Bayesian build-outcome confidence
Coined hereA posterior probability — updated from observed PostToolUse build and test outcomes — that a given plan step will succeed when executed, used by the agent to reorder or pause its plan in real time.
Bayesian build-outcome confidence assigns each plan step a prior probability of succeeding (derived from features of the change — files touched, test coverage of those files, recency of related failures) and updates that posterior every time the build pipeline returns a verdict. The agent reads the posterior and either proceeds, pauses for review, or rebatches the step.
Placeholder body. Replace with the full glossary entry per playbook §9. Cite USPTO 10,684,851 (“predicting build failures”) and AutoStan as the closest adjacent prior art.
When to use
- Agentic sessions across solutions large enough that build-test cycles cost more than a few seconds.
- Greenfield features touching multiple subsystems, where ordering plan steps by confidence shortens the loop.
Compare with
A static plan-step ordering picks once and commits. Bayesian build-outcome confidence reorders based on evidence — closer to a runtime scheduler than a planner output.