Lemma GTM brain

Lemma monitors production AI agents for silent failures, alerts engineering teams and turns resolved incidents into ongoing evaluations.

category

AI agent production monitoring

market profile

  • text: An agent can return a technically successful response while making a wrong tool call, losing context or violating a policy.
  • text: Teams need to connect production traces, incident triage and regression evaluation as agents change.

personas

  • title: Head of AI or engineering at an agent startup
    pains:
    • Silent agent failures damage customer trust before infrastructure alerts fire.
    • A small team cannot manually review every production trace.
  • title: AI agent or platform engineer
    pains:
    • Debugging a bad outcome requires correlating traces, tool calls and instructions.
    • A prompt fix can regress without a representative ongoing evaluation.
  • title: CTO of a software company deploying agents
    pains:
    • Scaling agent usage increases exposure to hard-to-predict failure modes.
    • Engineering needs a practical way to prioritize incidents and communicate their impact.

icp

  • text: Software companies running multi-step AI agents in production, particularly early-stage teams with a small AI or engineering organization.
  • text: Agent builders handling support, workflows or other customer-facing tasks where a successful API response may mask a bad outcome.

message pillars

  • text: Detect previously unknown failures rather than relying only on predefined checks.
  • text: Prioritize and alert the team where it already works.
  • text: Convert fixes into ongoing regression coverage.

objections

  • objection: We already have tracing and observability. Why add another tool?
    response: Test whether Lemma finds semantic failures in existing traces that standard success and error metrics missed.
  • objection: Will alerts create noise for engineers?
    response: Pilot with a representative agent and compare issue grouping, severity and actionable alerts with the team's current workflow.
  • objection: Can production traces be handled securely?
    response: Review the trust center, data handling and retention terms before sending sensitive traces.

competitors

  • name: LangSmith
    why: Provides agent tracing, evaluations and production monitoring.
  • name: Langfuse
    why: Open-source LLM observability and evaluation platform for tracing agent runs.
  • name: Braintrust
    why: Combines production trace scoring and evaluations for AI applications.
  • name: Latitude
    why: Offers AI agent observability and failure detection for production teams.

influencers

  • name: Jerry Zhang
    url: https://nitter.rdrama.net/zjearbear
    why: Lemma cofounder who posts about agent reliability and developer events.

keywords

  • AI agent production monitoring
  • agent observability
  • LLM silent failure detection
  • agent trace analysis
  • AI agent regression testing
  • LLM evaluation monitoring
  • agent incident triage

voice

  • technical and direct
  • developer-oriented
  • failure and resolution focused

signals

  • text: A team has deployed customer-facing agents whose outputs need review beyond HTTP success rates.
  • text: An engineering team is adding agents or seeing recurring prompt, tool-call or policy failures in production.

stack

  • Slack
  • MCP

hiring

  • Product Engineer, San Francisco
  • AI/ML Engineer, San Francisco

pricing

model: unknown

motion

sales_led

channels

  • website inbound
  • developer documentation
  • X
  • events
  • YC directory

confidence

medium

Founders

  • name: Jerry Zhang
    title: Founder/CEO
    role: CEO
    bio: Co-founder @ Lemma (YC F25) | Reliability platform for AI agents
  • name: Cole Gawin
    title: Founder/CTO
    role: CTO
    bio: developing the next generation of intelligent systems @ lemma (f25) | ex-USC

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