What is agent observability?

Agent observability is the engineering discipline of capturing detailed telemetry from AI agents: traces, spans, prompts, completions, tool calls, latencies, errors, and cost. Trodo combines agent observability with product analytics in one unified platform.

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 AI agent analytics different from agent observability?

Agent observability is the engineering view: traces, spans, prompts, latencies, errors, and cost. AI agent analytics is the product view: which agents drive activation, where users drop off, and how outcomes shift over time. Trodo combines both into one platform so engineering, product, and growth teams share a single source of truth.

Which agent metrics actually matter?

Core AI agent analytics metrics include task completion rate, tool-call success rate, average steps per task, hand-off rate, average latency to user-perceived outcome, downstream user action rate, agent-driven retention, and agent-driven revenue. Trodo computes these out of the box from your traces.

Does Trodo work with LangChain, LangGraph, OpenAI agents, Anthropic, Llama, and custom agents?

Yes. Trodo ingests traces from popular agent frameworks (LangChain, LangGraph, OpenAI, Anthropic SDK, custom orchestration) and any custom agent via a small SDK. You can correlate any agent run with the user, session, and product event that triggered it.

Can AI agent analytics help me debug failing agents?

Yes. From any product metric (a sudden drop in conversion, a spike in support tickets, a churned cohort) you can drill into the exact agent runs and tool calls behind it. Trodo is built so a product manager spotting a metric anomaly and an engineer fixing the underlying agent bug are looking at the same trace.

AI Agent Analytics

AI Agent Analytics for Production Agents

Trodo's AI agent analytics tracks every plan, tool call, retrieval, hand-off, and outcome your agents produce — and ties that behavior directly to product KPIs like activation, retention, and revenue. Stop guessing whether your agents work. Measure them.

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Task completion rate and tool-call success
Hand-off, latency, and step-count breakdowns
Agent-driven retention and revenue cohorts
Agent traces tied to user sessions
Works with LangChain, LangGraph, OpenAI, Anthropic
Free up to 1M events/month

What is AI agent analytics?

AI agent analytics is the measurement layer for AI agents running in production. It captures every plan, tool call, retrieval, hand-off, success, failure, and the downstream user outcome each agent run produces. Unlike LLM logging or agent observability tools (which were built for engineers debugging traces), AI agent analytics treats every agent run as an event in your product analytics graph — joined to users, sessions, funnels, and revenue.

For teams shipping agentic SaaS, AI copilots, or AI workflows, AI agent analytics answers questions a debugger never can: which agents are actually used? Which fail silently? Which drive retention? Which cost more than the value they create? Which hand-offs lose users?

Trodo provides AI agent analytics out of the box: drop in the SDK or stream traces from your existing framework, and Trodo correlates every agent run with the user, session, and product event that triggered it.

What you get with Trodo

Agent task completion
Measure how often agents finish what they start. Compare across agent versions, prompts, and user cohorts to spot regressions before users complain.
Tool-call analytics
Track success/failure rates per tool, average latency, retries, and cost. Identify which tools are dragging agents down — or never being called at all.
Hand-off & step analysis
See where agents loop, hand off, or escalate. Build funnels from prompt → plan → tool calls → final answer to find the exact step where users abandon.
Agent-driven retention
Cohort users by which agent they used, when. Trodo shows whether your agents actually drive long-term retention or just one-off interactions.
Cost & value attribution
Tie token spend, tool spend, and infra cost to the user outcome each agent produces. Spot agents that cost more than they earn.
Drill into any trace
Click any metric anomaly and jump straight into the agent traces behind it. Product and engineering see the same data, not two different vendor screens.

AI agent analytics vs LLM observability vs product analytics

Capability	Trodo	LLM observability	Product analytics
Tool-call success metrics	Yes	Yes	No
Agent task completion KPI	Yes	Partial	No
Tied to user/session	Yes	No	Yes
Tied to retention/revenue	Yes	No	Yes (no AI context)
Agent-driven cohorts	Yes	No	No
Funnels including agent steps	Yes	No	No
Built for product + engineering	Yes	Engineering only	Product only

Frequently asked questions

What is AI agent analytics?: AI agent analytics is the measurement of AI agents in production — every plan, tool call, retrieval, hand-off, success, failure, and the user outcome each run produces. Unlike LLM logs (which only show traces), AI agent analytics ties agent behavior to product KPIs like activation, retention, and revenue.
How is AI agent analytics different from agent observability?: Agent observability is the engineering view: traces, spans, prompts, latencies, errors, and cost. AI agent analytics is the product view: which agents drive activation, where users drop off, and how outcomes shift over time. Trodo combines both into one platform so engineering, product, and growth teams share a single source of truth.
Which agent metrics actually matter?: Core AI agent analytics metrics include task completion rate, tool-call success rate, average steps per task, hand-off rate, average latency to user-perceived outcome, downstream user action rate, agent-driven retention, and agent-driven revenue. Trodo computes these out of the box from your traces.
Does Trodo work with LangChain, LangGraph, OpenAI agents, Anthropic, Llama, and custom agents?: Yes. Trodo ingests traces from popular agent frameworks (LangChain, LangGraph, OpenAI, Anthropic SDK, custom orchestration) and any custom agent via a small SDK. You can correlate any agent run with the user, session, and product event that triggered it.
Can AI agent analytics help me debug failing agents?: Yes. From any product metric (a sudden drop in conversion, a spike in support tickets, a churned cohort) you can drill into the exact agent runs and tool calls behind it. Trodo is built so a product manager spotting a metric anomaly and an engineer fixing the underlying agent bug are looking at the same trace.

Measure your agents like you measure your product.

Drop in the Trodo SDK and start tracking AI agent analytics in minutes. Free up to 1M events/month.

Start free →Book a demo

AI Agent Analytics for Production Agents

What is AI agent analytics?

What you get with Trodo

Agent task completion

Tool-call analytics

Hand-off & step analysis

Agent-driven retention

Cost & value attribution

Drill into any trace

AI agent analytics vs LLM observability vs product analytics

Frequently asked questions

Read more on the Trodo blog

Measure your agents like you measure your product.