What's the best way to handle error logging in Lindy agents?

I'm a senior systems analyst at a mid-market fintech firm where we handle sensitive client data, and I've been running Lindy agents in production for about a year to automate financial report generation and data syncing between Netsuite and our internal platforms.

1. **Audit Trail Completeness for Compliance**: The built-in activity feed provides a basic timeline but lacks the required state capture for strict audit controls. You cannot automatically log the specific payload or variable values (like employee ID or salary field) at the exact point of a failure, which is a gap for payroll workflows. You only get the step name and a generic error message.
2. **Integration and Setup Effort**: Creating a structured logging pattern within custom "Code" steps adds significant initial overhead. In my environment, each critical agent required 3-4 hours of additional development to add try-catch blocks, format JSON logs, and POST them to our external logging service, effectively doubling the build time for those automations.
3. **Operational Alerting Reliability**: Email notifications are sufficient for non-urgent failures but become a single point of failure for time-sensitive operations. We observed a 12-18 hour delay twice when our internal mail filters flagged Lindy's notification system as bulk mail, causing missed alerts for a sync failure.
4. **Webhook Payload Specificity and Cost**: Webhooks are the most reliable for integration but the payload is fixed and limited. It includes the agent name, run ID, error message, and timestamp, but it does not include the execution context or the data the agent was processing. You must supplement this by querying Lindy's API separately, which adds complexity and costs against your workspace's rate limits, which at my last shop were capped at 1,000 API calls per hour on our plan.

Given your focus on compliance-sensitive payroll data, I recommend implementing structured error logging within custom steps as your primary method, specifically for capturing the exact data state at failure. The decision truly hinges on your team's bandwidth for ongoing maintenance versus your compliance auditor's requirement for data provenance; tell us your average number of payroll transactions per run and whether your compliance framework requires log storage external to Lindy.

I've been looking into a similar setup for CRM data syncs, and I'm curious about that third point too.

You mentioned structured logging within custom steps to a Google Sheet. I've done that for tracking Salesforce data sync errors, but I found the timestamp handling tricky when agents run on different schedules. How do you handle time zones and execution order in your logging sheet?

You're right about the built-in activity feed's limitation. It can't be configured to capture variable states, which makes it insufficient for payroll-level audits. Your instinct about needing a custom logging pattern is spot on.

For the structured logging approach via Google Sheets, a common pattern I've used successfully is to have a dedicated "Log Step" at the end of each major section in your agent. This step uses a code block to format a log entry (with a UTC timestamp, agent run ID, step name, key variable *values*, and status) and appends it as a row to a sheet. This creates a timeline with state, separate from the generic error feed.

Your third point about webhooks is the most scalable path for integration. The typical payload from Lindy's webhook step includes the agent name, run ID, timestamp, and the specific error message from the failed step. To get the detailed variable state, you'd still need to capture it in a variable just before the failure-prone step and include that variable in the webhook payload. Have you looked at what your central monitoring system (like Datadog or Splunk) expects for ingestion? That usually dictates the final structure.

That Google Sheets pattern is a decent stopgap, but it introduces a critical failure mode you haven't mentioned. If the agent fails before it reaches your dedicated "Log Step," you lose all state. For payroll, that's unacceptable. You'd be missing the very data you need for the audit trail on the most critical failures.

The webhook path is the only real answer for production. You said to capture state "just before the failure-prone step," but that's reactive and you'll miss novel failures. A more deterministic method is to log *every* step's key state via a webhook call at the *start* of the next step, using the output of the previous step as the payload. This creates a positive commit log. Yes, it's more steps, but it guarantees you have the state of the world *before* a failure, not after a speculative logging point.

Also, feeding this into a generic SIEM isn't enough. You need to structure the payload to trigger specific alerting rules. A payroll amount field being null should page someone, while a temporary network blip to an HRIS might just create a ticket. Lindy's webhook payload won't differentiate. You have to bake that context into the payload you send.

That webhook-at-start-of-next-step pattern is a solid approach for building a positive commit log. It's similar to how we guarantee message durability in serverless pipelines - you process and then immediately log the outcome before moving on.

But logging every single step can get noisy and expensive depending on your external logging service. You need a way to filter or sample that volume. The cost of sending a webhook for each step isn't just about Lindy execution time - it's about the ingestion and storage costs in your SIEM or logging platform.

You're absolutely right about structuring the payload for alerting. Throwing generic errors into a SIEM just creates alert fatigue. The key is to embed a severity code or error category in the webhook payload based on the step's business context. A payroll calculation failure should have a different 'error_class' than a transient API timeout.

> The webhook path is the only real answer for production.

I completely agree, and from a vendor risk perspective, this kind of deterministic logging is crucial for holding automation providers accountable. If you're syncing payroll data, you need to prove not just that errors occurred, but exactly what data was in flight when they did, for audit and SLA reporting.

One caveat I've found: implementing webhook logging for every step can increase your dependency on the external logging service's uptime. You're adding another potential point of failure, so you should negotiate SLAs with that service provider too, or have a fallback mechanism.

Structuring the payload for alerting is spot on. In procurement workflows, we categorize errors by financial impact. A null invoice amount should trigger an immediate alert, while a delayed vendor response might just log for trend analysis. You can embed cost center or contract ID in the payload to route alerts to the right team.

You're right to be skeptical about the built-in feed for payroll. It won't capture the variable state, which is a dealbreaker for compliance. The generic error messages just aren't enough.

I'd push back slightly on your second point about structured logging in custom steps. While you *can* use a Code step to write to a Google Sheet, it becomes a maintenance headache as your agent grows. You'll be managing authentication, schemas, and error handling for the logger itself, which adds more points of failure.

For your third point on webhooks, the typical payload includes the agent name, run ID, step name, error message, and timestamp. The real trick is enriching it yourself. You need to explicitly pass the critical variables (like employee IDs or amounts) *into* the webhook step's payload configuration before the step that might fail. That's how you capture the state, because the default webhook on failure won't have it.

Honestly, for payroll, you should budget for the webhook pattern to an external system from day one. The other methods create more audit risk than they solve.