Use an IDE agent when the work benefits from live conversation and local context; use a cloud agent when the task is bounded, reproducible, and valuable enough to run asynchronously.
This guide focuses on the engineering decision behind cloud coding agents vs IDE agents: what to standardize, what to constrain, and what evidence a reviewer should expect before accepting the result.
The core decision
IDE agents share the developer’s immediate feedback loop. They are strong for exploration, small edits, and decisions that change every few minutes. Cloud agents trade some immediacy for isolation, parallelism, and a durable handoff that another person can review later.
A workflow that holds up in review
Route work by coupling. If a task needs product taste, visual judgment, or repeated clarification, keep it interactive. If it has a stable reproduction, an explicit expected result, and automated checks, package it for remote execution. Teams often use both: explore locally, then delegate the mechanical implementation or regression work.
The failure mode to design around
The common mistake is measuring both modes by typing speed. The real comparison is coordination cost. A cloud run that saves thirty minutes of coding but consumes an hour of review and reruns is not a win. Track elapsed time, developer attention, review burden, and defect escape together.
Implementation checklist
- Choose the mode from task shape, not novelty.
- Keep uncommitted local context out of remote assumptions.
- Preserve a clear branch and review boundary.
- Measure total human attention, not generated lines.
Turn the checklist into operating controls
- Choose the mode from task shape, not novelty: name the owner, the evidence that proves it happened, and the condition that should stop the run.
- Keep uncommitted local context out of remote assumptions: name the owner, the evidence that proves it happened, and the condition that should stop the run.
- Preserve a clear branch and review boundary: name the owner, the evidence that proves it happened, and the condition that should stop the run.
- Measure total human attention, not generated lines: name the owner, the evidence that proves it happened, and the condition that should stop the run.
The list becomes useful when every item produces visible evidence. Store that evidence with the task or pull request rather than in a private chat. A future reviewer should be able to tell which repository revision was used, which permission profile applied, what stopped or failed, and who accepted the remaining risk. For cloud coding agents vs IDE agents, a short, complete record is more valuable than a long narrative that cannot be reproduced.
Move from one run to a repeatable practice
Introduce the practice with one task class and one repository. Compare the delegated path with the way the team completes the same work today, including waiting, review, and rework. Make the handoff visible in the issue tracker so the experiment does not create a second, private backlog. After several runs, write down which characteristics predicted success and which required live engineering judgment. That evidence should determine the next task class, not a general mandate to use an agent.
Before expanding the workflow, ask three review questions:
- What evidence shows that choose the mode from task shape, not novelty was satisfied, and would that evidence survive a rerun from the recorded commit?
- What evidence shows that keep uncommitted local context out of remote assumptions was satisfied, and would that evidence survive a rerun from the recorded commit?
- What evidence shows that preserve a clear branch and review boundary was satisfied, and would that evidence survive a rerun from the recorded commit?
Write the answers in the same place as the code review. That creates a compact decision record and lets the team compare later runs without relying on memory.
A practical acceptance test
Run the workflow from a clean checkout at a recorded commit. Give the agent only the documented task packet and the intended permission profile. Then ask a reviewer who did not launch the run to reproduce the important checks, explain the changed behavior, and identify the rollback path. The task passes only when the artifact, evidence, and repository state agree. Keep the failed examples as regression cases; they are more useful than a polished demo because they reveal where instructions, environment, permissions, or tests need improvement.
Related reading
- Agentic Coding in the Cloud: The Complete Guide
- Asynchronous Coding Agents: A Practical Operating Model
Primary references
Vendor features, limits, preview labels, and pricing can change. Recheck the linked first-party documentation for the current state before making a purchase or rollout decision.