Match the Agent to the Task, Not the Brand

One agent for everything.

That's the mistake.

You pick a tool, build up rules, accumulate custom configurations, and then force every task through it because switching feels expensive. Meanwhile, the agent that's great at large refactors struggles with surgical single-file changes. The agent that answers questions about unfamiliar code chokes when you need it to execute a plan.

The switching cost trap

The intuition is reasonable: invest in one workflow, get good at it, avoid the overhead of context-switching between tools. But the cost of staying loyal to the wrong agent for a given task often exceeds the cost of switching.

The variable that determines switching cost isn't brand loyalty. It's how much context you've encoded.

"If you know the codebase really well, switching from AI agent to AI agent, very easy. If you don't and you've built up a lot of rules and custom things that are particular to that particular AI agent, it can be very hard."

Adam @GosuCoder, Office Hours S01E12

If your rules are portable (standard linters, shared config files, documented conventions), switching is trivial. If your rules live in agent-specific memory or custom prompt chains that don't export, you're locked in.

A practical multi-agent workflow

Instead of picking one tool and hoping it covers every case, match the agent to the task type.

"Roo Code is like I'll use it to ideate, look through certain large files, help me break it apart, come up with a plan for that. And then when it's surgical stuff, I may use another agent. When I just want to ask questions about a codebase, I might use another agent for that."

Adam @GosuCoder, Office Hours S01E12

The pattern:

• Ideation and large refactors: Use an agent that can read across many files and synthesize a plan. Roo Code's ability to iterate based on real tool output makes it strong here: it can explore the codebase, propose a breakdown, and adjust as it learns.
• Surgical single-file changes: Use an agent optimized for tight, focused edits where you already know exactly what needs to change.
• Questions about unfamiliar code: Use an agent tuned for explanation and exploration, not execution.

This isn't about which tool is "the best." It's about which tool fits the task shape.

Short loops, cleared context

The other pattern that enables multi-agent flexibility: keep loops short.

"I'm not as big of a fan of these long-running conversations. I actually like, you know, build a plan, execute it. If that worked or didn't, a lot of times I'll even clear my context at that point and kind of then incrementally work through stuff."

Adam @GosuCoder, Office Hours S01E12

Long-running conversations accumulate stale assumptions. The model references decisions you've since revised. The context window fills with artifacts from three iterations ago. Eventually you're debugging the prompt history instead of the code.

Short loops: build a plan, execute, evaluate, clear. Start the next increment fresh. This also makes switching agents between increments trivial; you're not carrying baggage.

The portability checklist

If you want multi-agent flexibility, audit your current setup:

1. Are your rules in portable formats? Standard linter configs, .editorconfig, documented conventions in the repo, not agent-specific memory banks.
2. Can you export your custom prompts? If your best prompts live only in one tool's history, extract them to a shared doc.
3. Are you clearing context between increments? Or are you accumulating a tangled conversation that only one agent can parse?
4. Do you know which tasks each agent handles well? Run small experiments. Note where each tool struggles.

Why this matters for your workflow

For engineers shipping code daily, the cost of forcing a mismatched agent through a task compounds. You spend tokens on retries. You spend time interpreting suggestions that don't fit the task shape. You build resentment toward a tool that's actually fine; you just asked it to do the wrong job.

The shift: treat agents like specialized tools, not like a primary relationship. A hammer is great for nails. Using it on screws is not a hammer problem.

Keep configurations portable. Keep loops short. Match the agent to the task.

How Roo Code supports multi-agent workflows

Roo Code closes the loop by running commands, tests, and iterating on results - which makes it particularly strong for ideation and large refactors where the agent needs to explore, plan, and adjust based on real feedback.

Because Roo Code uses BYOK (bring your own key), your API keys and model preferences stay with you. There's no vendor lock-in at the token level. Your rules live in standard config files in your repo, not in proprietary memory systems that trap your workflow knowledge.

Roo Code enables portable, task-matched workflows by keeping your context in standard formats you control, so switching between agents for different task types costs you nothing.

When you need Roo Code to explore a large codebase and synthesize a refactoring plan, it can read across files, propose diffs, run validation, and iterate. When you need a different tool for a quick single-file fix, your linter configs and documented conventions travel with you.

Single-agent vs. multi-agent approach