Spikes are an exploration of potential solutions for work or research items that cannot be estimated. They should be time-boxed in short increments (1-3 days).

Since all work has some amount of uncertainty and risk, spikes should be used infrequently when the team has no idea on how to proceed with a work item. They should result in information that can be used to better refine work into something valuable, for some iteration in the future.

Spikes should follow a Definition of Done, with acceptance criteria, that can be demoed at the end of its timebox.

A spike should have a definite timebox with frequent feedback to the team on what’s been learned so far. It can be tempting to learn everything about the problem and all of the solutions before trying anything, but the best way to learn is to learn using the problem in front of us right now. Batching learning is worse than batching other kinds of work because effective learning requires applying the learning immediately or it’s lost.


  • Use spikes sparingly, only when high uncertainty exists.
  • Spikes should be focused on discovery and experimentation.
  • Stay within the parameters of the spike. Anything else is considered a waste.

Last modified December 15, 2023: Reorganize (7579932)