Improve your production agent systems from past failures

We analyze thousands of production traces, build custom evaluations from your domain, and ship measurable improvements iteration after iteration.

Used by teams building with

Anthropic
OpenAI
Gemini
Vercel AI SDK
LangChain
LangGraph
Anthropic
OpenAI
Gemini
Vercel AI SDK
LangChain
LangGraph
Deep Observability

Your agent has been failing in production this week. You just don't know where.

Kayba traces the full execution tree and attributes every failure to the exact agent that caused it.

Agent Execution Trace
run_4281

149.6s

Total execution

5 spans 1 failure
Orchestrator
149.6s
├─
Issue Reader
11.2s
├─
Code GeneratorSub-agent failure
64.8s
└─
Edit toolTool misuse
4.6s
├─
Test Runner
73.6s

Failure attributed

Code GeneratorEdit tool (tool misuse)

Dynamic Evals

Generic evals won't tell you why your agent failed. Kayba builds the ones that will.

Kayba builds custom benchmarks for your domain from your own production traces, so you can really measure whether your agent is improving against your real failure patterns.

  • Metrics generated from your specific failure patterns
  • Impact scoring to prioritize what matters most
Metric Health

Latest value per metric, sorted by impact

Metric
Score
Impact

hallucination avoidance rate

Response Quality

29.1%
493

policy compliance rate

Policy Compliance

57.4%
258

error recovery success

Error Handling

54.4%
260

tool parameter accuracy

Tool Usage

62.5%
236

context retention accuracy

Context Management

65.9%
157
Improvements

Every failure becomes a shipped improvement.

Kayba turns every failure pattern we detect into a targeted, measurable improvement.

  • Agent harness optimization and context learning in every cycle, delivering compounding improvements
  • Every shipped improvement verified on your evals
  • Doubles agent consistency on τ2-bench: +34% first-attempt success and +100% across four attempts
τ2-bench · Claude Haiku 4.5
BaselineKaybaImprovement
pass^141.2%55.3%+34.2%
pass^228.3%44.2%+56.2%
pass^322.5%41.2%+83.1%
pass^420.0%40.0%+100.0%

τ2-bench is a real-world agent benchmark by Sierra Research.

Run after run, your agent gets better.

Each cycle adds new evals, surfaces new failure patterns, and ships new improvements. The compounding shows up after just a few runs.

Trends

How metrics are changing across runs

Current Avg

65.3%

+6.7% vs prev run
8 runs
20 metrics

Fits into your existing stack

Already running Langfuse, LangSmith, or OpenTelemetry? We pull from your existing stream. If not, drop in the Kayba SDK and wrap your agent in two lines.

Agent Frameworks

OpenAI Agents SDK

Gemini SDK

Claude Agent SDK

Vercel AI SDK

LangChain SDK

LangGraph SDK

Observability & Trace Import

MLflow

LangSmith

Langfuse

OpenTelemetry

Kayba SDK

Custom API

Ready to start improving your production agent systems?

Book a demo and we'll analyze a sample of your production traces, build custom evaluations, and show you the results.

Built on research from

University of OxfordEPFLETH ZurichETH AI CenterMax Planck SocietySimons FoundationHSG
HumanLLM