What's the difference between running agents in sequence vs. in parallel?

Sequential execution is the right choice when each step depends on what came before it. A research agent gathers information, then an analysis agent interprets those findings, then a writing agent produces content based on the analysis. Each agent needs the previous output to do its job. The workflow runs in order: step one completes, its output passes to step two, step two completes, its output passes to step three, and so on.

Parallel execution works when multiple tasks are independent of each other. If you need to research three different competitors, those research tasks can run at the same time because none of them depends on the others. The workflow launches all three agents simultaneously, and execution continues once all parallel branches complete. This reduces total processing time compared to running the same tasks one after another.

In Brazenly's Workflow Builder, you control execution order by how you connect nodes on the canvas. Drawing a single line from one node to the next creates sequential execution. Drawing lines from one node to multiple downstream nodes (or from multiple nodes into one) creates parallel branches. You can also set edge conditions so certain paths only execute based on success or failure of a previous step, giving you additional control over branching logic.

Most real workflows combine both patterns. A content pipeline might start with parallel research agents gathering information from different sources, then converge into a sequential analysis step that synthesizes all the research, followed by sequential writing and review steps. The structure matches the natural dependencies in your process: parallelize where tasks are independent, sequence where outputs feed into inputs.