As enterprises deploy Virtual Agents powered by LLMs, a new challenge has emerged - understanding what the AI is doing behind the scenes. While AI systems promise speed, scale, and automation, there is also a certain level of unpredictability in the way they function. This is because of the non-deterministic nature of AI systems.

Why Observability Is Critical for AI Systems

In practice any virtual agent performs a series of multi-step decisions within a few seconds. These can include intent detection, information retrieval from knowledge bases, API execution to pass on or retrieve data, and finally generating a response.

And as virtual agents are powered by LLMs under the hood they operate in a fundamentally different way. Unlike deterministic software systems, where the same input reliably produces the same output, AI-powered virtual agents can produce different answers for the same question - depending on context, conversation history, or subtle variations in prompt construction.

This makes observability non-negotiable. Without it, virtual agents risk silent failures, hallucinations, incorrect intent routing, compliance gaps due to missing audit trails and hidden latency issues that are hard to detect when only the final response is visible.

What Does “Observability” Mean in a Customer Conversation

Observability in conversational AI means capturing the complete lifecycle of the user interaction - from the moment a query arrives to the final response delivered to the user. It’s not just about logging messages; it’s about understanding the internal decisions that shaped those messages.Observability is the bridge that turns the virtual agent’s opaque decisions into transparent, debuggable sequences at every turn.

Key dimensions of conversational observability include:

1. Skill and Intent Routing: Identification the skills leveraged to handle a query and the reason that it was selected. This makes root cause analysis significantly easier when the agent responds incorrectly.

2. Tool Invocations: Virtual agents frequently interact with external systems such as CRMs, order management systems, databases, or internal APIs. Observability means capturing every tool call, including the inputs sent, outputs received, errors encountered, and execution time.

3. Knowledge Retrieval: Enabling knowledge source attribution by showing exactly which documents were retrieved and used to generate the final response.

4. Context Switches: In multi-agent systems, conversations may be handed off between skills. Observability captures when these switches happen and the reasoning behind them.

Despite rapid innovation in conversational AI, most virtual agent platforms share a critical blind spot during testing and deployment of virtual agents. And, in order to debug issues, teams often have to rely on raw system logs buried deep within the infrastructure tooling that only engineers can interpret. This approach does not serve the people who need these insights - whether it is the product managers validating any behavior, QA teams testing edge cases, or customer success teams investigating escalations. Teams are either forced to rely on engineering for every investigation or accept vague explanations like, “It just didn’t work.”

The Level AI Advantage: Debug View for Virtual Agents

Level AI addresses the gaps in observability with an intuitive, self-serve layer built directly within the UI. For every virtual agent response, Debug View provides a clear audit trail embedded within the conversation transcript - breaking down the response into a sequence of internal events. Key data points surfaced include:

• Event Logging, Real-time Conversation Logs & Live Transcripts: Every agent response is visually tagged to clearly identify the intent and skills leveraged. For voice sessions, the Debug UI streams a live transcript as the conversation unfolds. The ability to save and export these debug conversations allows teams to reference past calls as a library for continuous bot improvement.
• Tool Abstraction: We provide total visibility into how the bot interacts with external systems including input parameters, outputs, errors, and latency.
• Knowledge Source Citation: Our platform ensures that the Virtual Agent cites sources with specific extracts, proving the bot is grounded in your actual business data and acting as a real-time fact-checker. In the Debug view, you see the exact paragraph used to build the answer. This visibility also confirms graceful fallbacks, showing when the agent responds with "I can't answer that with current resources" instead of hallucinating. to indicate documents used by the virtual agent while responding to user queries.

Instead of guessing where a conversation failed, teams can pinpoint exactly which step succeeded or broke. Our platform creates a user-friendly, visual layer of the agent’s internal decision-making in a way that’s accessible for non-technical users without digging through logs.

Conclusion

The key to scaling autonomous AI systems is transforming their opaque decision-making into transparent, auditable processes. Observability is this foundational shift - moving virtual agents from "black boxes" to explainable, trustworthy automation. This single, clear audit trail provides distinct advantages for every stakeholder:

• For Engineers: It enables faster debugging by tracing the exact decision chain, gives confidence for deployments through end-to-end validation, and supports performance optimization by providing detailed latency metrics for bottlenecks.
• For Customers and Business Teams: It delivers self-service transparency, allowing non-technical users (QA, product managers, bot builders) to see the agent's logic. This builds trust and compliance through explainable, auditable actions, ensures more accurate responses grounded in verified knowledge, and allows for faster issue resolution with instant access to conversation history.

With Level AI’s Debug View, virtual agents are no longer black boxes - they’re systems you can understand, improve, trust and scale.

Want to know more? Sign up for a demo: https://thelevel.ai/request-demo/