An okki-go Agent Workflow Taught Me What RevOps Should Evaluate in a Hard Bounce Rate

I'm the person at okkigo who says 'not yet' when an AI agent workflow isn't ready to ship. My title is Quality Manager, which is a polite way of saying I review workflows before customers use them and reject the ones that would make a founder look careless.

Roughly 200 workflow configurations cross my desk every year. Maybe 180, I'd have to check the tracker. I check sequence logic, suppression rules, intent triggers, enrichment coverage, and where the human actually gets to make a decision. Most issues are boring: a dead Salesforce field, a missing opt-out list, a step where the agent is allowed to infer something it shouldn't.

Then there are the lessons that stick. This one came from a hard bounce rate.

The day a 'validated' list bounced

In Q3 2025, I was reviewing a batch of okki-go agent workflows built for founders. One workflow looked like the kind of thing I would have asked for: it started with a clear ICP, pulled intent from a few sensible sources, enriched contacts through the waterfall, ran email validation, and sent everything through a human review step before launch.

The problem showed up after launch. The founder's RevOps lead flagged a roughly 7% hard bounce rate in the first seven days. My first instinct was to blame the sending infrastructure. That instinct was wrong.

When we pulled the records, the bounces weren't spread evenly across the campaign. They were concentrated in a segment that the verification source had labeled 'risky' rather than 'invalid.' The label felt harmless enough. The founder had configured the workflow to suppress invalid emails and keep everything else, because they didn't want to lose reach. The workflow did exactly what it was told.

The experience broke one of my assumptions: I assumed that if email validation was running, the contacts that made it into a sequence were reasonably safe to message. But validation isn't a single stamp of truth. In many cases, a verification tool can only tell you that an address might exist. Catch-all domains, for example, can accept validation messages even when specific mailboxes don't exist. What should have been a policy decision — how much risk are we willing to accept? — was left to a default setting.

What should revenue operations teams evaluate in hard bounce rate?

When a hard bounce rate gets high, the first question shouldn't be 'how do we lower this number?' It should be 'which workflow decision created this number?'

If you're evaluating a hard bounce rate inside an AI outbound program, here are the checks I now use:

1. Segment the bounces before judging the number. A 7% aggregate bounce rate can hide a 1% rate on one list and a 15% rate on another. Hard bounces usually point back to a specific source, an old enrichment export, or a risky segment, not to your entire sending domain.
2. Ask what your validator actually means by 'valid.' Has the email been checked at SMTP level recently, or is it just labeled deliverable because the domain accepted a test? RevOps teams should ask whether the validation result has a verified_at timestamp and whether the workflow can re-check a record if the send is delayed.
3. Make a conscious decision about risky and catch-all buckets. If you include moderately risky contacts because you want more volume, that is a valid RevOps decision. But it needs to be deliberate and it needs a threshold. Don't let an AI agent silently decide which risk level is acceptable.
4. Treat email validation as a stage, not a one-time event. In an okki-go agent workflow, validation should happen after enrichment, not just before it. If a contact sits in a list for weeks before the agent contacts them, the original validation result becomes stale. Re-validation at the point of send matters more than validation at the point of import.
5. Decide what happens after a bounce. The workflow should suppress the address, flag the source, and make the data problem visible. If a hard bounce just gets quietly removed from the sequence, the same bad contact source can keep producing the same bad rate next quarter.

Where my review checklist changed

The workflow that caused the 7% bounce rate wasn't broken in a traditional sense. It followed instructions. The gap was that nobody had reviewed the assumptions underneath the instructions: what the verification statuses meant, how much risk was acceptable, and who was responsible for making that call.

Now I review workflows with different eyes. I want to know what email validation allows through, not just that it exists. I want to see whether the human review step shows the verification note next to each message, or whether the reviewer is just checking copy while the data quality issue sits invisibly in the background.

Since Google and Yahoo's bulk sender requirements took effect in February 2024, deliverability is more visible and more fragile than it used to be. Hard bounces matter because they are a leading indicator of a sender reputation problem, not because one bounce itself is catastrophic. By the time you see a high bounce rate in a dashboard, the workflow already made the mistake.

No email validation solution can promise a zero percent bounce rate. At some point, a database goes stale and a mailbox gets deactivated. What revenue operations teams should evaluate in a hard bounce rate isn't just the number. It's whether the workflow is designed to catch issues early, make risk visible, and improve the next campaign instead of repeating the same mistake.

The okki-go workflow for founders is powerful partly because it removes so much manual work from outbound. But that's exactly why quality standards matter more, not less. When an agent can generate, enrich, and send at scale, every unchecked assumption gets amplified. The human in the loop is only valuable if they're reviewing the right thing.