Practice Observations
Observations of specific engineering use cases, encountered problems, and reported practices extracted from the evidence corpus.
18Evidence
18Assessed
0Pending
39Observations
100%Coverage
Assessment coverage measures evidence records explicitly assessed for practice observations. Assessed evidence may legitimately contain zero observations.
6 practice observations · learning
| Company | Specific use case being solved | Problem encountered | Reported practice | Selection condition |
|---|---|---|---|---|
| Cursor | Turn completed agent work into reusable improvements for future workflows. | Learnings from completed tasks disappear unless they are reviewed and encoded into durable workflow artifacts. | The Reflect skill runs three parallel reviewers, synthesizes durable learnings, checks whether they should be encoded structurally, and requires approval before skill edits. | learningverification |
| Teresa Torres | Determine whether a change to an AI workflow actually improves its behavior. | Anecdotal testing cannot reliably distinguish an improvement from a regression. | Establish a baseline, change prompt/context/model/decomposition/orchestration, rerun the same inputs, apply evals, and compare scores. | learningverification |
| Teresa Torres | Turn failures observed in real AI usage into regression protection. | Production failures remain isolated incidents unless they become reusable evaluation criteria. | Review traces, log and categorize recurring mistakes, and turn those failure categories into eval candidates. | learningobservabilityverification |
| Infobip | Continue or reconstruct engineering work across agent sessions. | Important state disappears when it exists only in conversational history. | The workflow externalizes durable state into Git, task state, logs, and other artifacts. | contextlearning |
| Uber | Find expensive behavioral patterns in live agent sessions. | Repeated unnecessary tool use and other poor session behaviors can silently consume compute. | Uber uses live cost counters and a session-analysis dashboard that identifies 16 anti-pattern classes with financial impact and targeted remediation. | economicslearningobservability |
| Spotify | Automatically repair an agent-generated change that fails quality evaluation. | A verifier or judge veto would otherwise leave failed work for a human to recover. | Judge vetoes are fed back into the coding-agent loop, and Spotify reports that the agent course-corrects about half of vetoed cases. | learningverification |