The Coding Agent Workflow From Issue to PR#

Coding agents are finally useful enough to make teams uncomfortable.

That is a good sign.

The bad version is giving an agent a vague ticket and letting it edit the repo until something compiles.

The useful version is a workflow from issue to pull request.

It has intake, context, planning, implementation, tests, review, and handoff.

The workflow should not start in code#

It should start with the issue.

The agent needs to understand:

• What problem are we solving?
• Who reported it?
• What behavior is expected?
• What files or routes are likely involved?
• What is out of scope?
• What tests would prove the fix?
• What risk should a reviewer care about?

If the ticket is fuzzy, the agent should ask for clarity or produce a scoped plan.

It should not freestyle.

Step 1: Intake#

The intake agent reads the issue and classifies the work.

Examples:

• Bug fix.
• UI polish.
• API behavior.
• Auth flow.
• Data migration.
• Build failure.
• Test update.
• Docs or content.

That classification decides which tools and checks matter.

A CSS polish task does not need the same workflow as a payment webhook change.

Step 2: Context scout#

The context agent gathers evidence before editing.

It should pull:

• Related files.
• Similar past changes.
• Failing logs.
• Existing tests.
• Route boundaries.
• Feature flags.
• Current UI screenshots if relevant.
• Recent commits touching the same area.

This is where coding agents become useful.

Not because they type faster. Because they can gather the boring context humans skip when rushed.

Step 3: Implementation plan#

Before edits, the agent should write a short plan:

• Files to change.
• Files not to touch.
• Expected behavior.
• Tests to run.
• Rollback notes.
• Risks.

This is not theater.

It creates a reviewable contract before the diff exists.

Step 4: Edit with boundaries#

The implementation agent can now work.

Give it:

• The plan.
• The relevant files.
• The repo rules.
• The allowed write scope.
• The test commands.
• The output format.

Do not let it roam through the codebase for unrelated cleanup.

Small diffs win.

Step 5: Verify#

The agent should run the checks a human would run:

• Unit tests.
• Typecheck.
• Lint.
• Build.
• Browser smoke test for UI.
• Screenshot check for visual work.

If a check fails, the agent should explain the failure and either fix it or mark the PR as blocked.

No quiet failures.

Step 6: PR handoff#

The pull request should include:

• What changed.
• Why it changed.
• Screenshots if UI changed.
• Tests run.
• Risks.
• Follow-up work.
• Anything intentionally left alone.

The reviewer should not have to reverse-engineer the agent's thinking from the diff.

Where Codelit fits#

Codelit can turn this into an agent workflow before you wire it into GitHub.

You can model:

• Intake agent.
• Context scout.
• Implementation agent.
• Test runner.
• Policy reviewer.
• Human approval gate.
• GitHub tools.
• CI handoff.
• Repo pack.

That gives the team the workflow, not just another "AI writes code" demo.

Build it in Codelit#

Try this:

Build a coding agent workflow that turns a GitHub issue into a pull request. Include issue intake, context gathering, implementation plan, file edit boundaries, tests, review gates, CI checks, and PR handoff copy.

Design an issue-to-PR coding agent

The agent should not just write code. It should make the work reviewable.