# What does it really take to create autonomous AI coworkers?
**作者**: Shiv
**日期**: 2026-04-18T15:55:10.000Z
**来源**: [https://x.com/shivsakhuja/status/2045531591926329474](https://x.com/shivsakhuja/status/2045531591926329474)
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Ever since I discovered OpenClaw, I've been obsessed with building AI agent coworkers for my startup.
I've tried tons of setups over the past few months using a mix of OpenClaw / Claude Code / Claude Agent SDK and our own product @GooseworksAI.
The dream is to have a team of agents that do useful work autonomously around the clock without constant supervision.
I wouldn't say we're in the dream state yet, but it's getting close and I've learned a lot about what it takes.
I think the reason that I (and many others like me) are obsessed with this idea, is because it's so clear that the agents like Claude Code are already good enough to do all the actual work. The bottleneck is all just orchestration.
Here's what I've learned about the pieces you need to orchestrate a coworker:
0. Environment
When we're talking about AI coworkers, they need an environment to run free. They need their own computer.
You can't run them locally otherwise, they can't work when your computer is off, and its too risky to give them full access to your personal machine.
If you're using OpenClaw, this could be a Mac Mini or an EC2 instance.
For @GooseworksAI, we use cloud sandboxes powered by @e2b.
1. Context
Your agent is only as good as the context it has.
The thing with context is – it requires a continuous stream, not a one-time dump.
I do a few things:
- I do frequent 1:1s with my agent: https://x.com/shivsakhuja/status/2038711292073320734
- My agent has a email address, a phone number and a Slack account, so I'm always texting it stuff - links I find interesting, notes, ideas, etc
- I have a cron job to sync meeting notes
- I have a personal CRM for my agent (updates automatically from calendar + email + meeting notes + linkedin) so it knows about people I'm connected to
- I have given it accounts so it can search through Notion + Linear
- I have signed up my agent to some newsletters (using @agentmail)
- I have an agent consume a content diet similar to what I consume (twitter mostly, but will probably add some podcasts soon via @ListenNotes)
- I have a heartbeat for the agent to read through recent notes and ask me questions about important context in Slack. It's pretty easy for me to respond when I'm prompted with a message.
I haven't tried it yet, but from what I hear @garrytan's gbrain is a pretty good system to stream context too.
2. Persistence
We’re using a filesystem. We dump / stream tons of context in there for each agent. This does very well for the most part. But there is a limit beyond which it's not sufficient.
The agent has a memory folder where it stores important info automatically. The agent also has a tool to search through it’s chat history (past sessions)
I’ve also found SQLite DB to be super helpful for structured query-able persistence. For example, we have an SEO agent, and that needs to keep track of past work, de-dupe, etc before we run a strategy, push to our CMS, email for backlinks, etc. So currently that’s using a SQLite DB in the filesystem for persistence.
There’s probably a very good case to be made for adding a knowledge graph / dedicated memory layer though we haven’t tried this yet.
Our agents also read / write from Linear to manage tasks, which is very helpful.
3. Skills — atomic units of execution
A skill is a repeatable task packaged into a prompt + script so your agent can execute it consistently.
We made some of these skills public at http://skills.gooseworks.ai/ but we have tons more in an internal github repo.
If nothing else, just turning everything into skills makes a massive difference. I'm making skills for everything now.
This makes my agent good at the things I'm good at.
An orchestrator skill has also been pretty useful – this is a parent skill that knows how to orchestrate related sub-skills.
4. Automations and/or a heartbeat — when does it run?
Still very much figuring this out, but my current mental framework is:
- If it's a clear repeatable task → scheduled automation (cron). Pull metrics every Monday, scrape inspiration accounts every Sunday, etc.
- If the agent needs to figure out what to do dynamically → heartbeat. The agent wakes up on an interval, checks what's changed, decides what to act on.
I’m still tinkering with heartbeats but getting the agent to make good prioritization decisions when it wakes up is harder than it sounds.
For many things, I’m just running the skills / orchestrator manually because I don’t trust the agent enough yet.
5. Tools and access
The agent needs to actually do things — hit APIs, send messages, scrape the web, etc
I use a mix of MCP servers and direct API calls wrapped in skills.
Haven't found a "one system to rule them all" — it's whatever works for the task at hand.
6. Communication channels — two-way
A channel where you can message the agent and it can message you. We use Slack primarily, but I can also email or text the agent.
The key is two-way — not just notifications, but actual back-and-forth. We also integrated WhatsApp, iMessage and Telegram, but mostly I just use Slack.
An advantage of Slack is team visibility (the agents are shared, not personal)
Email is important too because a lot of the web works over email (notifications, newsletters, etc).
7. Feedback loop
This is the piece that most people skip but seems super important to me.
The agent does work → you review it → your feedback gets fed back into the system (updated skills, adjusted prompts, new rules).
The agent self-reflects → proposes learnings / updates to its own system
If you have this and it works, the agent’s usefulness / success should (theoretically) compound over time. Without it, the agent only improves when you do dev work.
8. View layer
There are 2 challenges here.
Viewing files in the filesystem. We solved this with our own product (has a baked in filesystem and file viewer), but others may solve this with Obsidian remote vaults or something.
Agent activity dashboard: We have this in Gooseworks, but I found this to be pretty tough in OpenClaw.
Custom dashboards: I need custom dashboards for specific agents. Like I need a custom dashboard for my Outbound sales agent. I don’t have a great solution here right now though we're experimenting with one.
We use Slack alerts but that can get very chaotic if you’re relying on the agent to do a bigger scope of work.
Not sure what the best option is. I suspect it's to give each agent a DB + Dashboard and allow it to customize.
9. One agent or multiple?
I wrote a post about this: https://x.com/shivsakhuja/status/2035176670286786744
the TLDR is 1 agent if you don't mind mixing context + tool access. Multi-agent if you need strict separation.
The challenge with multi-agent is how these agents communicate if they are silo-ed.
I have not yet solved this, but I'd love to talk to anyone who has.
10. Tying it all together - the systems mindset
In theory, OpenClaw / Gooseworks exposes all these pieces but you still need to have an engineering mindset and stitch them all together, and engineer the system in the right way.
This is often the hardest part.
For example, let's say I want my agent to find me leads by scraping LinkedIn posts for some keyword. Sounds like an easy problem to solve. I wire up my agent to an Apify actor and run an automation, right?
But not really. Because the agent will just send me the same leads every day. No deduping happening. Now if I have the LLM dedupe, it's incredibly inefficient. So I need to make sure that my Apify scraper is ONLY checking the last 24 hours. But the Apify scraper doesn't have a way to filter by timestamp, so now what? Also, the same people tend to post about the same topics a lot.
This can be solved by sending the leads to a CRM and making sure there's a way to track the outreach status there, but the point I'm making is that these are actually engineering problems and require a systems mindset to solve. The example above is probably easier than most real systems.
What I'm curious about:
I imagine a lot of people are figuring this stuff out right now, so I'd love to trade notes:
- What are you trying to get your agent coworker to do? What's the dream scenario 6 months out?
- How do you structure your skills and feedback loops?
- What's your context layer? just a filesystem or anything else?
- Has anyone gotten a good heartbeat-based system working?
- What does your agent check for when it "wakes up"?
Feel free to comment and / or DM me. You can also find me on LinkedIn at https://linkedin.com/in/shivsakhuja
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- [https://x.com/shivsakhuja/status/2038711292073320734](https://x.com/shivsakhuja/status/2038711292073320734)
- [@agentmail](https://x.com/@agentmail)
- [@ListenNotes](https://x.com/@ListenNotes)
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- [http://skills.gooseworks.ai/](http://skills.gooseworks.ai/)
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*导出时间: 2026/4/19 19:15:31*