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What if we could watch AI at work?

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When AI results become traceable

Section titled “When AI results become traceable”

The more AI agents are built into processes, the more transparency matters: Which source did the agent use? Which system did it query, and with what data? Which agents are used how often, and on which channel?

LIVOI answers these questions on three levels: in the individual chat, in the inbox and in the dashboard.

Three levels of traceability
  • LIVOI tool
  • Result
  • Channel
  • Model
  1. 1
    LIVOI tool In the chat

    Every tool call with inputs, result and source.

  2. 2
    Result In the answer

    Optional answer validation against the sources found.

  3. 3
    Channel In the inbox

    Labels, channel and agent per conversation; the team can take over.

  4. 4
    Model In the dashboard

    Message trends, channel shares, activity and tokens per agent.

In the chat: look over the agent’s shoulder

Section titled “In the chat: look over the agent’s shoulder”

The chat shows which tools an agent called. For knowledge search, the files and text passages found are visible and can be opened directly. For custom tools, the inputs sent are shown as well, not just the target system’s response.

LIVOI chat with expanded tool calls: listing knowledge files and two knowledge searches with a text passage found in a product overview PDF LIVOI chat with expanded tool calls: listing knowledge files and two knowledge searches with a text passage found in a product overview PDF
Before answering, the agent lists its knowledge files, searches twice and shows the passage it found in the source.
Answer validation

For agents with knowledge search, you can enable a check that the answer is supported by the sources found. The answer then appears once the check is complete.

Structured answers

With an output schema, agents return results as a table with fixed fields. If details are missing, the agent asks for them.

In the inbox: every conversation at a glance

Section titled “In the inbox: every conversation at a glance”

All conversations from LIVOI Chat, WhatsApp and the LIVOI API come together in the inbox. Agents assign suitable labels such as “Quote”, “Feedback” or “Done” with every message. Filters show what needs attention. When needed, the team turns off automatic replies for a chat and takes over.

LIVOI inbox with chat list, labels, a WhatsApp conversation and the attributes contact, platform and agent LIVOI inbox with chat list, labels, a WhatsApp conversation and the attributes contact, platform and agent
Inbox with labels, channel, responsible agent and a switch for automatic replies. (German UI)
Section titled “In the dashboard: usage and trends”

The dashboard shows conversations, messages and active users for the selected period. Message trends can be compared with the previous period and copied as a table. Agent activity shows messages and token usage per agent; platform activity shows the distribution across channels.

LIVOI dashboard with key figures, message trend, agent activity with tokens and platform activity by channel LIVOI dashboard with key figures, message trend, agent activity with tokens and platform activity by channel
Key figures, message trend, activity per agent and distribution by channel. (German UI)

What changes day to day

Section titled “What changes day to day”
Before
  • AI as a black box: unclear where an answer came from
  • Errors are only noticed by customers
  • No overview of which agents and channels are used
After with LIVOI
  • Sources and tool calls visible in every conversation
  • Labels and filters show where the team should step in
  • Usage and costs per agent and channel in the dashboard

Benefits at a glance

Section titled “Benefits at a glance”
  • Traceable answers with sources and logged tool calls
  • Optional answer validation for high-stakes areas
  • Staff can take over conversations when it matters
  • A basis for optimization: which agents, which channels, how much usage