Choosing Cloud-Delegated vs Terminal Interactive Coding Agents

The value of an intelligent coding agent is not whether it can emit a few lines of code. It is whether it can finish a bounded job in a real codebase: understand the repository, change the right files, run the checks, and hand the result back to a person.

Common products fall into two working philosophies. In a terminal-interactive style, you give a task to a local session. The agent reads and edits as it goes, pauses at critical steps for confirmation, and unfolds the work in the terminal. In a cloud-delegated style, you submit a goal; the job runs in an isolated environment; the local workspace is not rewritten by default; results come back as a batch. The run may split into parallel subtasks. Both can edit multiple files and run commands. The experience is not the same. Choose by whether the workflow matches the risk, not by who has the louder slogan.

Feel the difference on the same kind of task

Take a typical job: replace the logging framework with another implementation, and keep the build and key tests green.

An interactive path usually scans dependencies and config, lists modules that will change, asks for confirmation before editing build files, call sites, and configuration, then compiles and tests locally or in a specified environment. On failure, logs go back into the session and the agent continues. You can always see the current files, commands, and open issues. That fits when the blast radius is unclear, or when some directories must not be touched.

A delegated path usually takes the goal and constraints, clones or syncs into an isolated workspace, analyzes, edits, builds, and tests in a sandbox, then returns a diff plus a write-up. While you wait, the local terminal often shows only “in progress.” Intermediate reasoning and failed attempts are not shown by default. The upside is a clean environment, disposable failures, and cheaper parallel exploration. The cost is a thicker black box. If acceptance criteria are vague, “done” may mean self-tests passed, not that the change is actually usable.

This is not good versus bad. It is where control lives. Interactive keeps control on each confirmation. Delegated moves control forward into the task statement, permissions, and acceptance conditions.

What each style actually solves

A terminal-interactive agent behaves like a pair programmer sitting next to you. It fits when requirements are still converging and you must read code while tightening the spec; when changes touch auth, payments, or data migration and must be gated step by step; when you have uncommitted local experiments you do not want a remote environment to overwrite; or when team rules require a human record for every file write and script run.

Its limits are equally clear. A strong model that stops too often fragments long tasks. If people habitually approve everything, confirmation is theater. If the local environment diverges from CI, local green and pipeline red will repeat.

A cloud-delegated agent behaves like assigning a ticket to an isolated workstation. Code is changed in a container or a throwaway working copy. Host dependencies, secrets, and untracked files stay out unless you explicitly mount them. It fits bounded batch work: upgrade dependencies from a list and fix compile errors, rename at scale and update references, try builds across several configs in parallel.

Parallel subtasks can cut wall-clock time. Parallelism is not free correctness. If subtasks share state or overwrite the same files, merges conflict. Isolation also means the agent cannot see services, certificates, or data that exist only on your machine unless you provide them safely.

Four sentences align expectations. Want to watch the process: interactive. Want isolated trial and error: delegated. Need to change direction at any time: interactive. Goal and acceptance already frozen: delegated. Small, reversible edits: either. Irreversible edits need a confirmation gate, whether execution is local or in the cloud.

Dimensions that actually matter

Do not decide with unreproducible claims about which agent is “stronger.” Check engineering constraints.

Process visibility. Interactive agents typically show which paths were read and which commands are about to run. If a delegated agent returns only a final diff, reviewers cannot tell whether tests ran or failed cases were skipped. Either way, the deliverable should include a change summary, a file list, checks that ran and their results, leftovers, and known risks.

Permissions and blast radius. Least privilege should cover three actions: read the repo, write allowed directories, run allowed commands. Production credentials, cloud account keys, and personal tokens should not live long-term in a session or image just to make the agent succeed. A delegated sandbox lowers the chance of wrecking the host machine. It does not automatically lower the chance of wrecking the business repo—merge can still land a bad commit on main. Step-by-step confirmation lowers mistake rates until fatigue clicking makes it fail.

Environment consistency. Tests the agent passes must use the same lockfiles, base images, or build scripts as the team gate. Otherwise you are comparing environment luck, not agent skill. Logs, caches, and temp files should be cleanable so one failure does not poison the next run.

Context and cost. Long repos should not be dumped into context indiscriminately. A more stable pattern is: locate relevant modules from directory notes, interface contracts, and failure logs, then read only needed files. Delegated parallelism multiplies context and compute. Interactive confirmation multiplies human time. Efficiency is machine time plus rework time.

Recoverability. Every material change should land as a reviewable diff, not only in conversation memory. Failures should restart from the last version that passed checks, not accumulate patches on a half-finished tree until nobody understands it.

How to write tasks so either mode stays controllable

Use five blocks instead of one spoken sentence.

• Goal: an observable outcome, for example “build succeeds, the named test set passes, business code no longer calls the old logging facade directly.”
• Scope: directories that may change, and directories that must not (generated code, vendor deps, sample secrets).
• Constraints: keep public APIs compatible, do not bump unrelated major versions, do not change data formats.
• Acceptance: commands or pipeline stages that must run, and what counts as failure.
• Stop conditions: on ambiguity, need for production data, or need for a new secret, stop and ask—do not guess.

A plain-text sketch:

Goal: replace the logging implementation and keep the public interface unchanged.
Allowed paths: build descriptors, logging config, logging calls in application source.
Forbidden: database scripts, committing any secrets, deleting tests.
Acceptance: run the agreed build and tests in a clean environment; summarize failing cases.
On uncertainty: list options and stop. Do not pick an incompatible default.

In interactive sessions, writing “stop and ask” into the constraints is more reliable than hoping the model will volunteer. In delegated jobs, pinning the acceptance commands is more reliable than asking afterward whether tests ran.

Collaboration and governance in production

Individuals can tolerate some black box. Teams should treat the agent as a collaborator that submits code. At minimum: agent diffs follow the same review path as humans; diffs do not smuggle unrelated formatting or renames; execution logs trace to a task and an input spec; high-risk directories need a second confirmation or two-person review. For delegated runs, keep command output from the isolated environment long enough to debug failures, not only a “completed” line. For interactive runs, do not treat the confirm button as ceremony; list delete, publish, and migrate commands separately.

Metrics need not be heroic multipliers. Useful ones: time from task to reviewable diff, rework count, defect types found in review, whether tests actually ran, whether unauthorized reads or writes happened. If rework comes from an unclear goal, a stronger executor will not fix it. If rework comes from environment drift, unify build and dependencies first, then talk about agents.

A practical selection order

First, grade the task. Read-only analysis, docs and comments, small reversible refactors can lean delegated for parallelism and isolation. Changes that touch permissions, data, release, or external systems default to interactive confirmation, or to delegated diffs with a human running the dangerous commands locally.

Second, write acceptance and forbidden items, then choose where execution happens.

Third, try both styles on a medium, reversible real repo. Compare diff quality, whether checks were complete, and how often a human had to intervene—not a subjective “feels good.”

Fourth, turn recurring constraints from the trial into a team template so later tasks inherit the same boundaries.

Both agents run the software engineering loop: understand, change, verify, deliver. The difference is whether the loop happens in front of a person or in isolation. When you need visibility and the ability to turn, use terminal interaction and keep confirmation real. When you need a clean environment and parallel trial-and-error, and the goal is already written down, use cloud delegation and pull acceptance forward.

The typical failure of the wrong mode is either people interrupted by confirms until they cannot move, or people handed an unexplained “done.” Once task boundaries, permissions, and checks are explicit, mode is only where execution happens. Quality still comes from constraints and review, not slogans.