Sequential research is slow. The agent fetches three sources one by one, then a fourth, then summarises. Most of that time is network wait, not work, and a serial loop wastes it.
Independent reads or computations whose results combine at the end. Multi-source research, parallel API checks, batch document summarisation.
When step N depends on step N-1's output. Parallelism adds nothing if the data is causally chained.
Describe this pattern to the builder assistant and it wires it up for you, brains, tools and triggers. It's vibe coding for agents, no config to write.
Walking through the pattern one piece at a time so the design is clear, not memorised.
Three calls to agent(agent="explorer", task="...") go out, one per research angle. Each spawns its own turn against its own (cheaper) brain.
Each explorer only sees its own angle and the web module. No shared state to coordinate, no risk of one explorer's findings polluting another's search.
Each spawned agent's result comes back into the coordinator's context as a tool result, ready to synthesize.
The lead's next turn writes the final answer using all three results. Wall-clock time is close to the slowest single explorer, not their sum.
The pattern above is not the only answer. Here is when something else is the right call.
Easier for the model to reason about, slower. Good when each step's output narrows the next query.
Run one explorer multiple times in sequence with different prompts. Cheaper to reason about, sequential cost.
Engineering notes from the Digitorn team. No marketing, no launch announcements, no "10 prompts that will change your life". Just the things we write that we'd want to read.
