Programmers and AI: Coordinating Coding Agents

Delegation begins with a dependency decision

A fictional library wants a catalogue filter. One coding agent builds the search function while another builds the screen. The first returns an object containing results; the second expects a plain list. Both tasks can look complete while the combined feature fails. The programmer's coordination work begins before either task is launched.

GitHub's cloud-agent guidance recommends clearly scoped tasks with acceptance criteria and useful file context. It also identifies broad, ambiguous or domain-heavy work as work a developer may choose to handle personally. Delegation therefore involves choosing which decisions are settled enough to hand over. [GitHub Docs: Best practices for using GitHub Copilot to work on tasks]

Agree on the shared contract

For our fictional filter, BELL RIVER suggests defining the function's input, returned fields and empty-result behaviour first. Give both agents the same examples. Assign separate files where practical and identify anything that must wait. Two tasks that both redesign the data format are coupled decisions, even if two agents can start them simultaneously.

GitHub documents custom agents with tailored instructions and selectable tools, plus subagents that work in separate contexts and can run in parallel where appropriate. These are product capabilities, not proof that additional agents improve every task. Their outputs still have to fit the same application. [GitHub Docs: About custom agents]

Manage outputs, questions and integration

Our proposed task brief contains the expected change, relevant background, allowed files, examples and the checks needed at completion. Ask an agent to surface unresolved assumptions rather than quietly redesign a neighbouring component. For a research-only subtask, select tools that match that purpose instead of granting editing ability by habit.

In the library example, the programmer reconciles disagreements about the shared interface, reviews each change and runs the screen against the real search function. A final integration check searches for an existing title, an absent title and an empty query. Summaries from separate agents do not demonstrate that these combined behaviours work.

Measure the coordination cost

BELL RIVER's editorial recommendation is to track time spent preparing tasks, correcting assumptions, resolving conflicts and checking the result. Count the whole delivery effort. For a short, tightly connected change, one agent or direct coding may be simpler. Learning when to stop splitting work is part of learning to delegate it.

Run a small delegation experiment

Create a local catalogue with six invented titles. Write the filter contract yourself. In separate tasks, ask for an implementation and a set of test cases based on that contract. If no agent tools are available, draft both task briefs and ask a peer to identify the assumptions each leaves open.

Review the outputs, resolve one disagreement explicitly and record the integration result. Keep the brief, changes and a short account of coordination time as your portfolio. Describe this as a single learning experiment, not a benchmark: task difficulty, model choice and your familiarity all affect what happens.

Three practical first steps

• Select one small feature and define its shared interfaces before dividing implementation work.
• Write task briefs with a bounded scope, concrete examples and an explicit completion check.
• Integrate and inspect the outputs, recording coordination and correction effort as well as generation time.

These GitHub documents describe specific capabilities and recommended workflows, which can change. They do not establish universal speed improvements, hiring demand or a new job category. The catalogue example is fictional and BELL RIVER's suggested workflow is editorial guidance. Sources checked 13 October 2026.

Sources & editorial note

• GitHub Docs: Best practices for using GitHub Copilot to work on tasks
• GitHub Docs: About custom agents

This career guide distinguishes source-backed descriptions from BELL RIVER’s editorial suggestions. Learning exercises are illustrative, not employer requirements, certifications or promises of employment. Overseas examples do not establish demand or eligibility in the UAE.