WorkflowAugust 31, 2026

A Practical Workflow for Parallel Coding Agents

Parallel execution is easy. Preserving independent evidence and a reviewable path to main is the real system.

A reliable parallel coding-agent workflow has seven stages: decompose the work, choose dependencies, isolate each task, prepare its environment, dispatch the right agent, verify the result, and merge in dependency order. Running several terminals is only the execution stage.

Four coding agents planning, editing, testing, and reviewing in parallel before results are collected

This guide is provider-agnostic. The workers can be Claude Code, Codex, Gemini CLI, Cursor, OpenCode, or any mix that fits the tasks.

Start with a task graph, not a list of prompts#

Before launching anything, classify the work:

Independent: can merge in any order.
Dependent: needs the output of another task.
Competing: several agents attempt the same outcome and only one result survives.
Shared-state: touches the same schema, generated file, service, or central interface.

Only independent and intentionally competing tasks should start together by default.

Suppose a feature needs an API endpoint, a React screen, database changes, and end-to-end tests. Launching all four at once sounds fast, but the UI and tests depend on the final API and schema. A safer plan is:

Schema and API contract.
API implementation after the contract is accepted.
UI and test work in parallel against that accepted contract.
Integration review after both branches land.

The goal is not maximum simultaneous agents. It is minimum idle time without creating speculative work that must be rewritten.

Give every editable task a physical boundary#

Two agents should not edit the same working directory. Use one of these boundaries:

Boundary	Isolates	Best for
Git worktree	Repository files, untracked files, local build output	Normal features, fixes, tests, docs
Container-backed worktree	Files plus processes, packages, mounts, and network policy	Dependency changes, service-heavy work, untrusted tooling
Remote or cloud workspace	Execution host and lifecycle	Long-running tasks, more compute, work that must survive laptop sleep

Git's worktree documentation describes the underlying model. Agentastic creates and tracks these environments automatically, but the branches remain ordinary Git branches.

Write prompts that produce reviewable outcomes#

Every task prompt should include five things:

Outcome: what should be true when finished.
Scope: where the agent may work.
Constraints: behavior or APIs that must not change.
Verification: commands or evidence required.
Handoff: what the final summary must contain.

For example:

text

Add server-side validation for checkout coupon codes.

Scope: packages/api/src/checkout and its focused tests.
Do not change the public response schema or the existing retry behavior.
Run `pnpm test checkout-coupon` and include the exact result.
Finish with the root cause, files changed, and any behavior you could not verify.

The agent should not decide what "done" means after it has already chosen an implementation.

Put durable context in the repository#

Prompts are task context. Repository conventions belong in versioned instruction files:

AGENTS.md for cross-agent repository rules.
CLAUDE.md for Claude-specific instructions when needed.
Tests and scripts as executable expectations.
Architecture decision records for durable reasoning.
.agentastic/setup.sh for environment preparation.

This keeps Claude Code, Codex, and other agents aligned without copying a long preamble into every task.

Prepare the workspace before inference begins#

The best prompt cannot compensate for a checkout that cannot run the project. A new workspace may need:

Dependencies.
Ignored environment files.
Generated code.
A virtual environment.
A unique database or schema.
A non-conflicting port.
Local emulators or containers.

Agentastic runs .agentastic/setup.sh when it creates a worktree. Keep the script deterministic and idempotent. See environment setup for agent worktrees for a complete example.

Dispatch by task shape, not brand loyalty#

Different agents and interfaces are useful for different work:

Use the provider you trust for a difficult implementation.
Use a fast agent for narrow mechanical changes.
Use a separate model or dedicated reviewer for the final diff.
Race two agents only when the quality gain is worth reviewing two implementations.
Prefer container or remote execution when the task needs an environment, not merely a different model.

Agentastic keeps the execution layer separate from the provider. You can run Claude Code on one branch, Codex on another, and a dedicated review agent after both finish.

Monitor state transitions, not every token#

Watching five transcripts does not scale. A useful control surface answers:

Which tasks are still running?
Which agent is waiting for approval or clarification?
Which task failed setup or execution?
Which branches have changes ready for review?
Which pull requests are open or merged?

Agentastic routes supported provider lifecycle events into the Agents navigator and notification inbox. The terminal remains available when you need detail, but supervision happens by exception.

Verification is a separate phase#

An agent's final message is a claim. Verification is the evidence.

For each worktree:

Inspect the diff.
Check the task stayed within scope.
Run the focused test command.
Start the application from that worktree.
Exercise the changed behavior.
Review migrations, permissions, generated files, and dependency changes separately.
Ask a fresh reviewer to look for bugs the implementer normalized away.

For browser-facing work, the built-in browser gives the agent and reviewer the same page, console, screenshot, and DOM snapshot. A green unit test is not enough when the change is visual or interactive.

Merge in dependency order#

Independent branches can merge in any order. Dependent branches cannot.

After an upstream branch lands:

Update dependent worktrees.
Rerun their validation against the new base.
Resolve conflicts in the branch that owns the dependent change.
Do not accept a previously green result if its assumptions changed.

Keep each branch pull-request sized. A massive parallel diff is still a massive diff.

Use a concurrency budget#

The useful upper bound is set by review, not compute. Track three queues:

Agents actively working.
Agents waiting for you.
Diffs waiting for review.

If the review queue grows every hour, lower concurrency. If agents are idle because tasks depend on an unfinished contract, improve decomposition. If most sessions wait on environment problems, fix setup before buying more parallelism.

A repeatable Agentastic loop#

Open a repository.
Write or update AGENTS.md and .agentastic/setup.sh.
Split the work into independent and dependent tasks.
Launch one isolated workspace per ready task.
Respond only when an agent needs attention.
Verify the result in its own worktree.
Run an independent review.
Merge in dependency order.
Archive or remove completed worktrees.
Record any repeated failure as a repository instruction, test, or setup improvement.

That last step is the compounding part. Every failure you convert into automation makes the next agent cheaper to supervise.

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