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SLOs and Error Budgets for AI Features
Define and enforce SLOs — SLO definition, error budgets, burn-rate alerts.
What You Will Learn
- Define SLOs for AI features.
- Set error budgets.
- Track burn rate.
- Alert on burn.
- Use budgets for decisions.
Why This Matters
Without SLOs, you don't know if Copilot is 'reliable enough'. SLOs turn reliability into measurable targets.
Concept Explained
SLO = target reliability (e.g., 99.5% successful Copilot calls). Error budget = 0.5% allowed failures. Burn rate = how fast you're consuming budget.
How It Works
Define SLO per surface (inline 99.9%, chat 99.5%, agent 99%). Track success rate. Calculate burn rate. Alert if burning too fast. Use budget for feature decisions (slow down new features when budget low).
Step-by-Step Tutorial
1. Define SLOs
Per surface. Inline: 99.9%. Chat: 99.5%. Agent: 99%.2. Error budget
1 - SLO. Inline: 0.1%. Chat: 0.5%. Agent: 1%.3. Track success rate
Per surface, daily.4. Burn rate
How fast consuming budget. Alert if >2x normal.5. Use budget
When budget low, freeze new features. Focus on reliability.Real-World Example
A team set SLO: chat 99.5%. One week, success rate dropped to 98% — burning budget 3x normal. Alerted. Investigated: model upgrade had regression. Rolled back. Budget restored.
Example Prompts / Commands / Code
"""Surface SLO Error budget Window
-------------------------------------------------------
Inline 99.9% 0.1% 30 days
Chat 99.5% 0.5% 30 days
Agent 99.0% 1.0% 30 days
Extensions 99.5% 0.5% 30 days
Error budget per 30 days:
- Inline: 43 min of failures allowed
- Chat: 3.6 hours
- Agent: 7.2 hours
- Extensions: 3.6 hours
""""""Burn rate = (actual error rate) / (allowed error rate)
Example: Chat SLO 99.5% (0.5% allowed)
- Actual error rate today: 1.0%
- Burn rate: 1.0% / 0.5% = 2x
Alert thresholds:
- Burn rate >1x for 1 hour: warning
- Burn rate >2x for 30 min: page
- Burn rate >5x for 5 min: page + freeze deploys
Actions:
- Warning: investigate
- Page: rollback or mitigate
- Freeze: stop new deploys; focus on reliability
"""Common Mistakes
- SLOs too strict — budget always low, team paralyzed.
- SLOs too loose — user impact before action.
- No burn rate tracking — budgets consumed silently.
- Not using budget for decisions — feature freeze when budget low.
Best Practices
- Define SLOs per surface (inline strictest, agent loosest).
- Track success rate daily.
- Calculate burn rate; alert on >2x.
- Use budget for decisions: freeze new features when budget low.
- Review SLOs quarterly.
Troubleshooting
| Problem | How to Fix |
|---|---|
| SLOs always violated | May be too strict. Or reliability issue. Investigate. |
| Budget always full | SLOs may be too loose. Tighten. |
Practical Exercise
Your Turn
Define SLOs for your Copilot usage. Calculate error budgets. Track success rate for a week.
Professional Challenge
Implement burn rate alerting. Use budget for feature freeze decisions over a quarter.
Key Takeaways
- SLOs = target reliability per surface.
- Error budget = 1 - SLO.
- Track burn rate; alert on >2x.
- Use budget for feature decisions: freeze when low.
- Review SLOs quarterly.
Frequently Asked Questions
What SLOs are typical?
How long is budget window?
Further Reading
Official References
SEO Metadata
SEO title: SLOs and Error Budgets for AI Features
Meta description: Define and enforce SLOs — SLO definition, error budgets, burn-rate alerts.
Primary keyword: slos and error budgets for ai features
Secondary keywords: slos and error budgets for ai features
Search intent: Informational
URL slug: /slos-error-budgets-ai-features
Categories: AI Tools, GitHub Copilot
Tags: GitHub Copilot, Professional, SLO, Error Budget, Reliability, Production, IMCSEIAN, Tutorial, IMCSEIAN
Featured image concept: IMCSEIAN lesson card for SLOs and Error Budgets for AI Features
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