01
Start local
Treat Indonesian language and operating context as part of the system behavior, not a translation layer added at the end.
About Verune
Helping AI agents earn bounded autonomy through evidence.
Verune Labs is building an independent evaluation and assurance practice for consequential agent workflows, beginning with the language and operating complexity of Indonesia.
The thesis
A convincing conversation is not the same as a reliable operation.
Agent failures emerge where language, local convention, policy, tools, and recovery meet. Verune makes those intersections observable before teams expand into real customer workflows.
02
Show the evidence
Tie every important claim to a reproducible interaction, observed state, and consequence a team can inspect.
03
Support the decision
Optimize the evaluation for a concrete launch or remediation decision rather than an abstract benchmark score.
Operating principles
Small scope. Deep context. Honest conclusions.
01
Outcome before coverage
Begin with the workflow outcome and risk boundary, then choose the scenarios that make the decision clearer.
02
Evidence before confidence
Prefer captured behavior and system state over claims about what the agent should have done.
03
Limits stay visible
State what was not tested and avoid presenting a bounded evaluation as universal proof of reliability.
04
Findings must travel
Structure evidence so product and engineering teams can reproduce, prioritize, fix, and retest it.
Founder
Fadli Adrian
Fadli works directly with teams to scope launch risk, review evidence, and translate observed failures into engineering decisions. Verune is founder-led, Indonesia-first, and designed to compound each engagement into a stronger evaluation method and scenario corpus.
Meet Fadli on LinkedInNext step
Bring the workflow that needs more than confidence.
If your team has a working agent and a consequential decision, bring the real launch question.