What is AI agent analytics?

AI agent analytics is the practice of tracking and measuring AI agent behavior in production — capturing every prompt, tool call, agent trace, and user outcome. Trodo provides AI agent analytics that connects agent performance to product KPIs like retention and conversion.

How does Trodo track AI agents?

Trodo tracks AI agents by instrumenting every agent run with a lightweight SDK. Each run captures the full trace — planner steps, tool calls, LLM calls, retrieved context, and final output — plus user identity and session context, so every agent execution is connected to a real product outcome.

What is AI product analytics?

AI product analytics is product analytics purpose-built for AI-native applications. Unlike traditional product analytics that tracks clicks and page views, AI product analytics tracks prompts, tool calls, agent traces, and AI feature adoption — giving product teams the behavioral data they need to improve AI products.

How is agent observability different from LLM observability?

LLM observability focuses on individual model calls — prompts and completions. Agent observability is broader: it covers multi-step agent runs (plans, tool calls, hand-offs, sub-agents) end-to-end, including the orchestration layer. Agent observability is what you need once your AI is more than a single LLM call.

Why combine agent observability with product analytics?

Because the engineering view alone cannot answer the questions product teams care about. Knowing that an agent had a P95 latency spike is useful only if you can see which users were affected, how it impacted retention, and which feature suffered. Trodo unifies agent observability with product analytics so engineering and product share one platform — not two disconnected tools.

Does Trodo support OpenTelemetry, OTel traces, and existing observability stacks?

Yes. Trodo can ingest traces from OpenTelemetry, OTel-compatible agent frameworks, and direct SDK instrumentation. You do not have to rip out your existing observability stack — Trodo enriches it with product context.

What does agent observability cost?

Trodo includes agent observability in the same plan as AI product analytics and AI agent analytics — no separate observability bill. Free tier covers up to 1M events/month, which is enough for most early-stage AI products.

Agent Observability

Agent Observability + LLM Observability — Connected to Product

Trodo provides full agent observability for AI-native applications: traces, spans, prompts, tool calls, errors, retries, latency, and cost — all connected to user sessions and product KPIs. Engineering and product see the same data, not two disconnected tools.

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Full agent and LLM trace capture
Tool-call latency, retries, and cost per agent run
Prompt and completion inspection
Tied to user sessions and product events
OpenTelemetry / OTel compatible
Free up to 1M events/month

What is agent observability?

Agent observability is the practice of capturing detailed telemetry from AI agents and LLM-powered systems running in production. It includes traces and spans for every agent run, every tool call, every retrieval, every model invocation — plus the prompts, completions, latencies, errors, and cost behind each one.

Where LLM observability covers single model calls, agent observability covers the full orchestration: multi-step plans, tool routing, sub-agent hand-offs, and the chain of decisions an agent makes before producing an output. As AI products move from one-shot completions to autonomous agents, agent observability becomes the only way to debug and trust them in production.

Trodo provides a complete agent observability layer — and connects it to product analytics. Instead of switching between an observability tool for engineering and an analytics tool for product, Trodo gives both teams one unified surface: drill from any product metric anomaly straight into the agent trace, prompt, and tool call that caused it.

What you get with Trodo

End-to-end agent traces
Every plan, tool call, retrieval, and sub-agent run, captured with timing, inputs, outputs, and errors. Filter, search, and replay any execution.
Prompt & completion inspection
See the exact prompt, system instructions, completion, and token usage for every model call. Diff prompts across versions and trace regressions.
Tool-call telemetry
Per-tool success rate, latency, retries, errors, and spend. Spot the slow or flaky tool dragging your agent down.
Cost monitoring
Token cost, tool cost, and total cost per agent run, per user, per cohort. Catch runaway spend before the bill arrives.
Tied to product KPIs
Every trace is joined to the user and session that triggered it. Drop from a retention chart into the exact agent runs behind it.
OTel-friendly
Ingest from OpenTelemetry-compatible agent frameworks. Keep your existing instrumentation; let Trodo add the product context layer on top.

Agent observability vs LLM observability vs APM

Capability	Trodo	LLM observability	APM (Datadog/NR)
LLM call traces	Yes	Yes	Partial
Multi-step agent traces	Yes	Partial	No
Tool-call analytics	Yes	Yes	No
Tied to user/session	Yes	No	Partial
Tied to product KPIs	Yes	No	No
Cost monitoring per agent run	Yes	Partial	No
Built for AI agents	Yes	Yes	No

Frequently asked questions

What is agent observability?: Agent observability is the engineering practice of capturing detailed telemetry from AI agents and LLM calls in production: traces, spans, prompts, completions, tool calls, retrievals, latencies, errors, and cost. It is the AI equivalent of APM — but for AI agents and LLM-powered systems instead of conventional services.
How is agent observability different from LLM observability?: LLM observability focuses on individual model calls — prompts and completions. Agent observability is broader: it covers multi-step agent runs (plans, tool calls, hand-offs, sub-agents) end-to-end, including the orchestration layer. Agent observability is what you need once your AI is more than a single LLM call.
Why combine agent observability with product analytics?: Because the engineering view alone cannot answer the questions product teams care about. Knowing that an agent had a P95 latency spike is useful only if you can see which users were affected, how it impacted retention, and which feature suffered. Trodo unifies agent observability with product analytics so engineering and product share one platform — not two disconnected tools.
Does Trodo support OpenTelemetry, OTel traces, and existing observability stacks?: Yes. Trodo can ingest traces from OpenTelemetry, OTel-compatible agent frameworks, and direct SDK instrumentation. You do not have to rip out your existing observability stack — Trodo enriches it with product context.
What does agent observability cost?: Trodo includes agent observability in the same plan as AI product analytics and AI agent analytics — no separate observability bill. Free tier covers up to 1M events/month, which is enough for most early-stage AI products.

One layer for engineering and product.

Get agent observability that's actually wired into product analytics. Free up to 1M events/month.

Start free →Book a demo

Agent Observability + LLM Observability — Connected to Product

What is agent observability?

What you get with Trodo

End-to-end agent traces

Prompt & completion inspection

Tool-call telemetry

Cost monitoring

Tied to product KPIs

OTel-friendly

Agent observability vs LLM observability vs APM

Frequently asked questions

Read more on the Trodo blog

One layer for engineering and product.