Introduction

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

Obtrace is an AI-native observability platform designed for teams that need faster incident response with less operational noise.

Most failures in incident response do not come from missing data. They come from fragmented context: telemetry exists, but engineers cannot connect it fast enough under pressure.

Why This Exists

Typical production workflow today:

  1. Alert fires.
  2. Engineer opens multiple tools.
  3. Team manually correlates logs, traces, deploys, and user impact.
  4. Diagnosis takes too long.
  5. Fix is delayed because confidence is low.

Obtrace focuses on collapsing these steps by keeping incident context connected.

Product Philosophy

Obtrace is built around four principles:

  1. Context over volume: more raw data is not always better data.
  2. Correlation over isolated dashboards: cross-signal linkage is mandatory.
  3. Operations over demos: setup should survive real production traffic.
  4. AI with evidence: assistant outputs must be grounded in observed telemetry.

Conceptual Architecture

1. Instrumentation Layer

Language-specific SDKs emit logs, traces, errors, and custom metadata with a shared schema strategy.

2. Ingestion Layer

Events are normalized and tagged for consistent querying across runtimes.

3. Correlation Layer

Signals are linked by identity and time (service/env/version/release).

4. Analysis Layer

Incident timelines and AI-assisted diagnosis are built on correlated evidence.

5. Workflow Layer

Human and machine interfaces:

  • Docs + runbooks
  • Ask AI
  • MCP interfaces
  • llm.txt / mcp.json

Phase 1: Baseline (Day 1)

  • Instrument one critical backend service.
  • Validate auth, transport, and tagging.
  • Confirm useful incident context appears.

Phase 2: Coverage (Week 1)

  • Expand to key services and async workloads.
  • Add frontend instrumentation where relevant.
  • Add runtime integrations and release metadata.

Phase 3: Operationalization (Week 2+)

  • Standardize incident runbooks using Obtrace context.
  • Define SLOs/alerts with reduced noise.
  • Enable AI-assisted triage with clear ownership.

Data Modeling Guidance

Use consistent keys globally:

  • service: stable service identifier
  • env: dev, staging, prod
  • version: release/build identifier
  • region: if multi-region

Inconsistent tags are the fastest way to make observability useless.

Security and Governance Basics

  • Keep keys in secret managers.
  • Separate credentials by environment.
  • Rotate keys with ingestion validation gates.
  • Treat observability payloads as production data with policy controls.

How To Read This Documentation

Recommended flow:

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

Then go deep in runtime-specific pages and operational hardening.

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