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TO:      AgentField <contact@agentfield.ai>
SUBJECT: AgentField + agent debugging

Hey,

Saw agentfield — building observable AI agents is exactly the right direction. 

One thing we hear from teams scaling agents: debugging which step failed (and how much it cost) gets messy fast. AgentLens auto-detects hallucinations and gives you a waterfall view of every LLM call — 2-line integration, self-hosted so data stays in your infra.

A German legal tech team cut debugging time by 90% with it: https://llm-evaltrack-production.up.railway.app/case_study.html

Worth a 20-min demo?

--
Soufian Azzaoui
AgentLens — LLM Observability for EU AI Teams
https://llm-evaltrack-production.up.railway.app/landing.html
Case study: https://llm-evaltrack-production.up.railway.app/case_study.html

==================================================
TO:      ZenML <support@zenml.io>
SUBJECT: Kitaru + agent debugging — saw your durable execution work

Hi,

Saw kitaru handles agent orchestration on ZenML. One thing we hear from teams building durable agents: debugging which step failed (and why) eats hours.

AgentLens auto-scores every LLM call and catches hallucinations — zero config. A German legal tech team cut debugging time by 90% this way: https://llm-evaltrack-production.up.railway.app/case_study.html

Self-hosted, so data stays in your infra.

Worth 20 minutes to see how it'd fit?

--
Soufian Azzaoui
AgentLens — LLM Observability for EU AI Teams
https://llm-evaltrack-production.up.railway.app/landing.html
Case study: https://llm-evaltrack-production.up.railway.app/case_study.html

