As we move deeper into the “Inference Era,” I’ve noticed a growing ROI blindspot that standard observability tools aren’t quite hitting.
When we were just building simple RAG chatbots, token tracking was easy. But with Agentic workflows, a single user intent can trigger 5, 10, or even 20 recursive calls. If that agent enters a loop and fails to reach a success state, you’ve essentially funded a “Zombie Task”—a sequence that burns your token budget with zero product value.
The Challenge: Moving from Traces to Margins
Most of the current stack (Langfuse, Helicone, etc.) is elite at technical debugging. However, I’m finding a gap in Feature-Level Unit Economics. For example:
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Feature A (Summarization): Simple, high margin.
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Feature B (Autonomous Research Agent): Complex, high “Zombie Loop” risk, potentially negative margin.
Without mapping every recursive call back to a specific Feature ID, it’s impossible for a founder or PM to know which part of their app is actually profitable and which is a “cost-sink.”
A Few Questions for the Community:
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How are you “collapsing” multi-step agent logs to see the total cost of a single user outcome?
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Are you setting hard token “guardrails” at the agent level, or are you monitoring margins after the fact?
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For those building on the OpenAI/Anthropic/Gemini stack simultaneously: How are you normalizing cost-per-feature across different pricing models?
I’ve built a small internal tool to handle this ‘collapsing’ logic for my own agents. If anyone wants to see the schema or try the SDK, let me know and I’ll send over the link