What is Trajectory Refinement?
Trajectory refinement is a technique for improving AI agent performance by treating an agent's planned sequence of actions — its trajectory — as an optimizable object that can be iteratively corrected before and during execution, rather than executed once as-generated.
Why It Matters
AI agents frequently fail not because their model is weak, but because of a gap between what they plan and what actually happens when they execute. A plan that looks correct in the abstract breaks down when tools return unexpected results, environment states differ from assumptions, or constraints are violated mid-sequence.
PIVOT, the leading trajectory refinement framework, achieves up to 94% relative improvement in constraint satisfaction on DeepPlanning and GAIA benchmarks while using 3–5× fewer tokens than competing refinement approaches — making it both more accurate and more efficient.
How It Works
A trajectory refinement cycle has four stages:
- Plan — the agent generates an initial multi-step trajectory
- Inspect — the agent executes part of the trajectory and observes real environment responses
- Calculate discrepancy — using textual gradients (natural language descriptions of what went wrong and why), the agent identifies where the plan diverged from execution
- Evolve — the agent generates an updated trajectory incorporating those corrections, then re-inspects