Resolving one GitHub issue as a DAG of parallel AI agents

Description and use

Parallel execution of independent AI agent tasks, split by a dependency graph, reduced the time to resolve a GitHub issue by 4 minutes.

Problem

Sequential AI coding agents waste time on independent tasks that could run concurrently.

Outcome

Parallel execution of independent AI coding tasks reduced total runtime by ~4 minutes for a single issue, demonstrating the scaling benefit of DAG-based parallelism.

Workflow

• Decompose issue into dependency graph nodes and edges
• Assign each node to an AI agent with its own branch and worktree
• Run independent nodes concurrently in waves
• Merge results and perform integration validation
• Repeat until all nodes processed

Evidence

Wave 1 produced a dependency required by the remaining work. Wave 2 then ran two independent implementation lanes concurrently. Their individual coding times were roughly: 4m18s, 10m22s. Sequentially: ~14m40s. As one parallel wave: ~10m22s. So this particular run saved around four minutes.

Source