Custom for every customer. Better every week.
Kayba finds what broke for each customer before they complain, drafts the fix, and tests it against every other customer. You approve what ships into that customer’s playbook.
Your customers find the bugs before you do.
You run one agent for many customers. Issues hide in traces nobody can read, every customer wants different behaviour, and your best people end up debugging the agent instead of growing the business.
What broke is buried in traces
One person on the team can find an issue in ten minutes. For everyone else it takes hours, so mostly nobody looks.
Every customer wants a different agent
Conflicting demands, exception lists, hand-tuned prompts per tenant. Today this scales one way: hiring.
“I fix this, something else breaks”
A tweak for one customer regresses another. So uncertain changes don’t ship, and known issues stay.
Kayba finds it, explains it, drafts the fix.
You keep the pen.
The same loop on every trace, for every customer. Approved fixes land in that customer’s playbook and compound.
Know what broke before the customer does
Issues surface as customer complaints. Then someone reconstructs what happened from 200,000 lines of logs.
Kayba reads every trace as it lands and flags failures with the evidence attached: which customer, how often, trending or not. Sentry, but for agent behaviour.
- Acme · refund promised outside policyfound by kayba12×
- Northpeak · AI status disclosed against policyyour check8×
- CoLearn · same answer loopingfound by kayba5×
- Relay · outdated rate card quotedyour check3×
Get the fix drafted, never auto-applied
Diagnosis is bottlenecked on the one person who can read the traces. Everyone else queues behind them.
Kayba pinpoints the root cause and drafts the fix in plain English. It never edits your agent on its own. Your team keeps the pen.
@kayba Acme says the agent promised a full refund on a 60-day-old order. Can you scope?
Found 4 traces in the last 24h matching the complaint. Three ended with an out-of-policy promise.
The agent checks the refund amount, but never the order date.
New rule for Acme’s playbook: confirm the order is within the 30-day window before promising a refund. Ready to test against every customer’s checks.
Ship it without fearing the rest
“I fix this, and something else gets broken.” So uncertain changes don’t ship, and known issues stay.
Every expectation becomes a named check. Each fix runs against the customer’s checks, and every other customer’s, before it ships.
- Follows the 30-day refund policy0/24 traces
- Answers only from the knowledge base0/24 traces
- Offers a human when asked0/24 traces
- Confirms identity before account changes0/24 traces
- Never invents discounts0/24 traces
Every approved fix compounds into the playbook
Per-customer behaviour lives in hand-tuned prompts and tribal knowledge. Scaling it means hiring.
Approved fixes become rules in that customer’s playbook. Every Friday, Kayba drafts the report you send them as proof: issues caught, rules added, score up.
- Confirm order date before refundsfrom 38 traces
- Never quote delivery dates from memoryfrom 12 traces
- Escalate after 2 failed answersfrom 47 traces
- Offer a human handoff on frustrationfrom 9 traces
- Address customers by first namerequested by Acme
- Acme · refund promised outside policyfound by kayba12×
- Northpeak · AI status disclosed against policyyour check8×
- CoLearn · same answer loopingfound by kayba5×
- Relay · outdated rate card quotedyour check3×
A complaint becomes a shipped fix in five minutes.
The same complaint used to mean hours of log-digging by the one person who can read them. Here’s the whole loop, timestamped.
“The agent promised a refund we don’t offer.” Acme, in your Slack.
4 matching traces found, evidence attached.
The agent never checks the order date.
Suggested, never auto-applied, and tested against every customer first.
One new rule in Acme’s playbook. Nobody else’s behaviour moves.
On Friday, Acme’s weekly report shows the issue, the rule, and the eval score going up. The playbook isn’t just your tooling. It’s the proof you show each customer that their agent is getting better.
Built by researchers. Verified on benchmarks
and production.
The team behind Kayba published the agent-learning research this product is built on, and benchmarked it in the open on τ2-bench before asking anyone to trust it with their customers.
Kayba reads traces from Langfuse, MLflow, Logfire, or any OpenTelemetry source. No SDK rewrite, no exports. Your data stays where it is.
| Baseline | Kayba | Improvement | |
|---|---|---|---|
| pass^1 | 41.2% | 55.3% | +34.2% |
| pass^2 | 28.3% | 44.2% | +56.2% |
| pass^3 | 22.5% | 41.2% | +83.1% |
| pass^4 | 20.0% | 40.0% | +100.0% |
τ2-bench is a real-world agent benchmark by Sierra Research.
Stop debugging your agent.
Get back to growing the business.
“Every day I get at least 200,000 lines of logging. It’s impossible to go through all that.”
Kayba takes the trace-reading, the regression anxiety, and the per-customer tuning. Your customers see proof every Friday that their agent is improving, and your week goes to onboarding the next customer instead of debugging the last one. Bring a week of traces and we’ll show you. 20 minutes, no slides.