SL#68 - A 131-Line Agent Scores 65% on SWE-bench. Yours Scores 67%.
Most of the scaffolding you are building around your agent buys you two percentage points at seven times the cost. Here is the evidence, and here is how to decide what to keep.
SL#60 - Your Agent Scores 94% on Memory. It Still Thinks You Live in New York.
Memory benchmarks are nearly saturated on recall. A new benchmark that penalizes stale memory shows the same systems falling apart, and getting worse the longer they run.
SL#46 - Your LLM Agent Is Drowning in Its Own Context Window
Context windows just hit two million tokens. So why are 5% of production AI requests still failing? Because the industry confused having more space with knowing what to put in it.
SL#44 - Building Agentic RAG Systems: Architecture, Reasoning Loops, and Production Considerations
The transition from simple LLM wrappers to AI Agents represents the next frontier in software engineering. While traditional Retrieval-Augmented Generation (RAG) improved LLM accuracy, Agentic RAG introduces a reasoning layer that allows the system to autonomously decide how to use data to solve a problem.
SL#42 - From Passive LLMs to Autonomous Agents: The Evolution of AI Workflows
The field of Artificial Intelligence is rapidly evolving from simple text generation to autonomous problem-solving. To understand where the industry is heading, technical professionals must distinguish between three distinct levels of AI implementation: Passive LLMs, AI Workflows, and Autonomous AI Agents.
