AI control plane
Spend deliberately. Let LLM-authored position safeguards watch continuously.
Everyone may inspect the active policy. Editing models, schedules, or token budgets requires a recent Google authorization from an allowlisted administrator and creates an immutable revision for the local runtime to apply.
LLM workloads
Models and schedules
Each workload has its own model, reasoning level, schedule, timeout, and daily limits.
Whole-day cost circuit breaker
Estimated USD guardrail
Provider-reported input, cached-input, cache-write, output, and web-search usage is priced by the local core. A complete eight-agent cycle is admitted atomically: all eight calls run, or none do. The standard policy warns at $5 and refuses a cycle projected to cross $7.
Always-on local automation
LLM position safeguards
These fast jobs keep data and position rules current between deliberate LLM cycles. They are shown here for clarity and are not controlled by the model budget.
- Position and exit rules
- Every minute
- Market/news feed refresh
- Every 10 minutes
- Order reconciliation
- Continuous / event-driven
Protected policy
Immutable safety boundary
- Execution mode
- paper
- Live routing
- disabled
- Paper-v3 agents
- 8
No dashboard revision can weaken these values.
Audit trail
Configuration revisions
| Applied | Revision | Changed by | Reason | Status |
|---|---|---|---|---|
| No applied configuration revisions yet. | ||||