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Observability for local agents

If you can't reconstruct what it thought, which tool it called, and why it failed, you don't have an agent—you have a black box with permissions. Observability turns a demo into a maintainable system.

  • Define useful traces for agents with tools and RAG.
  • Separate technical logs, decisions, and evidence.
  • Detect loops, latency, repeated errors, and lack of grounding.
Terminal
trace:
  request_id: "support-1042"
  user_id: "u_123"
  model: "local-qwen"
  route: "local"
  steps:
    - tool: "search_qdrant"
      latency_ms: 182
      chunks: 5
    - tool: "draft_reply"
      latency_ms: 4210
  guardrails:
    repeated_tool_calls: 0
    human_approval_required: true
  outcome: "draft_created"

What to always log

  • Model, runtime, and chosen route.
  • Tools called, arguments, and summarized result.
  • RAG chunks used and source.
  • Repeated errors and strategy changes.
  • Whether there was human approval or automatic cutoff.

Official sources

  • Langfuse observability overview
  • Langfuse tracing
  • OpenTelemetry traces
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Complete Aulafy map

How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

  1. 1Understand
  2. 2Apply or build
  3. 3Operate with confidence
01

Choose an application

Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.

Continue into the technical branch when you need to maintain code, data, or infrastructure.

02

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

This branch prepares you to design and operate reliable AI systems.

03

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

You do not need every course: choose the component your system needs and return as it grows.

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