Program syllabus

AI Assistant Observability & SLOs

Instrument your assistants like production services — traces, metrics, logs, and SLOs that make quality visible and actionable for engineering, product, and leadership.

Program overview · Audio

A short overview of the program: who it's for, what we cover, and how to get the most value out of it as a busy professional.

What we’ll cover

Use this to move from “the bot feels off” to concrete signals, dashboards, and alerts your team can own.

Module 1

Observability foundations

  • • What makes AI assistants different from classic services
  • • Key telemetry: traces, spans, logs, metrics, events
  • • Designing signals around workflows, not just infra

Module 2

Metrics for AI quality

  • • Defining assistant-specific KPIs and guardrails
  • • Turn-by-turn vs. session-level metrics
  • • Linking metrics back to business outcomes

Module 3

SLOs, alerts & dashboards

  • • Choosing SLI candidates that really matter
  • • Writing SLOs that humans can read and own
  • • Dashboards for operators vs. executives

1. Observability foundations for AI assistants

We translate classic observability concepts into the messy, probabilistic world of LLMs and tools.

We start by mapping your existing assistant architecture: providers, tools, retrieval layers, and UI surfaces. Then we decide where to capture signals so you can actually debug issues.

  • • Request, span, and conversation IDs done right
  • • Logging prompts, tool calls, and user feedback safely
  • • The minimum set of fields you’ll be grateful for later
  • • Choosing where observability lives: app, gateway, or platform

Artifact: observability map

Together we build a simple diagram that shows where telemetry is captured across:

  • • UI / channels (web, chat, Slack, etc.)
  • • Orchestration / agent layer
  • • Tools, retrieval, and external APIs

This becomes the blueprint for your engineering team to implement instrumentation consistently.

2. Metrics that describe quality, not just traffic

We define metrics at the level where humans feel pain: conversations and workflows.

We’ll design a metric set that balances business outcomes with measurable behavior. Think less “token count” and more “session success rate” and “handoff quality.”

  • • Session success, abandonment, and escalation rates
  • • Tool success / failure and retry behavior
  • • Content coverage and retrieval hit/miss ratios (for RAG)
  • • Feedback signals: thumbs, edits, CSAT, and annotation samples

Example: assistant quality scorecard

We’ll co-design a monthly scorecard for one of your assistants that leadership can understand at a glance:

  • • 3–5 top-line metrics with clear targets
  • • Leading indicators vs. lagging ones
  • • How metrics map back to business goals

3. SLOs, alerts, and runbooks

We treat assistants like services: clear expectations, owned by real humans, with playbooks for when things break.

  • • Choosing SLIs that reflect user experience
  • • Writing SLOs and error budgets for AI workflows
  • • Alert design that doesn’t melt your on-call rotation
  • • Attaching concrete runbooks to each alert

Templates you can reuse

  • • SLO definition doc for an AI workflow
  • • Alert + runbook template for incidents
  • • Checklist for shipping new prompts/models safely

4. Dashboards and review rituals

The goal isn’t just charts — it’s a shared rhythm where the whole team can see progress and risks.

We’ll design two dashboards: one for the people operating the assistant day-to-day, and one for leadership.

  • • Operator views: live health & incident drill-downs
  • • Exec views: trends, impact, and upcoming risks
  • • Monthly review rituals and decision logs

Example: review cadence

We’ll sketch a light-weight cadence your team can keep running:

  • • Weekly operator check-in (30 minutes)
  • • Monthly leadership review (45–60 minutes)
  • • Quarterly roadmap alignment on quality + features

Ready to make your assistants observable?

We usually run this as a 1–2 week engagement focused on one high-impact assistant. You bring the stack; we bring patterns, templates, and an outside view on where risk really lives.

By the end, you’ll have metrics, SLOs, dashboards, and a review cadence that fits how your team already works.

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This pairs especially well with Advanced Retrieval Engineering and AI Guardrails & Safety Engineering for a full-stack AI reliability track.