How Our 24/7 AI Coworker Replaced Half Our Internal Tools
An AI coworker that runs around the clock, manages its own files, remembers everything we've discussed over the past few months, and messages us on WhatsApp when something needs attention. That's what we've been running at Array and it's changed how we operate every day.
We launched Array AI Labs last year and went through a lot of iterations to get here. Early versions were just a static dashboard and AI workflows on triggers. Really useful but unable to think for itself or pick up where it left off. Here's what actually made the difference.
It Remembers Everything
The turning point was when our agent got a memory layer that actually persists. Structured memory that updates as it learns about our team, our projects, and how we work. It knows who it's talking to, what we care about, and what happened in conversations from weeks ago without anyone having to remind it.
We also built background processes that take everything the agent has seen and turn it into a searchable knowledge base so it gets smarter the longer it runs.
It Works While We Sleep
We set up a heartbeat, a pattern OpenClaw made popular. The agent wakes up on a schedule, checks what's happened, reviews open tasks, and flags anything that needs our attention. If something breaks overnight our system catches it. If a task stalled it reminds us to review.
We also connected it to WhatsApp and Slack so we can reach it from anywhere.
It Builds Its Own Tools
Once your agent is running in a sandbox with its own filesystem, you realize it can do a lot more than answer questions. It can build things.
We started with a standard operations dashboard that worked fine until every week someone needed a new view or a different chart. Requests would pile up for days. So I handed the agent a spreadsheet and said build me a dashboard. It spun up a working app with live data in minutes.
Now instead of requests piling up, anyone on the team just messages the agent. It updates the code, rebuilds, and it's live. What started as a dashboard has turned into our internal operating system. The agent maintains it and it moves as fast as we do.
What Surprised Us
The model matters way less than we expected. What actually makes the difference is whether the agent can hold context over time and keep learning from every conversation.
We didn't expect how much sandboxing would matter. Once your agent is running 24/7 with real filesystem access, having it isolated in its own environment so it can work freely without risk turned out to be essential.
Loading a bunch of integrations at once actually makes agents worse. They spend half their context reading tool definitions instead of doing the work. Giving the agent skills it can pick up on demand and put down when it's done made everything faster and more reliable.
And the things that sound small but ended up being real unlocks: putting the agent on WhatsApp so we don't have to open a laptop to talk to it, letting the agent schedule its own tasks, and giving each agent its own email and phone number instead of sharing ours. That last one turned out to be important for security reasons we didn't think about early enough.
What's Next
We're seeing teams start to connect multiple coworkers together. We've been experimenting with A2A protocols and exposing each agent as an MCP server so other agents and tools can call them directly. Multi-agent memory is the other big challenge since getting memory right for even a single agent is still evolving rapidly. For now one main agent with sub-agents it can call in parallel for specific tasks is what's working best for us.
The space is moving incredibly fast and I feel lucky that our team gets to build with these tools every day. If you're working on any of the infrastructure that makes this possible, I would love to talk.
Array Ventures invests $250K-$3M at formation in AI infrastructure. We use what we invest in. Email deals@array.vc.