Set up a cautious store-workflow agent in OpenClaw
A developer spent 100+ hours refining an OpenClaw setup until it could handle one store workflow reliably.
- What they did
- They built the system inside OpenClaw on a locked-down VPS and used Telegram for the chat layer instead of Discord. Kimi K2.5 did the main reasoning, while a lower-cost model handled heartbeat checks and background work. To support it, they added markdown-based memory links, reusable sub-agent patterns, and decision-tree instruction files that stayed closed unless needed.
- How it went
- They say the agent became dependable enough to use after 100+ hours and more than 200M tokens. The downsides were steep token usage, ongoing background expense, and harmful mistakes when guardrails were too weak, including deleted files and one case that wiped an entire VPS.
- Tips
- Keep permissions narrow and isolate destructive actions from everyday steps. When something breaks, expect to restart the gateway yourself and swap in a stronger model for debugging.
Try it yourself with OpenClaw
Help me design an agent for [store platform] that manages one specific workflow: [inventory review / supplier email drafts / product listing updates]. Use [main model] for the core reasoning and [cheaper model] for heartbeats or routine background jobs. Keep any long-term memory in linked markdown files, and only load extra decision rules when they are relevant. Before any message is sent, any listing is edited, any file is removed, or any payment is made, stop and ask for my approval. Run a test pass on [3 sample products], then tell me what succeeded, what failed, and what it tried to do.
Fill in the [brackets]. Agents can make mistakes, so review anything before it is sent, paid or deleted.
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