AI Trading Agent Failure Mode Checklist

Paper-first boundary

Trading Boy does not execute live trades, hold funds, or provide financial advice. This page is for simulated paper-trading review, prompt design, journal structure, and human review. It is not a signal feed, broker instruction, or promise of live trading results.

When to use this page

Use it after a sample review when the agent output looks confident but the evidence may be incomplete.

The search intent behind how to write an AI trading agent failure mode checklist is usually practical: the user wants a reusable asset, an example, and a checklist. This page treats those variants as one owner page rather than splitting guide, example, checklist, and template into thin sibling pages.

Use the asset only after the paper-trading workflow, prompt version, permission boundary, and journal output format are clear. If the user cannot identify the setup, invalidation, risk field, or review question, the next action should be a skip, caveat, or human review rather than a confident simulated entry.

Failure mode checklist

Review field	Pass condition	Paper-mode boundary
Prompt drift	Checks whether instructions changed inside the sample.	Untracked prompt changes create a caveated sample.
Missing invalidation	Flags entries without a clear wrong condition.	No invalidation means the agent should have skipped.
Risk override	Flags size increases, exposure stacking, or pause-rule violations.	Confidence cannot override paper risk.
Data caveat	Flags stale, missing, or inconsistent market context.	Stale data creates a skip or excluded row.
Review gap	Flags missing human signoff or unclear next action.	The sample cannot drive a rule change.

Reusable prompt or worksheet text

Role: You are helping organize a simulated paper-trading review. You may summarize context, apply written rules, identify missing fields, and prepare journal evidence. You may not route live trades, request secrets, or provide financial advice.

Required output: Decision type, setup name, rule version, thesis, invalidation, paper risk, data caveat, behavior tag, and one human review question.

Skip rule: If setup, invalidation, paper risk, or data quality is missing, produce a skip or excluded row. Do not invent the missing field to make the record look complete.

Review handoff: Send the output to the paper journal, evaluation checklist, or human review checklist. Choose one next paper-mode action: collect more samples, tighten one field, pause, or revert a prompt change.

Example paper workflow

Scenario: The agent makes three paper entries in one session. The failure checklist finds that two entries used stale data and one changed the watchlist rule without versioning. The reviewer marks the sample caveated and returns to prompt versioning.

Good output: The checklist turns vague concern into named failure modes and one next action.

Weak output: The review says the agent felt inconsistent but does not name the failure.

Decision: The reviewer keeps the evidence in paper mode, checks the output with the AI paper trading agent evaluation checklist, and records any prompt change in AI trading agent prompt versioning.

Use it with these controls

Prompt version: every sample should name the version that produced the output.
Output format: the agent should use consistent fields for entries, exits, skips, and missed setups.
Risk gate: size, exposure, drawdown, stop distance, and pause rules should be checked before judging the idea.
Human review: a person should approve prompt, risk, or workflow changes before the next sample.
Data privacy: private exchange credentials, account identifiers, and secret values should stay out of prompts and screenshots.

How it supports ranking

This owner page consolidates the guide, example, template, checklist, and for-paper-trading variants into one durable page. That gives searchers a complete answer without flooding the sitemap with near duplicates.

It also links into the surrounding Trading Boy system: paper-trading hub, AI paper agent, prompt template, output format, risk controls, permission boundaries, and paper-trading limitations.

Related AI paper-agent pages

Use these links to move between setup, output, risk, journal, and review pages without leaving the paper-first cluster.

AI Trading Agent Failure Mode Checklist FAQ

What are common AI trading agent failure modes?

Common paper-agent failure modes include prompt drift, missing invalidation, risk overrides, stale data, hindsight edits, and weak review actions.

Should a failure mode page be used only after losses?

No. Winning paper trades can still reveal broken process, missing fields, or unversioned changes.

What happens after a failure mode is found?

Pick one conservative paper action: pause, collect cleaner samples, tighten a field, or revert a prompt change.