How We Built an AI Chief of Staff with OpenClaw

We have been running OpenClaw -- an open-source, self-hosted AI gateway -- for about a week as what we internally call our AI Chief of Staff. It lives in Slack, keeps track of everything happening across the business, and handles the kind of work that usually falls on a founder's plate because there is nobody else to do it.

This post walks through how we set it up, what it actually does for us, and what we learned.

What OpenClaw Is (Quick Context)

OpenClaw is a self-hosted gateway that connects AI models (Claude, GPT, Gemini) to messaging apps. One process runs on your server and bridges Slack (or whatever you use) to an always-on AI agent with access to tools, files, browser, and code execution.

It is open source, MIT licensed, and self-hosted. It was originally called "Clawd Bot" before the project went open source as OpenClaw.

What matters for this post: OpenClaw gives you the building blocks to create something that behaves less like a chatbot and more like a team member.

What Our AI Chief of Staff Actually Does

Before getting into how it works, here is the concrete list of real tasks it handles for us at Waldium.

GTM and growth

Tracks new signups and flags high-signal ones: "a YC company just signed up, they have 200 employees and a docs site"
Monitors GEO report requests (our lead gen tool that shows companies their AI search visibility) and drafts follow-up emails for promising leads
Watches for YC deal page submissions and pings us to follow up while the lead is warm
Reviews content performance weekly, which posts are getting traffic, which are surfacing in AI search results, where the gaps are
Tracks MRR, signup trends, and churn signals so we always have a current snapshot without opening a dashboard

Content pipeline

Reviews content recommendations from our AI system and suggests which topics to prioritize based on what is performing
Keeps track of which customers have active scheduled publishing and whether their content quality is holding up
Flags when token costs are spiking on a specific model tier so we can adjust

Customer success

Watches for new signups and checks if they completed onboarding. If someone signed up three days ago and has not created a post, that is a signal
Tracks custom domain DNS verification and flags domains that have been stuck pending, this is a common friction point for new customers
Monitors webhook delivery health per customer so we catch integration issues before they file a support ticket
Drafts onboarding check-in emails for customers who just set up their blog

Daily operations

Morning briefing in Slack: overnight signups, any billing issues (failed Stripe payments), deployment status, content generation stats
Keeps a running log of decisions like pricing changes, feature prioritization, GTM experiments, so nothing gets lost between conversations
Prepares weekly summaries: what shipped, what is in the pipeline, key metrics, and anything that needs a decision

Strategic prep

Before a customer call, pulls together their account context: when they signed up, what plan they are on, how many posts they have published, whether they use the knowledge base
Before a demo, summarizes the prospect's website and suggests talking points based on their industry and tech stack
Tracks competitive moves, when we ask it to research how competitors handle pricing, AI generation, or content distribution, it spawns background workers and comes back with a summary

How It Works: Three Pillars

1. Memory That Carries Context

The worst thing about using AI as an assistant is re-explaining everything every session. OpenClaw fixes this with workspace-based memory -- plain Markdown files on disk that the agent reads and writes.

Daily logs (memory/YYYY-MM-DD.md): What happened each day. New signups, decisions made, features shipped, issues flagged. The agent writes these automatically.

Long-term memory (MEMORY.md): Durable facts about the business. "Pro plan is $117/month," "GEO reports are our primary lead gen channel," "Vercel cron handles scheduled post generation on 5-minute intervals," "Firecrawl extracts branding during onboarding." This builds up over time and means the agent understands how Waldium works without being re-briefed.

Semantic search: Memory files are indexed with embeddings. Ask "what did we decide about the enterprise pricing?" and it finds the right note even if the wording does not match.

Pre-compaction flush: Before a long conversation gets summarized to fit the context window, OpenClaw runs a silent step to persist anything important to disk first. Nothing gets lost.

After a week, the agent already knows our pricing tiers, our tech stack, our GTM motions, our key customers, and the decisions we made along the way. That context compounds fast.

2. Proactive Awareness

A Chief of Staff does not wait to be asked. They check in, catch dropped balls, and surface what matters.

Heartbeats run every 30 minutes. The agent checks a small HEARTBEAT.md checklist tuned to what matters for our business:

# Heartbeat checklist

- Any new GEO report requests? Flag high-value companies.
- New signups that have not completed onboarding in 48 hours?
- Stripe payment failures or subscription changes?
- Batch generation jobs stuck or errored?
- Custom domains pending DNS verification for more than 24 hours?
- If it is Monday morning, prep the weekly metrics snapshot.

If nothing needs attention, it stays silent. If something matters -- a promising lead just requested a GEO report, a payment failed, a customer's domain setup is stuck -- it pings us in Slack.

Cron jobs handle scheduled deliverables:

Morning briefing (7am): Overnight signups, errors, content generation stats, any Stripe events. Delivered to Slack so we start the day knowing the state of things.
Friday wrap-up (5pm): What shipped, key metrics, open items. Useful context for the weekend.
Monday metrics (9am): Weekly snapshot -- MRR movement, signup count, churn, content volume, model costs. The kind of thing you need for investor updates but never have time to pull together.

Active hours are configured so it does not interrupt outside of work time.

3. Multi-Agent Delegation

One agent doing everything gets noisy. We split ours into two:

main -- Claude Sonnet, handles day-to-day Slack conversations. GTM questions, customer context lookups, drafting emails, decision logging.
ops -- Bound to our #ops Slack channel. Watches for deployment issues, error spikes, and infrastructure noise. Cheaper model, focused scope.

The real power is sub-agents. The main agent can spawn background workers for parallel tasks. Things we have used them for in the first week:

"Pull together account context for tomorrow's demo with [Company X]" -- sub-agent researches their site, summarizes what they do, and drafts talking points
"Research how three competitors price their AI content generation" -- three sub-agents run in parallel, results arrive while we keep working
"Draft a changelog entry for the webhook retry improvements we shipped" -- sub-agent writes it in the background
"Check which of our top 10 customers by post volume are actually using the knowledge base" -- sub-agent queries and summarizes

Sub-agents run in their own sessions, use cheaper models to keep costs down, and auto-archive when done. The main conversation stays responsive.

What We Learned (After One Week)

It has only been a week. But some things were clear almost immediately.

What changed:

Mornings went from "open five tabs and figure out what happened" to "read the Slack briefing and act"
We actually follow up on GEO report leads now because the agent flags them and drafts the email
Decisions are logged automatically -- pricing discussions, feature prioritization, GTM experiments. No more "wait, did we decide that?"
Customer issues get caught before they become support tickets -- stuck domains, failed payments, broken webhooks
Investor update prep went from a monthly scramble to "just read the Monday metrics snapshots"

Early lessons:

Start with memory. The biggest value in the first few days was not automation -- it was having an assistant that understands your business. Spend time on MEMORY.md and let the daily logs accumulate. Everything else gets better once the agent has context.
Tune the heartbeat checklist for your GTM. A generic checklist is useless. Ours is specific to what matters at Waldium right now: GEO report leads, onboarding drop-off, content pipeline health. Write yours around the balls you are currently dropping.
Sub-agents are the closest thing to delegation. We did not expect to use them much. Turns out "go figure this out in the background" is exactly how you want to interact with a Chief of Staff. Research, prep, drafting -- anything that takes more than a few minutes but does not need your real-time attention.
The "Chief of Staff" frame matters. When you treat the AI as a colleague with a role -- not a tool you query -- you naturally give it better instructions and get better results. Write a SOUL.md that describes who it is: "You are the Chief of Staff at Waldium. Your job is to keep the founders focused on what matters and make sure nothing falls through the cracks."
One week is enough to know. You do not need months. If you set up memory and a morning briefing on day one, you will know by day three whether this changes your workflow.

Getting Started

If you want to try this:

Install: curl -fsSL https://openclaw.ai/install.sh | bash
Onboard: openclaw onboard --install-daemon
Connect Slack: openclaw channels login
Open the dashboard: openclaw dashboard
Start chatting -- tell it about your company, your GTM, and what keeps falling through the cracks.

Docs at docs.openclaw.ai. Source on GitHub.