Introduction

Deep overview of Obtrace architecture, workflows, and how to adopt it in production

Obtrace exists to solve a common failure pattern in software operations: incidents are detected quickly, but diagnosis and resolution still require too much manual correlation across disconnected tools.

The Problem We Solve

Most teams operate with fragmented observability:

  • Logs in one platform
  • Traces in another
  • Error tracking elsewhere
  • Deploy context in CI/CD tools
  • User-impact evidence disconnected from backend telemetry

This fragmentation increases mean time to resolution because engineers spend time assembling context instead of fixing issues.

Obtrace Approach

Obtrace centralizes incident context and adds AI-native workflows:

  1. Detect anomalies in production signals.
  2. Correlate telemetry and runtime context.
  3. Surface probable root cause with evidence.
  4. Accelerate remediation through guided actions.

The core principle is simple: fewer context switches, faster and safer incident closure.

Platform Architecture (Conceptual)

  • SDK layer: language and runtime instrumentation.
  • Ingestion layer: telemetry normalization and transport.
  • Correlation layer: cross-signal linking by service, environment, and time.
  • Analysis layer: incident intelligence and AI-assisted diagnosis.
  • Workflow layer: documentation, references, MCP/LLM context, and Ask AI entry points.

What To Instrument First

Start with the highest-value surfaces:

  1. Critical backend API/service.
  2. Public-facing frontend (if applicable).
  3. One asynchronous worker or queue consumer.
  4. Deployment metadata in CI/CD.

This gives enough correlated signal to make AI-assisted analysis useful from day one.

Adoption Model

Phase 1: Baseline (Day 1)

  • Configure authentication.
  • Install one SDK.
  • Validate telemetry arrives.

Phase 2: Coverage (Week 1)

  • Expand instrumentation to core services.
  • Add runtime integrations.
  • Standardize tags (service, env, version, region).

Phase 3: Operations (Week 2+)

  • Define incident response runbooks using Obtrace data.
  • Enable Ask AI workflows for faster triage.
  • Integrate machine-readable context (llm.txt, mcp.json, MCP docs) for internal assistants.

How To Read These Docs

Use this order for fast time-to-value:

  1. Authentication
  2. How to use
  3. SDK Catalog
  4. Project Guides
  5. Integration Matrix

Then go deep in your stack-specific SDK and deployment integration pages.

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