one of five roles. You can merge roles when starting out, but keep the boundaries conceptually clean, because the boundaries are where your controls live.

• ·Data agents collect prices, order book snapshots, news, or on-chain data, then publish normalized observations with timestamps. They never see credentials that can place orders.
• ·Signal agents read those observations and produce trade proposals: instrument, direction, suggested size in dollars, reasoning, and an expiry after which the proposal is void.
• ·An allocator turns approved proposals into target positions, given current holdings, per-agent budgets, and total exposure. It is the only component that decides how much.
• ·Execution agents convert target positions into orders and handle retries, partial fills, and venue responses. They never decide what to trade, only how to implement a decision.
• ·A risk monitor has read access to everything and write access to almost nothing. It checks proposals, positions, and limits, and it can trigger the kill switch.

The load-bearing principle here is the separation of proposal from permission. The agent that wants a trade should never be the agent that authorizes or sizes it, and no agent should be able to edit its own limits. Suppose a signal agent hallucinates a pattern and proposes buying an enormous position. If sizing lives in a separate allocator with a hard budget, the hallucination dies at the boundary instead of reaching the market.

How should agents share state and communicate?

Most multi-agent trading failures are coordination failures, not reasoning failures: two agents acting on stale state, a retried request placing the same order twice, or a proposal executed after the conditions it depended on have changed. Design the plumbing before tuning any prompts.

A structure that holds up well is an append-only event log plus a single portfolio state store. Every proposal, approval, order, and fill is an event with a unique ID and a timestamp, written to the log. The allocator owns the portfolio store and is the only writer to it. The risk monitor subscribes to the same event stream as everyone else, so it sees what the system sees in the same order. An orchestrator process can route events between agents, or agents can publish and subscribe directly; the orchestrator version is easier to audit, the peer version has fewer single points of failure.

• ·Give every proposal a unique ID and an expiry. Execution agents reject anything expired or already seen.
• ·Make order placement idempotent. If an execution agent retries after a timeout, the execution layer must recognize the duplicate and return the original result instead of placing a second order.
• ·Allow exactly one writer per resource. One allocator owns portfolio state; each execution agent owns its assigned markets.
• ·Use structured schemas for anything that moves money. Natural language is fine for reasoning between agents, but proposals and orders should be typed fields that validate.
• ·Timestamp everything and check for staleness. A proposal based on a price from ten minutes ago is a different trade than its author intended.

Keep a human-readable trace of agent reasoning alongside the structured events. When something goes wrong at 3 a.m., you will want to reconstruct not just what the system did but what each agent believed at the time.

How do you connect agents to real markets?

The execution layer is where multi-agent designs usually accumulate glue code: one client per venue, each with its own authentication, contract sizes, tick rules, and order types. A unified trading API removes most of that. Felix exposes one API across every market, covering stocks, crypto, perpetual futures, options, and prediction markets, with orders sized in plain US dollars while the API normalizes venue-specific contract math. For a multi-agent system this means your execution agents share one order format, and adding a new market type is a configuration change rather than a new integration.

Issue each agent its own scoped key rather than sharing one key across the system. Per-agent keys give you attribution in the logs, per-agent budget caps and position limits enforced at the API layer, and the ability to revoke one agent without disturbing the rest. Agents can connect through MCP tools from clients like Claude or Cursor, as described in our guide to trading over MCP, or call the REST API directly, which is usually the better fit for automated services. The exact request schema is in the docs; this example shows the shape.

curl -X POST https://api.felix.trade/v1/orders \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: proposal-7f3a2c" \
  -d '{
    "market": "perps",
    "instrument": "BTC-PERP",
    "side": "buy",
    "size_usd": 250,
    "agent_id": "execution-perps-01",
    "proposal_id": "proposal-7f3a2c"
  }'

Paper trading runs through the same interface, so the exact code path your agents exercise in testing is the one that will later touch real money. Live trading requires the owner to explicitly authorize a key, which gives you a clean gate between testing and production: the system can prove itself on paper while physically incapable of routing a live order.

How do you keep a multi-agent system safe?

More agents means more credentials, more concurrent decisions, and more ways for exposure to add up in places you are not watching. Safety has to be enforced at the infrastructure layer, not requested in prompts. A prompt can be ignored, misunderstood, or derailed by a strange input; a budget cap enforced by the API cannot.

• ·Scoped keys per agent, each limited to the markets and actions that agent actually needs.
• ·Budget caps per agent, plus a global cap. The sum of per-agent limits should never exceed what you are willing to lose, because correlated positions across agents can lose simultaneously.
• ·Position limits and exit plans attached to every open position, so the system defines in advance how a trade unwinds.
• ·A panic or kill switch that flattens all positions and revokes access. Wire the risk monitor agent to it, and test that it works before you need it.
• ·Non-custodial custody by construction: funds sit in a wallet you control, agents can spend within limits but can never withdraw to themselves, and withdrawal addresses are owner-approved only.

The risk monitor deserves emphasis because it is the piece most teams build last and regret first. It should be a different model or a simpler deterministic service, running outside the reasoning loop of the trading agents, with read access to every event and position. Its job is to detect what the trading agents cannot: their own aggregate behavior. Our guide to running an AI trading agent safely covers the single-agent version of these controls; in a multi-agent system the same controls apply, multiplied by the number of keys you issue. None of this removes market risk. Trading can lose money, including everything you allocate, and no architecture changes that.

How do you go from paper to live trading?

Treat the path to live trading as a sequence of gates, each with a pass condition you write down in advance.

1. 01Test each agent in isolation against recorded data. Verify that the data agent timestamps correctly, the signal agent emits valid proposals, and the allocator respects budgets.
2. 02Run the full system on paper trading for long enough to see it behave across different market conditions, including quiet periods where the correct action is doing nothing.
3. 03Run in shadow mode: the system produces live proposals and orders, but a human approves each one before it routes. This catches reasoning errors that paper fills hide.
4. 04Authorize live keys with small caps. Real fills introduce slippage, partial fills, and rejected orders that paper trading approximates kindly.
5. 05Raise limits gradually as the system proves boring. Boring is the goal: long stretches where proposals are sensible, limits hold, and nothing surprises you.

Keep monitoring after go-live: log every event, alert on rejected orders and limit breaches, and review reasoning traces on a schedule rather than only after incidents. Imagine a signal agent that slowly drifts into proposing the same losing trade every hour. Per-trade limits contain it, but only log review will show you the pattern. Multi-agent systems tend to fail slowly before they fail fast, and the slow phase is where review pays for itself.