Three Reviewers, One PR, Zero Wait: Build a Parallel AI Agent Pipeline

Part 2 of an eight-part Agentspan series explains the parallel multi-agent strategy, in which independent agents receive the same input simultaneously and return separate results, contrasting with sequential workflows where each step depends on the prior output. Using a Python code-review example, it defines specialized bug, security, and style reviewers, groups them under a parent agent configured with `Strategy.PARALLEL`, and retrieves their findings through `result.sub_results`, reducing execution time to roughly that of the slowest reviewer rather than the sum of all reviewers. Agentspan is presented as a durable orchestration and observability layer that can build native agent workflows or integrate agents from other frameworks, persist completed work after failures, and expose execution details in a dashboard. The discussion recommends parallel execution for independent tasks and sequential execution for dependent ones, while showing that both can be composed into a workflow that runs reviews in parallel before a summarizer produces a final verdict. It also outlines a production extension using GitHub tools to retrieve pull-request diffs, perform parallel reviews, summarize the results, and post comments back to the pull request, with the next installment set to cover handoff-based delegation.

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