trajectory-control

Long-horizon agent sessions require a control plane that tracks objective status and score history through a ledger-based data structure. This monitors progress against defined budgets to prevent drifting off-course or stalling in unbounded repair loops.

Curve-based steering manages agent trajectories by analyzing time-ordered progress curves to detect drift, stall, or detour overrun signals. This enables evidence-based intervention through a regime-gated steering ladder rather than relying on isolated performance snapshots.

You declare objectives by defining clear goals with associated plans, budgets, and witnessed scores to maintain alignment. This creates a ledger-based data structure that tracks objective status and score history for deterministic session governance.

Yes, a regime-gated steering ladder applies the doctrine of non-interference for healthy sessions while providing structured nudging, warning, or suspending actions for stalled runs. Intervention is gated by evidence from curve-based drift and stall signal detection.

Snapshot-based monitoring misses behavioral drift and stall patterns that emerge over time. Trajectory control overcomes this limitation by using a ledger to track score history and witness rungs, analyzing time-ordered curves to detect detour overrun and unbounded repair loops.

Agents get trapped in unbounded repair loops when lacking a structured control plane for monitoring progress. Implementing ledger-based session governance with objective declarations, budgets, and curve-based steering prevents these loops by detecting stall and drift signals early.