Run Parallel Agent Tasks to Reduce Waiting and Speed Up Context Building

Three terminals. Three tasks. One codebase.

While one agent maps the architecture, another digs into the auth module, and a third drafts the actual change.

The single-task bottleneck

You open Roo Code. You start a task. You wait.

The model is thinking. The context is building. You're watching a progress indicator instead of shipping code.

This is how most developers use AI coding agents: one task at a time, sequential, waiting for each step to complete before starting the next. It feels natural because that's how we've always worked with tools. But it's also the slowest possible way to use something that can run independently.

The bottleneck isn't the model. It's the assumption that you need to wait.

The parallel pattern

Power users treat agent tasks like background jobs. Instead of running one task and watching it complete, they run multiple lightweight tasks in parallel: one for fact-finding, one for architecture analysis, one for the actual code change.

"I'll have Roo Code on the left. I'll have like a couple terminal tools in the middle. And I'm actually working in the codebase in multiple parts at the same time. And CLIs allow you to run multiple tasks."

Adam, Roo Cast S01E06

The key insight: not all tasks need the same speed. Exploratory work, where you're building context and understanding the codebase, can run in the background. Active coding, where you want tight feedback loops, stays in focus.

Separating exploration from execution

Think of it as two categories:

Exploratory tasks: "How is this module structured?" "What calls this function?" "Where does this config come from?" These are fact-finding missions. Speed matters less because you're not blocked on the answer. You can let them run while you do something else.

"One of them might simply be a fact-finding mission, right? Like go find out how something's put together so I can start thinking about how I need to change it. Basically your ask mode, right?"

Adam, Roo Cast S01E06

Execution tasks: "Write this migration." "Fix this test." "Refactor this function." These need your attention when they complete because you'll review the diff and iterate. Keep these in your primary focus.

The workflow: kick off 2-3 exploratory tasks, then work on something else. When they complete, you have context you didn't have to build manually. The slower model isn't an obstacle; it's permission to multitask.

The setup

This works best with CLI-based agents or multiple terminal instances. The pattern:

1. Open multiple terminals or use a multiplexer
2. Start exploratory tasks in background panes
3. Keep your primary coding window in focus
4. Check results when tasks complete

"When you're locked into like a single experience, you don't really want to be. You want to be able to open up any number of windows and do a bunch of different things without having to open new IDEs for each one of them."

Adam, Roo Cast S01E06

The tradeoff: more context switching, more terminal management. If you're deep in a single problem and don't need background context, sequential is fine. But if you're starting a new feature or onboarding to an unfamiliar codebase, parallel exploration compounds.

Why this matters for your team

For a team of 5-8 engineers working across a shared codebase, context-building is a constant tax. Every developer spends time understanding code they didn't write. Every feature starts with "how does this part work?"

Running parallel exploratory tasks means that context arrives while you're doing something else. Instead of blocking on "let me understand the auth module before I can estimate this ticket," you kick off the exploration, move to another task, and come back with answers.

For a team shipping daily, even 30 minutes saved per developer per day on context-building compounds to 10-15 hours per week. That's time that goes into actual shipping instead of waiting.

How Roo Code enables parallel context building

Roo Code closes the loop by running tasks autonomously - proposing changes, executing commands, and iterating based on results - without requiring you to watch every step. This autonomous execution is what makes parallel workflows possible.

With BYOK (bring your own key), you control your API spend directly, which means running multiple exploratory tasks in parallel has predictable costs. You're not paying a markup on tokens; you're paying your provider's rates for the actual work being done.

The citable insight: Parallel agent tasks transform model latency from a bottleneck into a parallel processing window, letting developers build context in the background while focusing on high-value execution work.

Sequential vs. parallel agent workflows

The shift

Stop treating the agent as something you watch. Start treating it as something you dispatch.

Kick off the exploratory task. Move to something else. Come back when it's done.

The model's latency becomes your parallel processing window.