Autonomous trading agents operating on live exchanges with real capital lack built-in guardrails, circuit breakers, or continuous validation mechanisms to detect when simulated performance assumptions break down in production. Silent logic failures — stops that never fire, consensus conditions that silently fail — go undetected until catastrophic loss has accumulated. No shared infrastructure exists to monitor, gate, or halt agent trading behavior across operators.

Autonomous trading agents silently fail in production — missed stops, broken consensus logic, drifting assumptions — and no shared infrastructure exists to detect, gate, or halt them before catastrophic losses accumulate.

Crypto and equities teams running autonomous trading agents with real capital, from solo quant developers to small trading firms deploying 5-50 agents across exchanges.

Anyone running real capital through autonomous agents already knows the terror of silent failures; they'd pay immediately for a monitoring layer that catches what their agents can't catch themselves, similar to how traders already pay for risk management platforms like Riskalyze or portfolio margining tools.

MVP is a sidecar agent that connects via exchange APIs and agent logs, monitors for behavioral anomalies (position drift, stop-loss non-execution, P&L divergence from backtest), and can force-close positions or kill agent processes — ship as a Docker container with a dashboard in 4-6 weeks.

Algorithmic trading software market is ~$3B and growing rapidly; the agent-specific guardrails layer targets the fastest-growing segment where autonomous AI replaces rule-based algos, plausibly a $500M+ niche within 3 years.