As AI agents take on sophisticated, multi-turn workflows, organizations need more than strong macro-level results. They need a granular understanding of why an agent behaved the way it did for each individual turn of a conversation. When debugging a workflow or investigating an escalation, teams want to trace the exact logic that led to the agent’s response.

This is where Trace View comes in. As a multi-faceted interface providing insights into every step that the agent took, Trace View has become foundational for teams building agents with Decagon. It provides a level of transparency that transforms agent reasoning from an opaque black box into something completely observable and intelligible. Both customers and internal teams at Decagon rely on it daily to examine complex interactions, debug issues, and improve agent quality with far greater speed and accuracy.

A clear, step-by-step view of every interaction

A single agent response might appear simple on the surface, but behind the scenes, each turn is a miniature workflow. In a matter of seconds, the agent may make several LLM calls, retrieve information, evaluate conditions, invoke tools, and navigate safety checks. When the final output doesn’t look quite right, reconstructing that invisible chain of events can be remarkably difficult without proper visibility.

Most AI systems don’t make this any easier. They function like black boxes: a long prompt goes in, a response comes out, and everything in between is hidden. Developers are forced to guess which steps were executed and what might have gone wrong, making the process of improving agents slow and heavily reliant on intuition rather than evidence.

Trace View removes this opacity by revealing the agent’s reasoning in clear, sequential detail. Instead of digging through a massive dump of unintelligible logs, Decagon users have complete visibility into the agent’s flow turn by turn: which steps were executed, what instructions the model received, and how it responded at each moment. The result is a coherent, readable narrative of the agent’s decision-making.

This clarity immediately changes the debugging experience. Teams can pinpoint exactly where reasoning might have diverged from expectations, whether it was a retrieval step that returned imprecise context, a tool that behaved unexpectedly, or an instruction that wasn’t interpreted correctly. When agents escalate prematurely or produce inconsistent outputs, Trace View highlights the precise moment the deviation occurred.

The structured architecture behind it: Agent Operating Procedures (AOPs)

One of the reasons Trace View is able to provide such transparency is because Decagon agents are built on top of Agent Operating Procedures (AOPs). AOPs don’t rely on massive, monolithic prompts to the agent. Instead, they break agent behavior into modular steps, each with specific objectives and boundaries. The agent receives instructions iteratively rather than all at once, which keeps the model focused and dramatically improves instruction-following and reliability.

This structure naturally lends itself to observability. Because the agent thinks and acts in discrete steps, Trace View can present each piece of the workflow cleanly: the inputs, the reasoning, the outputs, and the transition to the next step.

It’s not a bolt-on debugging tool, but a direct consequence of how Decagon agents are architected.

Latency tracing: Visibility into performance, not just reasoning

Understanding how and why an agent performed certain steps is one part of observability; knowing how long each step took is another critical component, especially for voice interactions. When a pause before the agent’s response feels too long or unnatural, teams need clear data to pinpoint the latency bottleneck.

Trace View provides detailed latency metrics for every micro-step in the workflow. Instead of guessing whether a slowdown came from a model call, a retrieval step, or an external tool, teams can see exactly where time was spent. Performance tuning becomes evidence-driven rather than speculative, and logic updates become significantly more targeted.

When agents are interacting with customers at scale, uncovering performance gaps quickly becomes just as essential as understanding the reasoning trace itself.

Transforming diagnoses into solutions

Once a problem with the agent is identified, teams need to adjust the agent’s logic and confirm that the fix actually works. Trace View pairs naturally with Decagon’s unit testing capabilities for exactly this reason. A conversation under investigation can be converted directly into a test case, allowing a specific turn to be replayed against updated logic. Because agents rely on non-deterministic LLMs, the testing suite enables teams to run the same scenario multiple times to ensure the fix holds consistently.

Grounding these tests in real customer interactions rather than hypothetical prompts ensures that validation reflects the complexity and nuance of real-world usage. The result is a far higher level of confidence that the agent will behave reliably in production and won’t expose customers to untested behavior.

Combined with the rest of Decagon’s platform, Trace View help form a tight feedback loop that lets teams iterate quickly and move to production confidently:

• Identify flagged conversations at scale with Watchtower
• Diagnose and pinpoint issues with Trace View
• Update agent logic in flexible natural language with AOPs
• Validate the fix using unit testing and Simulations
• Deploy updates safely with Agent Versioning

A commitment to transparency

Trace View represents a broader philosophy behind how Decagon approaches AI systems. AI should not feel like a black box, especially when it has a direct and meaningful impact on customers. Teams deserve clear, actionable insight into how their agents think and act. 

With Trace View, every decision-making step becomes visible, simplifying debugging, accelerating iteration, and building trust. It also enables businesses to run AI systems with a level of rigor and accountability that matches the importance of the workloads these agents now handle.

If you’re curious how this all comes together in practice, we’d love to show you. Schedule a demo and see how Decagon can make your AI agents more reliable, performant, and transparent.