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.

13 practice observations · coordination

Company	Specific use case being solved	Problem encountered	Reported practice	Selection condition
Cursor Evidence →	Coordinate multi-day programs containing many stacked pull requests and subagents.	Parallel work needs explicit ownership, bounded context transfer, durable state, and independent verification.	pstack defines coordinator, optional sub-coordinator, worker, and verifier roles; structured state files; queue events; and worker briefs.	contextcoordinationverification
Cursor Evidence →	Choose whether a task should use multi-agent orchestration or a plain agent.	Coordination ceremony can cost more than it saves on work small enough for one agent.	The Orchestrate playbook says not to orchestrate when one agent can finish inside the task budget and uses a task-scale threshold for orchestration.	coordinationeconomics
Uber Evidence →	Reduce the cost of high-volume agentic engineering work.	Using an expensive primary model for every bounded subtask makes scaled execution more costly.	A primary model decomposes and evaluates work while lower-cost subagents execute bounded tasks.	coordinationeconomics
Cursor Evidence →	Integrate changes produced simultaneously by many agents.	Conventional merge behavior accumulated more than 70,000 conflicts in an earlier run.	Cursor uses neutral third-party agents for merge conflicts and a purpose-built version-control system at higher commit throughput.	coordinationexecution
Cursor Evidence →	Coordinate hundreds of agents building a substantial software system concurrently.	Homogeneous agents make conflicting architectural decisions and spend context on both global planning and local implementation.	Cursor separates planner agents that decompose and delegate goals from worker agents that execute bounded pieces.	contextcoordination
Cursor Evidence →	Keep independently produced implementation decisions coherent across a swarm.	Workers can make locally reasonable but globally incompatible design decisions.	Cursor uses reconciler agents to resolve conflicting design decisions.	coordination
Cursor Evidence →	Review large volumes of concurrently agent-produced code.	A single review mechanism can become a verification bottleneck as swarm output grows.	Cursor uses stacked review agents with different review lenses.	coordinationverification
Spotify Evidence →	Coordinate agent-generated migrations across thousands of repositories while directing scarce human attention.	At fleet scale, teams need to know which changes were created or merged and which require human intervention.	Fleetshift schedules and orchestrates Honk changes and tracks migration progress, pull-request state, and changes needing human attention.	coordinationobservability
OpenAI Evidence →	Run more concurrent Codex coding tasks than an engineer can comfortably supervise as separate sessions.	Engineers lost track of work and debugged stalled agents beyond roughly three to five simultaneous sessions.	Symphony moves task assignment and workspace lifecycle into an always-on orchestrator where open tasks receive agents in isolated workspaces and humans review outcomes.	coordinationexecutionobservabilityverification
Cursor Evidence →	Execute multiple parts of an engineering task concurrently.	Parallel agent work needs decomposition, isolation, and dependency-aware composition to avoid interfering changes.	Cursor Multitask decomposes work into asynchronous subagents, uses worktrees to isolate concurrent changes, and parallel plans to preserve task dependencies.	coordinationexecution
Spotify Evidence →	Apply a dataset migration across thousands of downstream consumers.	Repository-scale changes need coordinated scheduling and pull-request execution across a large fleet.	Spotify combines Honk, Backstage, Codesearch, and Fleet Management to target and orchestrate the migration.	coordinationexecution
Spotify Evidence →	Execute software migrations across a large repository fleet with a background coding agent.	Deterministic transformation declarations do not cover every migration, while repository targeting, pull-request lifecycle, review, and production merging still need coordinated infrastructure.	Spotify retained Fleet Management for repository targeting and pull-request lifecycle while replacing transformation declarations with natural-language instructions executed by a coding agent.	coordinationexecution
Spotify Evidence →	Turn developer requests from Slack or GitHub into background coding work.	The background coding agent needs sufficient task information before it can independently produce a pull request.	An interactive agent gathers task information and hands a resulting prompt to the background coding agent.	contextcoordination