Agnost AI GTM brain

Agnost AI monitors production AI agents and helps teams turn failures into tested fixes and specialized models.

category

AI agent monitoring and improvement

market profile

  • text: Conversational agents can return technically successful responses while users quietly fail to complete tasks.
  • text: Production traces can inform evaluation sets and models specialized for a narrow agent workload.

personas

  • title: AI engineering lead
    pains:
    • Agent failures hide in large volumes of successful API responses
    • Manual trace review and regression testing slow fixes
  • title: Product manager for conversational agents
    pains:
    • Users abandon conversations without filing actionable bug reports
    • Cannot prioritize recurring failure patterns across conversations
  • title: ML infrastructure lead
    pains:
    • Frontier model cost and latency rise with agent volume
    • Specializing models requires relevant examples and reliable evaluation

icp

  • text: B2B software teams with live customer-facing chat or voice agents and enough conversations to generate recurring traces; company size unspecified.
  • text: Agent builders with developers able to instrument projects and evaluate suggested fixes.

message pillars

  • text: Monitor actual production agent behavior.
  • text: Connect failure detection to experiments and proposed fixes.
  • text: Make agent improvement continuous.

objections

  • objection: We already have traces in an observability tool.
    response: Compare whether existing tools surface recurring user failures and produce testable fixes, using a sample of your own conversations.
  • objection: Conversation data is sensitive.
    response: Review retention, access controls and deployment options with security before sending production data.
  • objection: A specialized model might regress on edge cases.
    response: Evaluate it on held-out production tasks and monitor quality alongside latency and cost.

competitors

  • name: Langfuse
    why: Offers LLM tracing and evaluation for agent teams.
  • name: LangSmith
    why: Provides agent tracing, monitoring and evaluation.
  • name: Arize AI
    why: Offers AI observability and production debugging.

influencers

  • name: Parth Ajmera
    url: https://www.linkedin.com/in/parthajmera
    why: Co-founder discussing production traces and specialized models.
  • name: Shubham Palriwala
    url: https://www.linkedin.com/posts/shubhampalriwala_excited-to-share-that-we-at-agnost-ai-have-activity-7395058939727458304-FIL8
    why: Co-founder discussing agent failures and funding.

keywords

  • AI agent monitoring
  • agent failure detection
  • LLM observability
  • conversational AI analytics
  • agent trace evaluation
  • fine tune agent models

voice

  • technical and concise
  • action-oriented
  • developer-friendly

signals

  • text: A team has launched a customer-facing agent and sees escalations or abandonment that ordinary error metrics miss.
  • text: Growing trace volume or inference bills make manual review and generic frontier models harder to sustain.

stack

  • Claude
  • Codex
  • Cursor

pricing

model: unknown

motion

hybrid

channels

  • developer documentation
  • LinkedIn
  • YC launch
  • SEO

confidence

medium

Founders

  • name: Shubham Palriwala
    title: Founder/CEO
    role: CEO
    bio: Co-founder and CEO of Agnost AI. Youngest Engineer on Cisco's Analytics team, First Hire at Formbricks (Open-Source Qualtrics), wrote code in Bitcoin & other open-source projects across OWASP and the Linux foundation.
  • name: Parth Ajmera
    title: Founder/CTO
    role: CTO
    bio: Co-Founder/CTO at Agnost AI. Graduated in Computer Science from IIT Madras, ranking 159 among over 1 million candidates. Previously led graphics engineering at Infurnia and built terabyte scaled data pipelines at Microsoft

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