One Agent Per Lane
Beat context overload by running many narrow, purpose-built agents instead of one do-everything agent.
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 3
- Confidence
- 95%
The most common reason people get frustrated with AI agents is throwing every task at a single agent, which overloads its context window and degrades quality and memory. The fix is to section off tasks by domain and assign each to its own specialized agent — the same way you separate work streams into different Slack channels or hire different people for different jobs.
Origin
Claire Vo's core unlock after early frustration; the separate-machine dimension was crystallized by Jessie Janes on Claire's How I AI podcast.
Core principles
- 01Context overload, not intelligence, is the real ceiling on agent quality
- 02One coherent lane per agent keeps its context window focused and its memory reliable
- 03Separation mirrors org design: different teams, different channels, different agents
- 04Intersect agents only when the work genuinely needs to intersect
How to run it
- 1
Start with one agent and find the frustration
Run a single general-purpose agent until you notice it forgetting things, losing tool access, or blending unrelated tasks — the symptoms of context overload.
Pro tip Don't jump straight to nine agents. Play with one, feel the limits, then split.
- 2
Split tasks into lanes by domain
Group tasks the way you'd staff a team: work assistant, family manager, sales, research, kids' homework. Assign each lane its own agent with a scoped identity and only the tools that lane needs.
Pro tip Ask 'Would I hire different people to do these jobs in real life?' If yes, use different agents.
- 3
Decide same-machine vs separate-machine by information boundary
If you're fine with agents occasionally reading each other's docs and tools, they can share one machine. If a lane must never see another's data (e.g. a family agent that shouldn't know your work), put it on its own physically partitioned machine.
Pro tip Think of it like a work phone vs personal phone — physical separation is the hardest, clearest boundary.
Watch out A shared machine means any agent can potentially access another's tools; only accept that where it causes 'no heartache' as an employer.
In the wild
Both Claire's work assistant (Polly) and family assistant (Finn) do scheduling, calendaring, and email — but Polly has enough to handle with work that she shouldn't be tracking the kids' soccer schedule, and Finn shouldn't be replying to the work inbox. Claire scaled this into nine agents (Polly, Finn, Max, Howie, Sam, Kelly, Holly, Sage, Q), likening them to nine Slack channels for different teams.
→ Each agent stays focused and reliable within its lane instead of degrading under a single overloaded context window.
Common mistakes
Throwing every task at one agent
A single agent asked to do everything overfills its context window, so it forgets prior conversations, loses tool access, and produces worse results — the exact frustration that makes people abandon agents.
Treating multiple agents as excess or 'AI psychosis'
Multiple purpose-built agents aren't hoarding; they're the same practical separation as having distinct Slack channels per team. Collapsing them into one 'general' stream forces you to process unrelated context and defeats the point.
Is it for you?
Best for
Power users running an agent across several distinct areas of work and home life who keep hitting memory/context failures
Not ideal for
Someone with a single simple use case where one agent's context stays comfortably small
From the transcript
“where people stumble with Open Claw is they read about Open Claw's running my business and they think they can throw any task at a…”
“this really comes down to one concept I think we're”
“I have nine Slack channels with my team. And my marketing team's in one, and my sales team's in another”
From the episode
From skeptic to true believer: How OpenClaw changed my life
Claire Vo