Mirrors GTM brain

Mirrors helps engineering teams identify recurring AI agent failures, apply scoped guidance and measure the effect in production.

category

AI agent reliability and guidance

market profile

  • text: As production agents accumulate fixes in system prompts, teams must maintain increasingly broad instructions without knowing which ones improve outcomes.
  • text: The product launched in 2026; an independent directory notes that pricing, named customers and independent performance verification are not public.

personas

  • title: AI agent engineering lead
    pains:
    • Growing system prompts contain patches for unrelated customer situations
    • A correction for one workflow risks changing another
  • title: Applied AI engineer
    pains:
    • Finding recurring failures across production sessions
    • Testing which past conversations a proposed fix would reach and whether it helps
  • title: Customer support automation owner
    pains:
    • An agent repeats troubleshooting or makes avoidable policy mistakes
    • Account-specific guidance must stay consistent as requirements change

icp

  • text: Engineering-led software companies operating customer-facing agents with sufficient production traffic to identify repeated errors; company size is unspecified.
  • text: Teams handling account-specific policies or workflows that need to evaluate and target corrections without replacing their agent framework.

message pillars

  • text: Identify repeated mistakes across production sessions.
  • text: Preview the scope of a correction before activating it.
  • text: Measure the effect against outcomes without the correction.
  • text: Apply targeted fixes only in their intended situations.

objections

  • objection: Why not patch the system prompt?
    response: Mirrors scopes each correction to specified situations and measures it separately, reducing broad prompt accumulation.
  • objection: Could a fix affect the wrong conversations?
    response: Preview the past sessions a correction would reach, then test neighboring scenarios before activation.
  • objection: Must we replace our framework?
    response: The company says its SDK works across agent frameworks; validate integration and latency in a pilot.

competitors

  • name: LangChain
    why: Its LangSmith product overlaps in agent tracing, evaluation and production debugging.
  • name: Langfuse
    why: Offers agent observability, evaluation and prompt management, an adjacent improvement workflow.
  • name: Braintrust
    why: Provides evaluation and production monitoring for AI applications and agents.

influencers

  • name: Aryan Singhal
    url: https://xitter.gq/ai_singhal
    why: Co-founder discusses agent correction and integration.
  • name: Aditya Gupta
    url: https://teapawt.net/idyeetya_
    why: Co-founder and CTO posting about agent reliability.

keywords

  • AI agent reliability
  • production agent monitoring
  • agent prompt management
  • AI agent corrections
  • agent evaluation platform
  • LLM agent observability

voice

  • Direct and developer-oriented
  • Specific about production workflows
  • Measurement-focused

signals

  • text: A team has deployed customer-facing agents and is repeatedly patching system prompts.
  • text: Agent behavior varies by customer account and changing operational policies.

stack

  • LangChain
  • LangGraph
  • LlamaIndex
  • CrewAI
  • OpenAI
  • Anthropic

pricing

model: unknown

motion

sales_led

channels

  • X
  • YC launch
  • developer documentation
  • SEO

confidence

low

Founders

  • name: Aryan Singhal
    title: Founder/CEO
    role: CEO
    bio: Co-Founder & CEO @ Mirrors (YC F26). Published six AI/ML research papers in high school at top AI conferences such as NeurIPS and EMNLP. Prev. ML research at UT Austin and UCSB.
  • name: Aditya Gupta
    title: Founder/CTO
    role: CTO
    bio: Founder at Mirrors (YC F26). Studied Computer Engineering at Purdue.

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