How to take an AI trading agent live with real money

Taking an AI trading agent live means replacing simulated fills with real capital at risk. The transition is not a single toggle but a deliberate sequence of technical and procedural steps. Each step exists to ensure the agent behaves exactly as tested while operating under hard financial limits that the owner controls. Rushing this sequence increases the chance that an untested edge case becomes a live loss.

What changes when an agent trades real money?

Paper trading validates logic, connectivity, and prompt stability without exposing capital. Live trading introduces execution risk, slippage, fees, partial fills, and market impact. An agent that appears profitable in simulation may generate losses when real order books move against it, or when latency causes entries and exits to differ from expected prices. The owner must expect that the first live trades will differ from paper results, even if the strategy logic is identical.

Felix provides paper trading for all five market types so developers can observe behavior across stocks, crypto, perps, options, and prediction markets before any authorization occurs. Paper environments mirror the live data feeds and order placement flows, but settlement happens in a simulated ledger. This gap between simulation and reality is where most early failures occur. The agent might handle a rejected order gracefully in paper, but in live trading a rejected order can cascade into a missed hedge or an unintended delta exposure.

The psychological shift matters too. Once real money is involved, every anomalous log line, delayed fill, or unexpected position warrants immediate attention. The agent does not know the difference between paper and live, but the owner does. This asymmetry means the monitoring burden falls entirely on the human operator until the system has proven stable over multiple sessions and market conditions. The owner should schedule dedicated observation time for the first live trades rather than treating the launch as a passive event.

How much paper testing is enough?

Paper testing should cover far more than happy paths. The agent needs to encounter rejected orders, rate limits, empty order book states, rapid price moves, and API errors. If the agent uses a multi-step strategy, each leg should execute in paper first. The goal is not to prove the strategy will profit, but to prove the agent will not panic, loop, duplicate orders, or bypass limits when conditions degrade. A strategy that is theoretically sound but technically fragile will fail faster in live trading than a simple strategy that is robust.

Use paper trading to validate the full stack. This includes the MCP tool or REST client, the prompt instructions, the safety middleware, and the wallet connection. If the agent relies on external data or reasoning steps, verify that delays or malformed responses do not cause duplicate orders or stale decisions. Test what happens when the LLM returns an unexpected symbol, an incorrect side, or a size that exceeds the configured cap. The safety layer should catch these, but the owner should witness the catch in paper rather than discovering it during a live session.

Run the agent through at least one simulated drawdown scenario. If the strategy is directional, observe how it behaves when prices move against the position continuously. If it is market making or arbitrage, test how it handles one leg failing to fill. A practical checklist for prediction market trading with AI agents offers market-specific tests that generalize to other asset types. When the agent runs for several days without manual intervention and respects every cap, it is ready for the next phase.

What guardrails should be locked before authorization?

Authorization of a live key should only happen after guardrails are configured, documented, and verified. Felix enforces safety through scoped keys, budget caps, position limits, exit plans, and a panic kill switch. The owner sets these parameters; the agent cannot override them. Non-custodial architecture means funds remain in a wallet the owner controls, and the agent can spend within limits but can never withdraw to itself or an unapproved address. This separation of trading permissions from withdrawal permissions is built into the system, not added as a plugin.

Budget caps should reflect the maximum loss the owner is willing to accept during the first live session. It is prudent to set a daily or weekly cap that is small relative to the total wallet balance. Position limits prevent oversized concentration in a single asset or direction. Exit plans define how the agent should close or reduce exposure when drawdown thresholds hit, either through automated rules or through alerts that trigger manual review. The kill switch flattens positions and revokes access immediately, without requiring the agent to cooperate.

These controls are not optional extras. They are the foundation that makes live deployment responsible. How to evaluate guardrails for a trading agent step by step provides a framework for testing each control before real money is at stake. The owner should physically test the kill switch during paper trading. Press it, verify that orders cancel, positions flatten, and the key deactivates. If this process takes longer than expected, fix it before going live.

Sizing deserves special attention. Felix normalizes orders in plain US dollars, which removes venue-specific contract math and multiplier errors. This simplification helps, but the owner must still decide what dollar amount constitutes a safe test. A common approach is to reduce live size to a fraction of the paper size, then scale only after observing stable behavior. For example, if the agent traded one thousand dollars per position in paper, the owner might set a fifty dollar live size for the first session. The strategy does not need to prove itself at full size to prove that the infrastructure works.

How do you authorize a live key?

Live trading requires explicit owner authorization. The owner generates or scopes a key, binds it to a wallet the owner controls, and approves withdrawal addresses. Until this happens, the agent cannot place live orders even if the code is running and the prompt is eager to trade. This deliberate friction prevents accidental deployment and ensures the owner has consciously accepted the risk.

Agents connect through MCP tools in Claude, Cursor, or other MCP clients, or directly through the REST API. The connection method does not change the authorization requirement. The owner must still approve the key and confirm the safety parameters. For developers using the REST API, the exact request schema is in the docs; the shape looks like this:

curl -X POST https://api.felix.trade/v1/orders \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "market": "perp",
    "side": "buy",
    "size_usd": 50,
    "symbol": "EXAMPLE-PERP"
  }'

After authorization, start with a single market and a small budget. Multi-market agents add correlation risk, sizing complexity, and cognitive load for the owner. It is easier to debug behavior when only one venue and one asset class is active. Once the agent proves stable, the owner can expand to additional markets under the same key or create separate scoped keys for each market type. Separating keys by market also limits blast radius if one venue experiences an outage or anomalous behavior.

How do you monitor after going live?

The moment the agent goes live, observability becomes the primary tool. The owner should review fills, position states, and log entries in real time during the first sessions. Felix provides unified audit logs across all five market types through one API. This means a single stream reveals what the agent decided, what it sent, what the venue returned, and what filled, regardless of whether it is trading stocks or prediction markets. The owner does not need to log into five separate interfaces to reconstruct a trade.

Do not assume silence means health. An agent that stops trading may be stuck on a failed precondition, or it may have hit a cap and halted correctly. Both states look similar from a profit and loss perspective, but they demand different responses. Structured logs show the difference by revealing whether the last action was a rejected order, a cap block, or a successful exit. The owner should know how to read these states before launching.

Maintain a human oversight loop. The agent may run autonomously, but the owner should define check-in intervals. During each check, verify that budget caps are intact, positions match expectations, and the kill switch is responsive. If the live behavior contradicts the paper results in any way, pause and investigate before continuing. How to evaluate audit logs and observability for trading agents through one API explains what to look for during the first live hours and how to spot drift before it compounds.

Track fill quality explicitly. Compare live fill prices to the paper benchmark prices. If slippage is consistently wider than expected, the sizing may be too large for the available liquidity, or the order type may be inappropriate. Small size reduces this risk and gives the owner a realistic baseline for scaling up.

What should trigger an immediate shutdown?

Certain conditions warrant flattening positions and revoking the live key without hesitation. These include breaching a budget cap, generating orders outside the tested strategy, encountering repeated API errors that could lead to phantom or duplicate orders, or observing market discontinuities such as delistings, halted trading, or extreme volatility. The kill switch exists for this purpose. It is not a failure to use it; it is the intended control and the final backstop.

Prompt design plays a role in preventing the need for emergency shutdowns. A well-constructed prompt includes hard limits that align with the technical guardrails. If the agent is instructed to trade within specific hours, dollar ranges, or directional biases, and the prompt contradicts the API caps, confusion can occur. The agent might attempt to trade a size that the prompt deems standard but the API rejects as over limit, leading to loops or errors. How to write prompts that keep trading agents inside hard limits describes how to keep instructions consistent with infrastructure constraints.

Trading can lose money, including everything. No guardrail, prompt, or safety layer eliminates risk. The goal is to ensure that when losses occur, they are contained, expected, and survivable. An agent that loses its test budget and halts is acting within design parameters. One that loses multiples of its budget because controls were bypassed or sizing was optimistic is not. The owner should treat the first live month as a paid systems test rather than a profit target.