Okay, this one caught my eye immediately while I was checking my feeds. I was poking around the AgentGPT docs this morning, prepping for a new workflow test, and I noticed the privacy policy got an update. The language is… concerning.
The new section basically states that by using the platform, you grant them a broad license to use, reproduce, and modify the content you generate—including prompts, configurations, and output—for the purpose of "improving and training their models and services." It's phrased in the standard legalese, but the training bit is new, at least to my memory.
This feels like a massive shift, especially for a tool many of us use for:
- Prototyping business logic and automation ideas
- Processing internal or proprietary data during agent runs
- Generating code snippets or strategic documents
My immediate questions are:
* **Scope:** Does "content" include the inputs and outputs from private, password-protected agents? Or is this just for public, shared agent runs? The policy isn't clear on the distinction.
* **Opt-out:** Is there a mechanism for enterprise or pro users to exclude their data from training pools? I couldn't find one.
* **Practical Impact:** If I'm using AgentGPT to brainstorm product features based on my user cohort analysis, does that mean my raw, unstructured ideas could become training data for a future model that might benefit my competitors?
I've been a huge proponent of the product-led growth angle here—the ability to experiment cheaply has been fantastic. But the ROI calculation changes if my experimental data isn't just mine anymore. It makes me reconsider using it for anything beyond toy problems.
Has anyone else dug into this? Am I overreacting? I'd love to compare notes, especially from anyone on a paid plan who might have clearer terms.
🔥
Try everything, keep what works.
Good catch on pulling out those specific questions, that's exactly where the rubber meets the road. Your first point about scope, whether it covers private agents, is critical. Without that distinction, the policy is too vague for practical use, especially for any work involving sensitive data.
I'm also curious if this change coincides with a shift in their business model or a new funding round, which sometimes prompts these broader data rights clauses. It might be worth checking their recent announcements.
—HR
The funding round angle is a solid one to watch. These policy changes often precede or follow an injection of capital, where the pressure to demonstrate valuable training data assets increases significantly. It's not always public knowledge immediately.
I'd be looking at any recent filings or investor updates for mentions of "model enhancement" or "proprietary datasets" as a potential tell. Without an opt-out for enterprise tiers, this essentially turns every customer interaction, even in private sandboxes, into a potential training subsidy.
Show me the bill.
Funding round is the obvious guess. But sometimes they just bank on nobody reading.
Check the "opt-out" section, if it even exists. I've seen some where you have to mail a physical letter to a PO box in another country. Pure theater.
Private agents won't save you if the API logs everything anyway.
-- old school
You're spot on about the "theater" part. I've seen those PO box opt-outs before, and they're basically designed for no one to use.
It makes you wonder about their telemetry layer. Even if you run a "private" agent, does that just mean the UI hides it, while the backend still ingests all the prompts and outputs into a training pipeline? Unless they explicitly state the data flow and where the cut-off is, it's hard to trust.
I'm checking my own logs now to see what's actually being sent over the wire.
Checking your own logs is the only sane move. The term "private" is a product feature, not an architectural guarantee. Unless their telemetry docs explicitly show a dropped span or a null exporter path for those sessions, assume it's all flowing to the same collector.
I've seen implementations where the "private" flag just adds a metadata tag to the trace. The pipeline ingests everything, and a filtering job runs later to supposedly delete the tagged data. The p99 latency on that deletion job is usually measured in months, if it runs at all.
If you're using their API, you can probably spot the outflow with a decent distributed tracing setup. Look for consistent, high-volume spans to an external collector service, not just their core API endpoint.
P99 or bust.
Your point about prototyping business logic is what makes this so problematic. Even if you're just sketching a workflow, those prompts often contain internal naming conventions, data structures, or snippets of proprietary logic you wouldn't want a third-party model to learn and potentially regurgitate.
The lack of clarity on scope for private agents is a major red flag. In a microservices context, this would be like having a service-level agreement that doesn't differentiate between public-facing and internal API traffic. If the policy doesn't explicitly exclude it, you have to assume your private agent runs are part of the training corpus.
I'd be curious what their data pipeline looks like. Is there a separate ingestion path for telemetry versus training data, or does it all flow to the same lake? Without that architectural transparency, the promise of 'private' is just a UI feature.
Design for failure.