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Guides and insights on LLM observability, AI agents, and cost tracking.
LLM Observability: What to Track and Why
A practical guide to LLM observability: the metrics, events, and quality signals that explain cost, latency, failures, and user outcomes in production.
ReadRetry Patterns for AI Agents That Actually Work
Practical ai agent retry patterns for LLM failures: classify errors, add backoff and budgets, retry with intent, and measure if retries improve outcomes.
ReadDebugging AI Agent Failures in Production
A practical workflow to debug AI agent failures in production: classify failures, add observability, reproduce runs, and ship fixes without guesswork.
ReadBuilding a Self-Improving AI Agent with Feedback Loops
Design a self-improving AI agent using measurable outcomes, evaluation gates, and retry actions. Add observability so the agent learns from its own runs.
ReadHow to Track LLM API Costs Across Providers
Track LLM API costs across OpenAI, Anthropic, and more by normalizing usage, estimating cost, and attributing spend per feature, user, and run.
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