−31%
goal-drift failures
Intent checkpoints triggered by detected plan drift kept long-running agents aligned with the customer's original goal more often without restarting the workflow.
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Do long-running LLM agents still pursue the user's original goal after dozens of steps?
Measure goal drift across long-running LLM agent trajectories and test lightweight checkpoints that restore user intent without restarting the workflow.
WHAT YOU CAN EXPECT
Evidence protects you in both directions.
01
Illustrative result · threshold met
−31% · goal-drift failures
What your study would pin down
You get the drift triggers for your workflows, the checkpoints that actually recover customer intent, and the point where intervention costs more than the failures it prevents.
Decision
Trigger checkpoints only when drift signals indicate that customer intent is at risk.
02
Decision
Evidence protects you in both directions.
What the data may show
No meaningful drift reduction
If the intervention does not clear the predefined threshold, that is evidence against spending more to build, launch, or scale it in this context.
Decision
Do not add checkpoints as a general orchestration mechanism.
The other expensive error
A real opportunity can still be left on the table.
If the intervention clears the threshold but the business keeps the current approach, measurable savings, revenue, adoption, or risk reduction may remain unrealized.
Decision
Act only when the measured opportunity is large enough to justify the change.
03
Source
Public precedent · not your result
HYPOTHESIS
Periodic intent checkpoints triggered by detected plan drift will reduce goal-inconsistent terminal actions by at least 25% with less than 5% additional model tokens.
PRIMARY METRIC
Rate of completed tasks whose terminal action remains consistent with the original user goal and constraints.
MEANINGFUL THRESHOLD
At least 25% fewer goal-inconsistent terminal actions with less than 5% additional tokens.
BUSINESS TARGET
More reliable long-running LLM agents and clearer evidence for when orchestration should pause, resume, or request human input.
DECISION RULES
Goal drift falls within token budget
Replicate across agent frameworks and longer trajectories.
Drift falls but checkpoint overhead is high
Trigger checkpoints only on higher-risk state changes.
No meaningful drift reduction
Do not add checkpoints as a general orchestration mechanism.
Business outcomes are research targets, not guarantees. A null or negative result may still create substantial value by preventing investment in an ineffective product, feature, or campaign.
Population
Long-running agent tasks involving planning, tool calls, interruptions, intermediate observations, and changing local subgoals.
Intervention
Compact intent checkpoints that compare the current plan and intended terminal action with the user's original constraints.
Comparator
The same long-running workflow without explicit goal checkpoints.
Secondary metrics
Task success · Recovery after interruption · Additional tokens · Checkpoint false-positive rate
MAKE IT YOUR DECISION
Turn this research question into a decision for your business.
We adapt the population, intervention, thresholds, and economics to your customers. The result may tell you to scale, to stop spending, or to act on an opportunity you are currently leaving unused. Each of those is a useful business decision when the evidence is strong enough.
The goal is not a positive result. The goal is evidence strong enough to change a real decision.