Engineering
Agent Model Fallback Routing Guide: Reliability Without Surprise Spend
agent model fallback routing: compare workflow, cost signals, source dates, and TokenLab API paths before choosing a model for production.
CryptoCrypto
Engineering
AI Agent Memory: Why It Keeps Disappearing and How to Fix It
AI agents forget conversations when memory consolidation fails. We built a dual-layer fallback system that chains 5 models to guarantee zero memory loss, while cutting consolidation costs by 70%.
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TokenLab
Engineering
Why Your Semantic Cache Is Returning Wrong Answers
We found that 95% of our semantic cache hits were false positives. The root cause: embedding vectors dominated by fixed template text. We dug into the production data, read the papers, and built a two-layer fix.
T
TokenLab
Engineering
Building AI Agents with Multiple Models: A Practical Architecture Guide
Why single-model agents hit a ceiling, and how to build multi-model agents that route tasks to the right model for cost, speed, and capability optimization.
T
TokenLab
Engineering
How to Cut Your AI API Costs by 30% Without Changing Models
Prompt caching, model routing, and batch processing can dramatically reduce your AI API bill. Here's exactly how, with code examples and real cost breakdowns.
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TokenLab
Engineering
DeepSeek V4 Pro Guide: Architecture, Benchmarks, and Practical Usage
DeepSeek V4 Pro delivers state-of-the-art reasoning and coding capabilities at a fraction of the cost of closed-source alternatives. Learn how it works, when to use it, and how to integrate it.
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TokenLab