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Trade like a fund. Bank like the future.

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Docs/AI Trading Agents/Agent Lifecycle and Fleet Operations

Agent Lifecycle and Fleet Operations

Production Track

Production operation requires explicit agent state transitions, health tracking, and intervention controls.

Objective

Operate multi-agent systems reliably with predictable rollout, rollback, and incident handling.

System Context

AI Trading Agents

LLM-assisted decisioning with deterministic execution controls

View full system documentation

Lifecycle States

Typical states include created, warming, active, paused, draining, and terminated.

State transitions are policy-driven and should produce auditable transition events.

Fleet Governance

Fleet-level controls enforce aggregate exposure, strategy diversity constraints, and coordinated pause behavior.

Role-based controls separate strategy authorship from execution approval authority.

Observability

KPIs include P&L, hit ratio, drawdown, rejected action count, and safety-trigger frequency.

Operational dashboards should support per-agent drilldown and fleet-wide anomaly views.

Production Checkpoints

  • ✓Lifecycle transitions are explicit and logged
  • ✓Fleet exposure limits enforce globally
  • ✓Agent health metrics are real-time
  • ✓Rollback paths are rehearsed

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AI Trading Agents

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