How to decide between a trading bot and a trading agent when building

A trading bot and a trading agent both automate orders, but they differ fundamentally in how they make decisions and how much human judgment they replace. A bot executes deterministic rules that you write in code, while an agent uses a large language model to interpret context and adapt its plan when the environment changes. If your strategy can be written as a precise set of if-then statements with no ambiguity, a bot is usually simpler, cheaper, and easier to debug. If your strategy requires reasoning over unstructured information or shifting goals, an agent is the better fit, though it demands stronger guardrails and more observability.

What is a trading bot?

A trading bot is a deterministic program that follows rules you specify in advance. It reads market data, computes signals, and places orders according to a fixed decision tree. Because every branch is written in code, the bot’s behavior is reproducible. If you replay the same price series, you get the same actions, which makes backtesting straightforward and debugging a matter of tracing logic errors. Most bots run in a tight loop: fetch prices from a websocket or REST feed, evaluate technical indicators or statistical thresholds, size the order based on fixed logic, and send it to a stock broker, a perps venue, or an options venue. You are responsible for handling each venue’s contract specifications, margin math, tick sizes, and error responses in your own code. The infrastructure is usually monolithic, with the data feed handler, signal engine, risk module, and execution client tightly coupled inside one codebase. Safety is entirely your responsibility. A typo in a threshold, an off-by-one error in a time window, or a missing null check can send orders you never intended. There is no external layer to enforce a budget cap, flatten positions, or block a blacklisted market unless you build it yourself. Bots excel when speed and repeatability matter more than interpretation. They do not hallucinate, they do not drift from their instructions, and they do not require prompt engineering. They simply do exactly what you wrote, including the bugs.

What is a trading agent?

A trading agent is an autonomous system that uses an LLM to reason about market conditions and decide what to do. Instead of encoding every rule in code, you describe the strategy, constraints, and goals in a system prompt. The agent observes the world through tools, such as MCP servers that read market data, portfolio state, or even news summaries, and then generates a plan. It can adapt to new information that was not explicitly foreseen in the original prompt, which is both its strength and its risk. Because the LLM is non-deterministic, the same scenario may produce different actions on different runs, especially if the context window changes or the model is updated. Because of that, you must surround the agent with hard guardrails that sit outside the model itself. The Felix API normalizes order sizing in plain US dollars across stocks, crypto, perps, options, and prediction markets, so the agent does not need to learn venue-specific contract math, tick sizes, or margin formulas. Funds remain in a wallet you control, and the agent operates within scoped limits it cannot override. The architecture is decoupled by design, with the LLM, tool layer, and execution API separated so you can change the model or the prompt without rewriting market connectivity. You will spend more effort on observability, prompt design, and audit trails than on traditional unit tests.

How do the architectures differ?

A bot is typically a single application that runs on your server, desktop, or container. It opens a websocket to a data provider, runs a calculation inside an event loop, and fires an HTTP request to a trading venue. You manage the state, the retry logic, the order book parsing, the position tracking, and the heartbeat monitoring. If you want to trade across multiple markets, you must integrate each venue’s API independently and handle the idiosyncrasies of each one, such as different rate limits, authentication schemes, and order types. An agent, by contrast, delegates execution and normalization to a standardized layer. The agent’s primary job is reasoning, not plumbing. It receives context through tools, thinks via an LLM, and outputs an action such as buy, sell, rebalance, or hold. That action is then checked against safety rules before it ever reaches a market. The Felix API translates the agent’s intent into venue-specific instructions, so a single prompt can result in trades across a stock broker and a prediction market without the agent knowing the underlying contract details. This separation means you can change the LLM provider or rewrite the strategy prompt without touching execution code. It also means the agent relies on external guardrails for safety, whereas the bot relies on internal checks. The bot is tightly coupled to its venues; the agent is loosely coupled to its tools.

How do safety and control differ?

With a bot, safety is code. You write the position limits, the stop losses, the daily loss checks, and the market whitelists. If the bot is compromised or buggy, it can do anything the API key permits. If that key has withdrawal rights, a bug could move funds. You must implement key rotation, IP restrictions, and rate limiting yourself, and audit your own codebase to ensure the checks are correct. With an agent built on Felix, safety is structural and non-custodial. The agent receives a scoped key that can place orders within a budget, but it can never withdraw funds to itself or an unapproved address. Owner-approved withdrawal addresses are enforced by the infrastructure, not by the agent’s prompt. A panic switch can flatten all positions and revoke access instantly. You should configure dollar-based order sizing to keep the agent’s notional amounts explicit and human readable. You should also set up audit logs and observability because an LLM’s reasoning must be recorded for later review; you cannot simply step through a stack trace. A kill switch is mandatory before going live, and prompt design within bounds keeps the model from inventing trades outside its mandate. The bot offers total control until it breaks; the agent offers bounded autonomy that is easier to contain.

When should you use a bot instead of an agent?

Use a bot when your strategy is fully specifiable and you can enumerate every edge case in code. If you are running high-frequency scalping, statistical arbitrage between two correlated assets, or a grid system that relies on microsecond consistency, deterministic logic is essential. Bots are also appropriate when latency is critical because they avoid the overhead of an LLM inference step and the round-trip context building that agents require. You can backtest a bot with confidence that the historical results will closely mirror live behavior, assuming your slippage and fill assumptions are accurate. A bot is easier to debug because you can step through the code, inspect variables, and reproduce the exact state that led to a trade. It does not hallucinate, misinterpret a prompt, or introduce novel reasoning that was not in the source code. If your trading style is purely quantitative, requires no natural language reasoning, and involves no portfolio narrative or cross-market discretion, a bot will be simpler to maintain, cheaper to run, and more predictable. The main risk is the code itself, which means thorough testing and static analysis are your best defenses.

When should you use an agent instead of a bot?

Use an agent when your strategy involves judgment that is hard to encode as rigid rules. Suppose you want to reduce exposure to technology stocks if earnings sentiment turns negative, while simultaneously increasing hedges through an options venue and a prediction market. Writing explicit if-then logic for every permutation of news, sentiment, and correlation is impractical. An agent can absorb this context, reason about the portfolio as a whole, and propose a coordinated set of trades. It is also the better choice when you want to describe a strategy in plain language and iterate quickly without redeploying code. For example, you might tell the agent to maintain a risk parity allocation across crypto and stocks, but to cap any single position at five percent of capital and avoid leverage greater than two times. The agent interprets the intent and adjusts as markets move. Just remember that trading can lose money, including everything, and non-deterministic reasoning does not eliminate risk. The agent changes the shape of the risk from code bugs to model misinterpretation, which is why hard limits and continuous monitoring are essential.

How do you migrate from a bot to an agent?

Migration should be gradual. Keep the existing bot running as a baseline so you have a reference for performance and behavior. Next, build the agent in paper trading mode. Extract the data pipeline and execution layer from the bot, but replace the rule engine with an LLM prompt that describes the strategy in natural language. Define hard limits immediately: maximum position size, allowed markets, daily budget, and maximum drawdown before halting. Add dollar-based order sizing so the agent thinks in dollars rather than contracts, which prevents confusion about lot sizes or margin multipliers. Implement audit logs and observability from day one so you can compare the agent’s reasoning against the bot’s raw signals. Wire in a kill switch and prompt guardrails before any live key is authorized. Run the agent in parallel with the bot for several weeks. Look for overtrading, drift from the intended strategy, or unexpected interactions between tools. Tune the prompt and tool descriptions until the agent’s decisions align with your intent. Only then should you authorize a live scoped key and allocate real capital. The exact request schema is in the docs at /docs; the shape looks like this.

curl -X POST "$FELIX_API_BASE/orders" \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "market": "BTC-USD",
    "side": "buy",
    "dollar_notional": 500,
    "order_type": "limit"
  }'

After you go live, continue to compare the agent’s fills against the bot’s hypothetical outputs. If the agent consistently deviates in ways that increase cost, slippage, or risk, tighten the prompt or reduce the budget. Over time, you may retire the bot entirely, or you may keep it as a specialized execution layer for high-speed tasks while the agent handles allocation and discretion. The goal is not to replace one with the other blindly, but to assign each system the work it does best. A hybrid setup is often the most robust architecture for serious developers.