Introduction

Documentation Index

Fetch the complete documentation index at: https://docs.trodo.ai/docs/llms.txt

Use this file to discover all available pages before exploring further.

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What is Trodo?

Trodo is an observability platform built for the generation of products that have both a product surface and an AI layer. It brings together two things that have always lived in separate tools: event-based product analytics and agent-level tracing. Most teams ship faster when they understand three things:

1. What are users actually doing? — not what you assumed they’d do, but the real click paths, drop-off points, and activation patterns.
2. What are users getting out of your agents? — which prompts succeed, which fail, how long things take, where errors stack up.
3. What should you build next? — the answer lives in the data, not in opinion. Trodo surfaces it directly.

Trodo connects those three questions into a single platform.

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Talk to your data, not your dashboards

Traditional analytics tools give you charts. You still have to interpret them, build your own views, write SQL, or wait for a data team to pull a report. By the time you have an answer, the sprint is half over. Trodo is built around the idea that your data should answer questions, not raise them. Every run your agent executes, every user who hits a wall, every workflow that fails — it all lands in Trodo with the structure needed to query it in plain language. You describe what you want to know; Trodo finds it. This matters especially for AI products, where the feedback loop is fast and the product surface changes week to week. You cannot afford to wait on manual reporting.

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Two layers, one platform

Both layers share the same user identity. That means you can ask questions across them — which users whose agents failed more than twice in the last week also churned within 30 days? — without stitching data from multiple tools.

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What Trodo tells you

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Are users getting value from your agents?

The most important question for any AI product. Trodo gives you:

• Success rates — what percentage of agent runs reach a successful outcome.
• Time-to-value — how long from first prompt to a result the user acts on.
• Retry signals — how often users re-run, rephrase, or abandon — a proxy for frustration.
• Satisfaction — attach explicit feedback to runs, or infer it from downstream behavior (did they use the output, or did they close the tab?).

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Are there issues you don’t know about yet?

• Error aggregation — server-side crashes, uncaught exceptions, LLM refusals, and tool failures, all in one place with stack traces attached.
• Anomaly detection — latency spikes, token cost outliers, and error-rate shifts surface automatically as incidents.
• Root-cause traces — click any failed run to see the full span tree: which LLM call threw, what the input was, how far the agent got before it stopped.

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What should you build next?

• Usage patterns — which tools get invoked most, which agent workflows users actually complete vs. abandon.
• User segments — power users vs. occasional users vs. churned. What separates them.
• Conversion paths — which product actions correlate with upgrade, expansion, or long-term retention.
• Feature gaps — where users attempt something your product can’t yet do.

All of this comes from the data your product already generates. You instrument once; Trodo surfaces the patterns.

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Who it’s for

Trodo is built for teams shipping AI-native products — copilots, autonomous agents, workflow automation, chat interfaces — where understanding the product means understanding both the human side and the AI side.

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Get started