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Engineering insights
Introducing Agent SLOs: The New Primitive for AI Reliability
Why classifying accuracy, context completeness, and response relevance as SLO dimensions changes how enterprises govern AI agents in production.
100+ LLM Providers, Zero Vendor Lock-In: How Sparge Uses LiteLLM
The architectural decision to abstract all AI intelligence through LiteLLM, and why it means swapping from GPT-4o to Ollama is one environment variable.
847 Incidents, 73 Minutes, $2.3M: The Splunk Schema Change Story
A detailed post-mortem of the context quality degradation scenario that shaped Sparge's core design. How a single schema version change caused 73 minutes of systematic mis-triage.
Observable by Design: Why Sparge Instruments Itself
An observability platform that is not itself observable is a governance liability. How Sparge applies the same rigour to its own /metrics endpoint that it applies to the agents it monitors.
EU AI Act Technical Compliance: What Articles 9–61 Actually Require
A practical breakdown of the technical evidence requirements for high-risk AI system operators, and exactly how Sparge's immutable audit log satisfies each article.
Google SRE's Burn Rate Alerting, Applied to AI Agent Decision Quality
The mathematics of error budget burn rate, and why firing alerts when the budget is being consumed 5× faster than normal changes everything about how you respond to agent degradation.
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