AI agents · 13 min read
LLM Trading Agents: Using Language Models to Trade on Monad
How large language models can plan, reason, and call tools in a Monad trading agent, plus the failure modes you must design around.
From chatbot to agent
A language model becomes an agent when it can call tools: fetch a price, read a pool, simulate a swap, or request a signature. The model plans a sequence of tool calls to achieve a goal you describe, such as keeping a portfolio at 60% stablecoins.
Strengths
LLMs excel at translating intent into parameters, summarizing news and governance proposals, explaining their reasoning, and handling unusual situations a rigid script would ignore. They make agents configurable by people who do not write code.
Failure modes
Models can hallucinate token addresses, misread numbers, or be manipulated by prompt injection hidden in data they read — a token name or website text instructing them to send funds. They are also non-deterministic: the same input can produce different outputs.
Treat all external text as untrusted data. Never let a model choose a destination address freely; constrain it to a whitelist.
A safe architecture
Use the model for planning and explanation. Pass its proposed action as structured data to a deterministic validator that checks limits, whitelists, and simulation results. Only the validator can trigger signing. Log the model's reasoning beside every transaction.
Cost and speed
Large models are slow and costly per call. Use them for slow decisions — daily allocation, risk regime — and fast small models or rules for tick-level reactions.
Try it
AI agents on Monad are an active frontier. Follow what monaf.ai is building to see language-model trading tools in practice.
Monad.Cash is an independent educational site, not financial advice. Check official Monad documentation for current details.