Explainability - Trust Through Transparency
AI agents reduce the time to first response and resolution, unlock deeper reporting, and handle the most complex workflows 24/7 — until they don’t. When your agent makes an unpredictable decision, how do you know what happened and why?
Most platforms only show the output, the final result that the agent generated. We believe that’s not enough.
True confidence in your agent comes from understanding the reasoning behind every AI response — so that you can explain your agent’s behavior, improve it, and deploy your agent across thousands of conversations.
That’s why we built explainability into every message.
Inside every decision
Every agent response is the result of dozens of micro-decisions. Your agent considers which knowledge to use, whether to follow a multi step workflow, and how to phrase the response to match your brand tone. Most platforms treat this as a black box. But improving your agent when it responds unexpectedly means diagnosing exactly what happened in that sequence of micro-decisions. You have to know why it did what it did. We make it transparent — so you know the exact steps and reasons your agent made its decision.
Three layers of visibility
We break explainability down into three parts.
- References: What knowledge the agent retrieved and considered.
- Reasoning: How it arrived at this answer
- Gaps: What information was missing or incomplete
A real example
Your agent offers an exchange that goes against company policy.
References show the agent retrieved conflicting knowledge articles — one outdated (no holiday exchanges), one current (exchanges within 30 days).
Reasoning reveals it chose the newer one based on customer frustration.
Gaps show the customer asked about holiday exchanges specifically, but no knowledge covered that scenario.
You know exactly what to fix: archive the older policy, add specific knowledge on the holiday exchange policy and introduce new guidance to help the agent prioritize conflicting exchanges.
Explainability in every stage
The ability to transparently understand your agent is essential at every stage of of its lifecycle.
- Testing confidently: You see exactly why your agent responds across a variety of edge cases.
- Debugging quickly: You can diagnose what went wrong — incorrect knowledge, missing information, conflicting guidance.
- Improving iteratively: You make an improvement and see whether your change worked as intended.
- Analyzing at scale: When your agent is live, you can quickly spot patterns and opportunities for improvement.
Trust through transparency
The best agents aren’t built once—they’re improved continuously. To improve your agent, you need to understand it. Without explainability, you’re flying blind.
Get in touch
If you want to see what explainability looks like in your own agent, we’d love to walk through it.
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