API integration
AI Agent Discipline — Guardrail API integration guide
How to integrate SEIGYO's Guardrail API for ai agent discipline with pre-trade evaluation, behavioral detection, and enforcement.
Who this page is for
Teams building LLM-powered trading agents (Claude, GPT, custom models) who need behavioral guardrails between the model and the market.
Core problem
AI agents can reason about strategy but cannot feel risk. Without external guardrails, an agent will keep trading through tilt conditions that any human would recognize.
Why this matters
Why this page exists
The page should answer the exact query before asking the user to convert.
Every trade should be evaluated against discipline rules before execution, not after the damage is done.
A stateless API means your existing infrastructure does not need to change — just add one HTTP call to the order flow.
Block, warn, or approve in under 50ms so the guardrail does not slow down execution.
What to do first
Start with the smallest useful workflow
A specific first step keeps the page practical and reduces decision fatigue.
Get an API key from the SEIGYO dashboard (Settings → API Keys).
Send a POST to /v1/guardrail/evaluate with the trade, rules, and session state.
Check the verdict: approved, warned, or blocked. Act accordingly.
What to measure
Look for signals that change behavior
Useful review starts with a small number of repeatable measurements.
blocked trades per session, agent compliance rate, and behavioral pattern alerts
API response latency and uptime
Ratio of blocked trades to total evaluations
How it helps
Where SEIGYO fits
Move from the query into a workflow users can try with demo data, CSV history, or a setup path.
The API evaluates 10 rule types and detects 7 behavioral patterns in a single call.
Returns discipline score, budget remaining, and enforcement recommendations alongside the verdict.
Works for humans, bots, and AI agents — same endpoint, same rules, same enforcement.