AI TRADING · SAFETY AND SYSTEMS
Building a Safe AI Trading Agent
An architecture for observation, research, reasoning, risk checks, approval, execution, reconciliation, and audit.
Short answer. A safe AI trading agent is a bounded workflow, not an unrestricted model with a broker credential. Separate observation, research, reasoning, risk, approval, execution, reconciliation, and audit, and make the execution boundary deterministic.
Reference workflow
Observation → Research → Reasoning → Signal → Risk check → Human approval → Execution → Reconciliation → AuditAnalysis permission is not execution permission. Tools should declare their data scope, account scope, environment, allowed instruments, limits, expiry, and audit requirements.
Production controls
- Use scoped credentials and read-only defaults.
- Require deterministic validation before any order command.
- Enforce maximum order size, allowed venues, daily loss, and rate limits.
- Support dry-run, paper, demo, and testnet environments.
- Persist the prompt or decision revision, evidence, policy result, approval, and provider receipt.
- Fail closed on stale data, missing mappings, expired approvals, or unknown execution states.
Sources
Last verified: 2026-09-28. Provider capabilities can change by region, account, product, environment, and entitlement; verify the linked official documentation before production use.