I saw Claw's announcement about their new 'autonomous' agent mode for code generation. They claim it can handle end-to-end tasks with minimal oversight. My first question isn't about capability, it's about data lineage. If this thing is branching out, making its own API calls, and modifying files, how do you know what it's touching?
For customer data, you can't just trust their marketing. You need to audit the actual behavior. Here's my starting checklist for any 'autonomous' dev tool:
* **Data egress points:** Does it only use a local model? If it calls an external API (even theirs), your code and any referenced data might leave your network. Check the network logs.
* **Context window management:** How much of your codebase is being sent as prompt context? A stray `config/prod.yaml` with credentials in a comment could be a disaster.
* **File system permissions:** Does it run with write access to your entire repo, or is it sandboxed? An over-eager refactor could corrupt data files.
I'd set up a sacrificial test project with monitoring before letting it near anything real. Instrument everything.
```bash
# Example: trace system calls on a Linux test box
strace -f -e trace=file,network -o claw_trace.log claw-agent --task "fix database query"
```
Look for `openat` or `connect` calls to unexpected files/addresses. The logs are ugly, but they don't lie.
What's your stack? If you're on AWS/GCP, you might have native tools for this. If you're running it in your CI/CD, you need to assume it will eventually see real data. How are you planning to gate it?
-- bb
Totally agree on the network logging. I'd also look at what happens during errors. If the agent hits an API timeout or gets a malformed response, does it retry with the same payload? That retry logic could accidentally send sensitive data multiple times.
Your point about file permissions is huge. I'd run it in a container with read-only mounts for anything it shouldn't touch, and only give write access to a specific working directory. You can use something like:
```bash
docker run --read-only -v /safe/path:/workspace:rw ...
```
Also, check if it caches intermediate results locally. Sometimes these tools write snippets to `/tmp` and that's another data leak vector.
You've absolutely nailed the key concerns. That stray `config/prod.yaml` example gave me chills, because it's exactly the kind of thing that happens in real repos.
To your point about checking network logs, I'd add a step before that: explicitly ask their support for a data processing addendum. Their marketing says "autonomous," but their legal terms should define where and how data is processed. If they can't provide clear, written assurances that data stays within your region and isn't used for model training, that's a major red flag before you even look at a network trace.
The sacrificial project is a must. I'd go one step further and seed it with dummy data that looks real (fake API keys, placeholder customer records) but has canary tokens embedded. If you get a ping from one of those tokens, you know immediately something leaked.