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Getting Started

Introduction

Introspection continuously improves your AI systems with production feedback and frontier practices.
Introspection dashboard

Modern AI products are compound systems: models, orchestration, tools, context, and guardrails all interacting. The model is no longer the bottleneck. The system around it is.

Introspection helps teams continuously improve that system. It combines production feedback with code-aware review workflows so failures become concrete outputs your team can act on: issues, reports, investigations, and pull requests.

Getting Started

  1. Connect your repository: Introspection indexes your codebase and maps systems to code.
  2. Let the agent instrument the system: instead of wiring everything by hand, the agent identifies what to trace and drafts the instrumentation work for review.
  3. Review your first findings: inspect systems, reports, issues, and feedback as they start to accumulate.
  4. Run tasks to investigate or fix: launch sandboxed agent workflows that produce evidence, patches, or pull requests.

Along the way, Introspection gives you:

  • System reviews: continuously assess your AI architecture against frontier practices as models, APIs, and patterns change
  • Production feedback: understand what is actually happening in production through traces, conversations, and user feedback
  • Tracked issues and reports: turn findings into evidence-backed issues, reports, and prioritized recommendations
  • Sandboxed agent tasks: investigate problems, draft fixes, and open pull requests inside scoped execution environments
  • Human approval: agents draft, your team reviews. Nothing changes without approval

How It Works

Introspection runs two feedback loops around your system.

  • The System Loop reviews how your AI system is built: orchestration, tool design, context engineering, model selection, and emerging best practices.
  • The Data Loop reads what is happening in production: traces, agent failures, user frustration, and patterns that would otherwise stay invisible.

Those loops surface work across the core product surfaces:

  • Systems: AI services discovered in your codebase and reviewed continuously
  • Issues: concrete problems linked to architecture gaps or production behavior
  • Reports: higher-level reviews and maturity snapshots across a system
  • Feedback: user signals analyzed and triaged at scale
  • Conversations: multi-turn interactions resolved to users, agents, and context
  • Tasks: sandboxed agent workflows that investigate problems and draft changes

Together, they produce work your team can review and ship: tracked issues, maturity reports, supporting evidence, investigation files, and pull requests.

Go Deeper

Last updated on
Architecture