Why Cekura Over Tracing Platforms for Monitoring Conversations

If you are building AI agents today, you are likely in the middle of a major shift: moving from simple "chatbot" chains to complex, stateful agents that handle real business logic.

When you need to monitor these agents, the default choice for many developers is an LLM Tracing Platform (like Langfuse, LangSmith, or Arize Phoenix). These are excellent tools for debugging individual LLM calls. But when it comes to monitoring conversations - especially multi-turn, goal-oriented voice or chat agents - developers often hit a wall.

This post breaks down why we built Cekura to fill that gap, using a real-world example to show the difference between "tracing steps" and "monitoring outcomes."

1. The Context Trap: Why "Traces" Are Not Enough

The biggest technical difference lies in how these platforms view data. Tracing tools log turns (individual request/response cycles). Cekura logs sessions (the entire multi-turn interaction).

To understand why this matters, let's look at a standard Lost Credit Card Replacement workflow.

The Scenario

Your banking agent needs to handle a stressful situation with four distinct steps:

1. Verification: Verify the user (e.g., DOB or Last 4 digits).
2. Reason: Ask why the card is being replaced (Lost vs. Stolen).
3. Delivery: Confirm where to ship the new card.
4. Action: Execute the block and reissue APIs.

The Trace-Based View (Fragmented)

Tracing platforms see this conversation as four separate rows in a table. They evaluate them individually:

• Turn 1: Did the agent ask for DOB? Yes. (Pass)
• Turn 2: Did the agent ask for the Last 4 digits? Yes. (Pass)
• Turn 3: Did the agent ask for the address? Yes. (Pass)

The Failure Mode: Imagine the user failed verification in Step 1, but the agent hallucinated and proceeded to Step 3 anyway. In a turn-based view, Step 3 looks perfect - the agent asked the right question for that step. You get a "Green" status on a critical security failure because the LLM Judge doesn't know what happened across three turns ago.

The Cekura View (Holistic)

Cekura ingests the entire conversation history as a single context. Because Cekura gives you combined insights across all steps, our LLM Judge asks:

"Did the agent reach the Delivery step AND successfully complete Verification in the history?"

If the answer is "No," Cekura flags the entire session as failed. Even verification itself is often a multi-step process (e.g., "I don't have my ID" -> "Okay, let's try SMS"). Cekura tracks this state change over time, whereas turn-based tools lose the thread.

2. Cost Efficiency: The "1 Judge" Rule

Cost is a major factor in observability.

• Tracing Platforms: To get granular insights, you often run an "LLM Judge" on every single turn. For a 20-turn conversation, that is 20 separate evaluation calls, racking up token costs and latency.
• Cekura: We use a "1 LLM Judge per Conversation" model. We feed the entire transcript to the LLM Judge once. It’s significantly cheaper and faster to process.

3. Native Audio Metrics (Voice & Text)

While text is important, many agents can and are usually extended to support voice as well. Most tracing tools are text-native; they see a transcript, but they don't "hear" the call.

Cekura supports Audio Metrics out of the box. Since our goal is monitoring all conversations, we track the physics of the interaction:

• Silence & Dead Air: Did the agent hang for 3 seconds while thinking? (A transcript won't show this).
• Interruption Handling: Did the user cut the agent off? Did the agent stop speaking immediately, or did it keep barreling through?
• Latency: We track the exact milliseconds between the user finishing a sentence and the agent starting audio playback.

4. The "Metric Lab": Optimization with DSPy

Defining good LLM Judge is hard. How do you write a prompt that accurately measures "verification success" or "sales aggression"? Usually, it involves hours of tweaking prompts and hoping they work.

Cekura simplifies this with our DSPy-based Metric Optimizer.

• Define: You say, "I want to measure if the agent successfuly completed verification."
• Annotate: You grade 5 or 10 calls manually and provide feedback to our optimiser agent.
• Optimize: Our optimizer (powered by DSPy) essentially "compiles" the perfect prompt for you. It iterates on the instructions until the metric mathematically aligns with your manual grades.

You don't need to be a prompt engineer to get reliable metrics; you just need to know what a good conversation looks like. Also you now have a golden dataset of what success looks like for that LLM Judge.

5. Semantic Alerts

Infrastructure alerts (like "Latency > 2s") are standard. Cekura supports Custom Conversational Alerts.

You can set up alerts based on business logic failures and route them directly to Slack or PagerDuty:

• Example 1: "Alert me if the user mentions 'Lawyer' or 'Sue' and the sentiment is Negative."
• Example 2: "Alert me if the 'Credit Card Replacement' flow completes but the 'Address Confirmation' step was skipped."

This moves monitoring from "Is the server up?" to "Is the business running?"

Where Tracing Platforms Shine (And the Trade-offs)

To be fair, platforms like LangSmith or Langfuse are incredible tools for specific phases of development.

1. The "Micro" View for Prompt Engineering

Tracing tools check at the individual LLM turn level. They know exactly what prompt went in (variables, system instructions) and what came out. This allows you to work on your prompts easily. If you are tweaking the syntax of a specific RAG retrieval prompt, these tools give you the microscope you need.

2. Broad vs. Specific Agents

Tracing Platforms are better suited for broad, general-purpose agents where the conversation flow is unstructured or random.

Cekura wins for conversation-specific agents (Support, Sales, Booking, Recruitment and more) where there is a clear goal and multiple turns. Everything on Cekura right from the UI, the LLM Judges, the dashboards and alerting systems are designed for conversational agents.

Note : Cekura is also a simulation platform helping you test your agents. You can simply pick a conversation from production and replicate it in simulation. This is a great way to build evals for your conversational agents.

Summary

If you need to debug the internals of a single prompt, grab a microscope like a tracing platform.

If you need to monitor the success of a conversational agent - checking multi-turn context, voice performance, and business outcomes - Cekura provides the platform to see the whole picture.