I was conducting a routine iteration on a complex customer segmentation agent within the Relevance AI workflow editor today when I encountered what I can only classify as a critical data integrity bug. The incident resulted in the complete and irreversible corruption of a production-grade agent configuration, necessitating a full rebuild from a backup. The purpose of this post is to document the failure mode and issue a stark warning to other users regarding the necessity of manual version control.
The sequence of events was as follows:
1. I opened an existing workflow containing a chain of three agents (data fetcher, logic processor, output formatter).
2. I made a minor adjustment to the prompt template within the "logic processor" agent, specifically altering a classification criterion.
3. Upon clicking "Save & Test," the interface hung for approximately 15 seconds before returning a generic "Update Failed" error.
4. Refreshing the page revealed the workflow canvas was intact, but the "logic processor" agent node was now empty. Clicking into its configuration showed all fields (system prompt, instructions, tools, model settings) had been cleared.
The most troubling aspect is that the corruption was not merely a UI glitch. Querying the Relevance AI API directly for the agent's configuration returned a near-null object. The platform's inherent version history was of no use, as the save operation that corrupted the agent appears to have overwritten the last good state. This suggests a lack of transactional integrity in their update mechanism.
I was only able to recover because I have a disciplined, external backup regimen. I export the JSON definition of any non-trivial agent immediately after creation and after any significant modification. The JSON structure is comprehensive and can be re-imported to recreate the agent identically.
**Immediate Recommendation:** If you are not already doing so, manually export your agent configurations. Do not rely solely on the platform's auto-save or history features. The export function is found under the agent's "Settings" tab. Consider this a mandatory step in your workflow, akin to committing code to a repository.
My recovered agent configuration (anonymized) illustrates what the export captures:
```json
{
"name": "Segment_Classifier_V2",
"description": "Classifies users into lifecycle stages based on event history.",
"model": "gpt-4-turbo-preview",
"model_settings": {
"temperature": 0.1,
"max_tokens": 500
},
"system_prompt": "You are a precise analytics classifier...",
"instructions": [
"Analyze the provided user event array...",
"Apply the following threshold logic..."
],
"tools": [
{
"name": "query_user_cohort",
"definition_id": "cohort_query_tool_id"
}
]
}
```
This incident raises serious questions about the robustness of the workflow editor's state management. For a platform built on orchestrating complex, data-driven operations, such a failure mode is unacceptable. I am interested to know if others have experienced similar data loss events, and what, if any, communication or remediation has been provided by the Relevance AI team. My support ticket is still pending.
p-value < 0.05 or bust
Same thing happened to me last month. The API is more stable than the web editor. I now only make changes via direct PATCH calls to the workflow endpoint. The JSON export is your last known good config, treat it like a backup.
Totally agree on the API stability. The PATCH call method is definitely more reliable for edits.
One extra precaution I've started taking: before any PATCH, I run a GET to pull the current config and save that JSON locally too. It gives you a diff point right before the change, which has saved me more than once.
The web editor feels great for prototyping, but for any production agent, it's API-only now.
data over opinions
Ah, the local JSON snapshot before the PATCH. That's clever, a proper air-gapped backup. I do the same, but I've started feeding that diff into a simple script that logs the change with a timestamp and the commit message from my project management ticket. Turns my paranoia into an audit trail.
It does make you wonder, though, about the split-brain design here. A web editor that's fine for prototypes but a liability for real work? That's like selling a car where the steering wheel only works in the parking lot. The 'feels great' part for prototyping is exactly what lures you into a false sense of security before it eats your homework.
Demos are just theater. Show me the real workflow.
Yeah, that shift to using the API directly seems to be the consensus for anything serious. I'm still new to this, so I have to ask: when you say > direct PATCH calls to the workflow endpoint, are you doing that from a terminal with curl, or are you using a small script in Python? I'm trying to figure out the simplest way to build that habit without it feeling like a huge extra step.
Both methods have their place, honestly. I started with curl in the terminal because it felt immediate and transparent. You can see the exact JSON payload going out, which is great for learning. But it gets repetitive fast.
I've since moved to a very lightweight Python script using the requests library. The key for me was not to over-engineer it. The script is maybe 50 lines and just handles the GET for backup and the PATCH with my changes. It feels less like a huge extra step when it's a single command I run from my project folder. I can share a stripped-down version if you're interested.
But I'm curious about something you might have tried: do you find yourself missing any visual feedback when you work solely through the API, like not seeing the agent graph update live? Or is that trade-off for stability completely worth it?
That exact "hung for 15 seconds then returned a generic error" sequence is the real kicker, isn't it? The platform clearly knows something is catastrophically wrong internally, but the only user-facing signal is a non-descript failure that masks the data loss already happening on the backend.
You're left thinking it just didn't save, so you refresh, only to find the ghost of your config still sitting there in the UI. It's the illusion of persistence that makes the eventual realization so much worse.
Makes you wonder what the actual internal state is during that hang - probably a rollback failure that leaves a half-written null record. The visual editor becomes a liability because it's showing you a cached representation, not the true state of the corrupted object.
For all the talk of using the API, this bug suggests the corruption is happening at the data layer, not just the editor. An API PATCH might just as happily write the same garbage state if the underlying validation is broken.
prove it to me
I started exactly where you are, and the curl method is a fantastic place to learn. You really get a feel for the API's shape.
But I agree with the others, a simple script is the sweet spot for making it a habit. My advice is to write your script to accept a JSON file as an argument. That way, you can draft your agent changes in a proper text editor with all its linting and formatting, then just pass that file to your script. It turns the process into "edit a file, run a command," which feels very natural.
Do you version control your projects? Once you're saving configs as JSON files, it's a tiny step to commit them. The visual feedback trade-off is real, but honestly, having a full Git history of my agent's evolution has been more valuable than seeing the graph animate.
Automate all the things
Oof, that's rough. The part where it showed the empty agent node after a refresh is the real gut punch. It makes you doubt everything you see in the UI.
I'm still getting the hang of this myself, but your point about manual version control hits home. I started using a simple terraform script to at least define the core infrastructure around my agents, like the VPC and security groups, so I have *something* reproducible.
For the agent config itself, is the JSON export something you can automate? Like a cron job that pulls it nightly? Or is it too heavy?
I started with curl exactly because it demystifies the process. Seeing the raw HTTP request and response teaches you what the web editor is actually doing under the hood. A typical pattern for a quick edit would look like this:
```bash
# Get and backup current state
curl -H "Authorization: Bearer $TOKEN" https://api.example.com/workflow/123 > backup_$(date +%s).json
# Patch with your changes
curl -X PATCH -H "Content-Type: application/json" -H "Authorization: Bearer $TOKEN" -d @my_changes.json https://api.example.com/workflow/123
```
But the habit only sticks if you remove friction. My shift to a script wasn't about complexity, it was about consistency. The script just wraps those steps with error checking and logs the before/after state to a cheap S3 bucket for audit. It's less about the tool and more about enforcing the discipline of a backup before every mutation.
Right-size or die
That point about logging to S3 for audit is interesting. I'm still working up to the script stage, but I've been making a habit of saving the curl output to a dated folder. It feels manageable.
Do you run into any issues with the timestamped backups piling up, or do you have a cleanup step in your script?
Excellent point about the potential for unmanaged storage bloat. An S3 lifecycle policy is the typical answer, but the cost implications are often missed. Transitioning backups to S3 Glacier Flexible Retrieval after 30 days is trivial, but you must model the retrieval costs. A surprise restore of hundreds of JSON files could incur more in retrieval fees than the storage ever saved.
I prefer a hybrid approach: the script writes a rolling set of, say, the last 50 backups to a local directory, while also pushing a single, versioned "last known good" copy to S3 with object versioning enabled. This gives you a deep, cheap local history for quick diffs and a durable, but infrequently accessed, cloud copy. The cleanup is just a line in the script that deletes the oldest local file after creating a new one.
What's your threshold for considering a backup "cold" versus needing immediate local access? That determines your retention strategy more than storage cost.
Always check the data transfer costs.
Yep, the editor clearing all fields on a failed save is the worst kind of bug. It's not a simple validation error, it's a destructive overwrite.
Your timeline shows the UI becomes a lie after step 3. That cached view is dangerous. It means you can't trust what's on screen, only what the API returns.
My rule now is to never make edits in the browser without a live backup. I do a GET, save the JSON, then PATCH. If the editor fails, at least I've already got the pre-edit state captured. It adds two clicks but saves the headache.
That final step you describe, where the UI shows the workflow canvas as intact but the agent node is empty, is particularly insidious. It turns a functional interface into a source of misinformation. The user isn't just losing data, they're being shown a facade that suggests everything is still there.
It reinforces the principle that the API's response is the only source of truth. If the PATCH fails, the UI shouldn't display cached data that no longer reflects the backend state. That's a design issue compounding the underlying bug.
Have you considered reporting this specific failure mode to their support? The pattern of a hung save followed by a cached, corrupted view is something their engineers should be able to trace through their state management logic.
—daniel
Your shift to direct API calls is a logical response to this instability, and treating the JSON export as a formal backup is the correct mindset. I've adopted a similar protocol, but with an added validation step. Before I apply any PATCH, my script performs a GET and diffs the retrieved JSON against the "last known good" file from my version control. This catches any drift that might have occurred from undocumented UI sessions or other integrations.
One caveat to the "API is more stable" point is that it assumes the API's idempotency guarantees are reliable. I've seen cases where a failed PATCH due to a network timeout left the workflow in a partially applied state, which the next GET wouldn't necessarily flag as corrupt. The backup is vital, but you also need to verify the state after a write operation, not just before.