AI agents / Self improvements
Turn recurring failures
into lasting improvements.
Refund failures share one cause: missing tool verification. A prompt change is ready for review.
Find the shared cause behind failed journeys. Draft targeted fixes, test them before release, and measure whether customers get better outcomes.
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Finding the failure is only half the job.
Teams collect issues without a clear fix or owner. The same failures repeat, and nobody knows whether the last change helped.
Different conversations keep failing for the same reason.
Your team cannot tell whether the prompt, knowledge, or tools need to change.
Fixes ship without a clear link to customer outcomes.
Fix the root cause.
Measure the result.
Find the changes worth making first.
Cluster failed journeys by root cause. Rank improvements by affected customers, resolution impact, effort, and risk so the team has a focused backlog.
Recurring refund failures point to one missing verification step.
64% Validated36% In review
151Track the same failure after the fix
Turn human recovery into better AI behavior.
Use successful human resolutions to draft prompt and knowledge changes and identify missing tool or workflow capabilities. Your team reviews what changes.
What should we change first?
ChatProposed fix for human review:
Cause: tool result not checked
Change: verify refund confirmation
Test: refund and timeout scenarios
Measure: resolution after release
Keep the improvements that work.
Test proposed fixes in simulations, then monitor outcomes after release. Compare resolution, handoffs, customer effort, and regressions to confirm the impact.
Slack integrationRulebaseAPP8:01 AMThe verification fix is ready for review. Run the refund scenarios, approve the change, and monitor repeat contact after release.
Close the loop from failure to improvement.
What it learns from
Evidence of what failed and how a human recovered the journey.
Failed interactions
Human resolutions
Agent traces
Tool errors
Policy guidance
Outcome historyWhat it proposes
Targeted changes tied to the cause, ready for your team to review.
Prompt fixesKnowledge updatesTool requirementsRouting changesHandoff guidanceTest scenariosWhat you measure
The effect of each approved change on live customer journeys.
Successful resolutionAvoidable handoffsCustomer effortRegressionsCommon questions
How does Rulebase identify AI agent improvements?
Rulebase compares failed AI journeys with successful human resolutions to find recurring gaps in prompts, knowledge, tools, and workflows. It turns those findings into proposed changes for the team to review and test.
How do you know an agent change helped?
Compare the same scenarios before and after a change, then check production outcomes for the affected journeys. Look at successful resolution, avoidable handoffs, repeat failures, and regressions. A better test score alone does not establish that customers had better outcomes.
Follow each fix from testing to monitoring
Test proposed changes before release →
Compare agent versions against the same customer scenarios and inspect regressions.
Monitor customer outcomes after release →
Check whether the same failures recur and whether customers still need human help.
Make your agent better with every fix.
Connect recurring failures to reviewed changes, tested releases, and measurable customer outcomes.
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