Your AI agent is failing. Let's see why.

Trace every step, tool call and reasoning turn. Latitude discovers failure patterns and turns them into evals your team can act on.

Up to

99%

of agent failures before they reach users

As little as

10 traces

is enough to start discovering repeating error patterns

Agents fail differently. Most tools are not for that. Latitude is.

Agents fail silently. A wrong tool call at step 3 looks fine by step 12. Latitude finds it before your users do.

Multi-step traces

See where in the chain your agent went wrong, not just what it returned

Tool call visibility

Know exactly which tool was called, with what input, and what it returned

Reasoning observability

Follow your agent's decision path turn by turn

Set up evals in minutes

You can set up Latitude and start evaluating your LLMs in less than 10 minutes

Observability

Failure detection

Issue monitoring

Observability

Capture real inputs, outputs, and context from live traffic. Understand what your system is actually doing, not what you expect it to do.

Full traces

Observe your AI’s behaviour in the most comprehensive way

Usage statistics

Keep track of the token usage and regulate expenses

Generic evals scores your AI. We show you why users are complaining.

Latitude builds evals around your actual failure modes — not abstract quality benchmarks.

What's measured

What's considered good performance?

Success definition

Who defines success?

Data used

Context awareness

What's being considered upon judgment?

Failure detection

What issues are being discovered?

Optimization metric

What teams optimize for?

Adaptation over time

How continuous support work?

Most teams

Generic evals

Your AI agent follows instructions good enough

Model provider / Public dataset

Static, generic datasets

Contexts a model was trained on

Biased and superficial issues

“Better abstract model score”

Monitoring static benchmarks that don’t evolve

Latitude's approach

Aligned evals

Your users actually got what they needed from an agent

Your domain expert

Real production logs & user feedback

Your real failure modes and specific cases

Exact patterns that hurt your users

Fewer user complaints, Higher reliability, Business KPIs

Continuously updating as new failures appear live

Observe

Monitor agent behaviour

Capture real inputs, outputs, and context from live traffic to understand what your agent is actually doing

Annotate

Flag what went wrong

Review real agent responses and annotate where things went off. That signal is what drives everything next.. Turn intent into a signal the system can learn from.

Reflect

See what keeps going wrong

Automatically group failures into recurring issues, detect common failure modes and keep an eye on escalating issues.

Evaluate

Evaluate automatically based on your issues

Convert real failure modes into evals that run continuously & catch regressions before they reach users.

Start with visibility

Start with visibility. Grow into reliability.

Start the reliability loop with lightweight instrumentation. Go deeper when you’re ready.

Providers

OpenAI

Anthropic

Azure

Google AI Platform

Amazon Bedrock

Cohere

Together AI

Vertex AI

Gemini

Groq

Mistral AI

Ollama

LiteLLM

Replicate

AWS SageMaker

Hugging Face Transformers

Aleph Alpha

IBM watsonx.ai

import { LatitudeTelemetry } from '@latitude-data/telemetry'
import OpenAI from 'openai'

const telemetry = new LatitudeTelemetry(
  process.env.LATITUDE_API_KEY,
  { instrumentations: { openai: OpenAI } }
)

async function generateSupportReply(input: string) {
  return telemetry.capture(
    {
      projectId: 123, // The ID of your project in Latitude
      path: 'generate-support-reply', // Add a path to identify this prompt in Latitude
    },
    async () => {
      const client = new OpenAI()
      const completion = await client.chat.completions.create({
        model: 'gpt-4o',
        messages: [{ role: 'user', content: input }],
      })
      return completion.choices[0].message.content
    }
  )
}

TypeScript

Python

Instrument once

Add OTEL-compatible telemetry to your existing LLM calls to capture prompts, inputs, outputs, and context.

This gets the loop running and gives you visibility from day one

Learn from production

Review traces, add feedback, and uncover failure patterns as your system runs.

Steps 1–4 of the loop work out of the box

Go further when it matters

Use Latitude as the source of truth for your prompts to enable automatic optimization and close the loop.

The full reliability loop, when you’re ready

Integrations

Integrates with your stack

Latitude is compatible with the majority of the platforms used to build LLM systems

How we helped Boldspace set up smart kitchen devices

Start the reliability loop with lightweight instrumentation. Go deeper when you’re ready.

Dan, CEO @ Boldspace

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Conversion rate increased from 4% to 8% on deals touched by Enginy campaigns.

4% conversion boost

Conversion rate increased from 4% to 8% on deals touched by Enginy campaigns.

FAQ

Asnwer to the most popular questions

Do I need evaluations if I'm already logging my LLM calls?

How is this different from just using an LLM to judge outputs?

Is the annotation step going to slow us down?

How quickly can I run my first eval on production data?

What if our quality criteria change over time?

Can we use this alongside our existing testing setup?

What happens when an eval fails?

Is there a free trial, or do I need to commit upfront?