Explainable AI: See exactly how the AI got an answer for you.
Every conversation in Rezolve.ai is auditable - what the AI understood, which agent took it, which sources it weighed and scored, what it decided and why. A glass box for IT, not a black box.
- Check the site's recycle bin and restore
- Check the second-stage recycle bin
- Restore the whole library to an earlier date
A real trace, shortened. Each step can be expanded to the full reasoning, the scored results and the sources behind them.
How it's different: How it's different?
Most AI in IT support gives you an answer and a thumbs-up button. With Rezolve.ai you can trace the reasoning behind every answer and action.
Every step, on the record
Routing, search, synthesis, web search and follow-up are each shown with what the agent concluded and why for every query. Trace-back and audit every AI output.
AI audit trailEvery source, scored
See match score for knowledge articles, documents and automations that were considered, so a wrong answer leads straight to the content fixes.
match scores shownEvery rule, bespoke
Escalation, triage, web search, visibility and data masking are settings you set and can point to - not AI behavior you have to guess. Put additional guardrails as per your standards.
governed by youThe agents: AI agents you can see in action
Each query passes through specialist AI agents. Traceability shows each one's role in it.
Routing agent
Reads the enriched query and decides whether it needs knowledge, an action or a person, with the reason written down.
Search results
Knowledge results and automation offers, ranked and scored, with the source of each.
Synthesis agent
Works through the question, the resources found and what's ambiguous, then chooses the next step.
Follow-up agent
Checks whether the answer is enough and offers a ticket or a person when it isn't.
Governance: AI Governance you can show an auditor
Who sees what
Make conversations visible by role only, or by role and workspace. Content from restricted workspaces is redacted in full, and rows stay listed so counts still reconcile.
Sensitive data masked
Data loss prevention masks entities such as social security, passport, driver's license, tax ID and bank numbers by region, with optional audit logging.
What employees really ask
Queries are classified against an intent hierarchy of what employees ask for. Questions that fit no intent are proposed as new nodes for your reviewers.
- Full conversation and query history
- Expandable step-by-step reasoning
- Accuracy, CSAT and abandonment dashboards
- Escalated and non-escalated conversations
- Audit history for settings changes
- Event and action logs
- Role-based access to reasoning
- Export and reports
Compared: Compared with typical AI in IT support
| Typical AI assistant | Rezolve.ai | |
|---|---|---|
| Why it answered that | Not shown | Each agent's reasoning, step by step |
| What it considered | Hidden | Knowledge and automation results with match scores |
| When it escalates | Fixed behavior | Rules you set and can point to |
| Fixing a wrong answer | Guess which content caused it | Follow the trace to the source |
| Sensitive data | Stored as typed | Masked by data loss prevention |
The same principle runs through ticket triage in AI incident management and cited answers in AI knowledge management.
Straight answers: FAQs for Rezolve.ai Explainable AI
What is explainable AI?
Explainable AI is AI whose decisions can be inspected: you can see what it understood, what it considered, what it chose and why. In IT and employee support that means being able to open any answer or action and read the reasoning behind it.
What is an example of explainable AI in IT support?
An employee writes "Help! All my SharePoint files are gone!" In Rezolve.ai you can open that query and see the routing agent's reason for treating it as a knowledge question, the 7 knowledge results and 14 automation offers it matched with their scores, the synthesis agent's reasoning that no document covered file recovery, its decision to search the web, and the final answer.
What is an AI audit trail?
An AI audit trail is the record of what an AI system did and why: the input, each step taken, the sources used and the output. Rezolve.ai keeps one for every conversation, alongside audit history for changes to settings and knowledge.
Why does explainable AI matter for agentic AI?
Agents take actions, not just give answers, so it matters who let them act, on what evidence, and whether you can prove it afterwards. A step-by-step trace lets IT check a decision, correct the knowledge behind it, and show auditors what happened.
Can I see why the AI escalated or created a ticket?
Yes. Escalation rules are settings you control, such as ticket creation, the number of triage questions before a ticket and automatic escalation after repeated attempts, and the conversation record shows which one applied.
Who can see conversations and their reasoning?
You choose. Conversations can be visible by role only, or by role and workspace, and content from restricted workspaces is redacted in full while the rows stay listed so counts still reconcile. Sensitive data such as social security, passport and bank numbers can be masked with data loss prevention.
How does Rezolve.ai learn what employees ask for?
Every query is classified against an intent hierarchy that describes what employees ask for, separate from who answers it. When questions arrive that fit no existing intent, Rezolve.ai proposes a new node as a draft for your reviewers to accept or discard.
Is explainable AI only for the virtual agent?
No. The same principle runs through Rezolve.ai: ticket triage writes the reason for each priority and assignment, change risk shows the rule that fired, and knowledge answers cite their source.
Ready when you are: Start resolving tickets autonomously.
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Bring twelve months of ticket history to the demo. DeskIQ ranks your top five automation opportunities on your own queue, with the projected hours next to each — you leave with the roadmap, not a slide about one.