AI & Machine Learning

The Model Wasn't the Hard Part. The Harness Was.

32 sec read23 views0 listens

The shift towards AI agents—systems designed to do something rather than simply answer questions—is revealing that the "harness" surrounding the core language model is increasingly critical. This harness encompasses infrastructure like guardrails, identity management, memory systems, and execution environments, essentially managing the agent's actions and ensuring safety and reliability. As a result, product development for AI agents now requires more focus on these supporting systems than solely optimizing the underlying model itself, impacting how requirements are defined and evaluated.

Read the full article at Towards AI - Medium

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.

You Might Like

AI & Machine Learning1 day ago31 sec read

Replace Guesswork with Statistics for Testing Your LLM Applications.

Evaluating large language model (LLM) applications often relies on subjective testing, which can lead to inconsistent results and deployment issues. A new approach suggests using statistical methods, specifically bootstrapped resampling, to compare m...

Ali Nemati

AI & Machine LearningSep 828 sec read

When You Don't Need MCP

MCP (Model Context Protocol) is an open protocol that allows AI applications to interact with external tools in a unified manner, but it may not always be necessary. Developers can opt for simpler alternatives like function calling or CLI integration...

Ali Nemati

AI & Machine LearningApr 1534 sec read

LLM Inference Infrastructure from Scratch: How to Fine-Tune Correctly, Part 7

The article discusses the critical role of efficient inference infrastructure for large language models (LLMs), focusing on memory management techniques like PagedAttention and RadixAttention that significantly improve throughput by optimizing KV cac...

Ali Nemati