Trodo
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AI Agent Analytics — trace, score, and improve every agent run in production

Trodo captures every AI agent execution: the planner steps, tool calls, LLM prompts and completions, retrieved context, latency, cost, and final output. Connect each trace to a real user and product outcome so engineering and product teams share one view of agent health.

Agent Observability — full-stack telemetry for LLM and AI agent systems

Agent observability goes beyond individual model calls. Trodo traces multi-step agent runs end-to-end — plans, sub-agents, tool hand-offs, and errors — and links them to product KPIs like retention, conversion, and feature adoption. One platform for engineering and product, not two disconnected tools.

AI Product Analytics — measure adoption, retention, and outcomes for AI features

Traditional product analytics was built for clicks and page views. Trodo is built for prompts, tool calls, and agent traces. Track AI feature adoption funnels, cohort retention for users of AI features, and the business outcomes that prove your AI is working.

AI Tracking and AI Monitoring — real-time alerts and anomaly detection

Trodo monitors AI agent runs in real time and surfaces issues automatically — latency spikes, tool call failure surges, cost anomalies, and quality regressions. Get alerted before users notice and get a direct path from the issue to a fix in your IDE.

Agent observability · v2.4

Built for agents that fix themselves.

Most tools are built for humans to watch agents. Trodo is built for both — real-time issue detection, root cause analysis, and a direct line to your IDE to write the fix.

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LATENCYp95 · 47ms
RUNS2.4M scored
MTTR↓ 61%
UPTIME99.99%
Tool misuse · agent.responder · 62 runsLatency drift · process_request · p95 +180msCluster forming · api_errors · 6h windowPR opened · fix/intent-classifierFrustration spike · onboarding · wait_timeEval regression · llm_judge · −0.04Auto-ticket #4821 · owner on-callCanary 4.2% · stableTool misuse · agent.responder · 62 runsLatency drift · process_request · p95 +180msCluster forming · api_errors · 6h windowPR opened · fix/intent-classifierFrustration spike · onboarding · wait_timeEval regression · llm_judge · −0.04Auto-ticket #4821 · owner on-callCanary 4.2% · stable

The Problem

Agents fail in unpredictable ways
that are hard to catch — and harder to solve.

01You see traces.Not what caused them.
02You find issues manually.Hours after your users already felt them.
03Your tools work for your team.Not for your agents.

The tools you have were built for humans to watch — not for agents to improve.

Platform

Everything your agents and
your team need.

Issues

Every failure, already diagnosed.

Broken runs, wrong tool calls, frustrated users — automatically grouped, scored, and explained. Not raw data. Root cause already written.

Issues — last 24hlive

  • tool.process_request

    Tool misuse

    60runs

  • agent.responder

    Tone drift

    18runs

  • tool.embed_batch

    Latency drift

    10runs
root cause: queue_depth > 40auto-grouped

Chat & MCP

Talk to your data. Let your agents do the same.

Ask anything, get a full orchestrated analysis in seconds. Your agents can query, eval, and fix — without a human in the loop.

ChatMCP · live
you
Why did sat scores drop after 09:00?

Evals

Score every run. On your terms.

Python, LLM-as-judge, or manual — all running in real time. Combine all three. Every run scored, continuously, your way.

EVALS24k runs · streaming
  • eval.python0.000%
  • eval.llm_judge0.000%
  • eval.human0.000%
  • eval.tool_use0.000%
  • eval.rag_relevance0.000%

Heal

From issue to merged PR in one click.

Trodo passes the root cause to your IDE agent. The fix gets written, a PR is created. You review and merge.

HEALPR · #4821
  1. 01

    Issue surfaced

    tool.process_request · queue_depth > 40

  2. 02

    Root cause written

    Selector picks process_request when fetch_context is hot — wrong path under load.

  3. 03

    PR opened in IDE

    + 24 −9 · fix/intent-classifier

  4. 04

    You merge.

For Every Team

Built for every team
building with AI.

Finally know why it broke.

Every failure diagnosed. Every run scored. Write evals in Python, via LLM, or by hand — all running in real time against your live agents.

Read the ml engineer guide →
EVAL SUITEpolicy_pack v2.4
  • eval.python0.000%
  • eval.llm_judge0.000%
  • eval.human0.000%
  • eval.tool_use0.000%
  • eval.rag_relevance0.000%

Integration

We’re built for agents.
So let our agent talk to yours.

No docs to read. No SDK to wire. Trodo’s setup agent handshakes with your IDE agent and instruments the stack itself.

T

Trodo · setup agent

github.com/trodoai/skills

idle
›_

Your IDE agent

cursor · claude · cline

agent ↔ agent · live transcript0 / 5

    OpenTelemetry native

    Already emitting OTLP traces? Trodo accepts them directly.

    TypeScript & Python

    First-class SDK support for both. Let the skill decide your integration.

    Frameworks

    LangChain, LangGraph, OpenAI Agents, Vercel AI SDK, Custom.

    Security

    Your agent data stays yours. Always.

    Every run, every user signal, every conversation — isolated, encrypted, and never used to train anything.

    ✓

    SOC 2 Type II

    In process

    Independently audited. Controls verified.

    ✓

    End-to-end encryption

    AES-256 at rest. TLS 1.2+ in transit.

    ✓

    Data isolation

    Fully isolated per organization. Your data is yours.

    ✓

    No model training

    Your agent runs and user behavior are never used to train models.

    View Trust Center →

    Your agents are running.
    Are they improving?

    See your first issue in minutes.

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