Parallel agent orchestration is compatible with deep work
Agent orchestration is making me rethink how I work
Over the last 4 years I have been the world’s loudest advocate for monotasking. Nothing beats focused deep work.
AI is really making me reconsider this.
I find my most productive days are when I’m managing many agents. It looks something like:
My main attention is on a deep thinking task, like a complicated design doc
I check in on a codex thread every 20 minutes
I check in on a Claude Code thread every 20 minutes
I can loop this for hours, and the marginal productivity loss from splitting my attention off the main task pays itself off from the insane amount of work I get done by managing the agents as a background task.
You can’t delegate all thinking to AI
Delegating all of your thinking to agents means you’re not necessary. Think about it. If you’re finding that your job is writing a few words in a prompt and commiting the code changes that are made, your role in that process is quite fragile.
Some things really require your full attention, especially when LLMs can’t make meaningful progress.
Recently, I had the responsibility of designing a critical service refactor that spanned several apps and required a lot of legacy knowledge.
With enough context, something like this could probably get a passable result from an LLM. But by the time you gather (or create) enough context, you might as well have designed the refactor yourself.
The more complex and nuanced something is, the more your own brain is needed.
Need to throw together a couple of CRUD endpoints in an API? Spin off an agent to do that!
But when you need to figure out which CRUD endpoints should exist, how the request/response contract should look, and so on, then it’s time to dust off your thinking hat.
This post is sponsored by Cosmos, the agent orchestration platform for AI-native engineering teams.
Cosmos is a shared system where agents work across triage, spec, implementation, review, testing, deployment, and feedback with the context, memory, and controls teams need. Humans steer, agents do the implementation, and the system gets better as the team uses it.
Something complicated still deserves your full attention
When you have a task that you are doing, it’s worth doing with your whole brain. That means you can’t be balancing three of these at once. It also means you can’t be checking your phone every 4 minutes.
I wouldn’t exceed one metaphorical thread of a complex task. But recently I’ve been challenging the monotasking strategy with some agent orchestration in the background.
If you have time to send a message to a coworker, you have time to manage an agent working on your behalf.
You can get a lot done managing parallel agents
My strategy revolves around tradeoffs. You lose a bit of productivity when you pull yourself away from your main task. But with coding agents, you gain some of that productivity back.
This is especially true as agents get longer-running. The average length of an agent run on a task has exploded over the last year. It’s not unusual that with sufficient permissions, an agent can work on a task without your feedback for hours.
So your parallel agent orchestration does not mean you need to divert your attention every few minutes.
BUT, flow is still important!
Nothing is better than writing code and getting into a pristine flow state.
AI doesn’t have to kill flow state.
What do you think? Have AI tools change how you approach focus?
Parallel orchestration has transformed the why that I approach agentic projects.
I've the created an agentic micro team.
I've created enduring roles for: system architect (me), agentic platform/project director, builders and verifiers, and global policy creator.
Task bound specialists (e.g. coding, infrastructure, communications and market analysts) are created as need. Some are temporal.
Overall, I've been impressed with what the team has accomplished - quantity and quality.
Human in the loop is still required, of course, but the ability to delegate tasks has been a real time saver and creates high quality baselines that are refined during reviews with the platform director task.