AI Agents on DataThrillz

I Built Agentic Search Four Ways. Here’s What Actually Matters.

Tue, 17 Mar 2026 20:49:40 +0000

While traditional RAG relies on static vector lookups that often lose global context, agentic search transforms retrieval into a dynamic, reasoning-driven process. By utilizing hierarchical structures like RAPTOR, Knowledge Graph RAG and autonomous sub-agents, these systems can navigate complex, multi-hop queries that typically overwhelm standard semantic search. This shift from one-shot retrieval to iterative loops allows for parallelized processing and self-correction, ultimately providing the precision and structural awareness required for professional-grade document analysis.

The Evolution of Agentic Search: From Naive RAG to Reasoning-Driven Retrieval

Sat, 14 Mar 2026 20:18:15 +0000

As Large Language Models (LLMs) transition from simple chatbots to autonomous agents, the methods we use to feed them data must evolve. While Retrieval-Augmented Generation (RAG) remains the industry standard for grounding models in external data, its “vanilla” implementation—converting text chunks into vectors for semantic lookup—often falters when faced with interconnected documents, technical jargon, or multi-hop queries. For Machine Learning Engineers (MLEs) and Product Managers (PMs), understanding the shift toward Agentic Search is critical. This approach moves away from static lookups toward dynamic, iterative, and hierarchical strategies that mirror how a human expert navigates a complex knowledge base.

Understanding How Claude Code Works

Fri, 06 Mar 2026 14:35:47 +0000

Inside Claude Code: How Sub-Agents and Parallel Execution Define Next-Gen Coding Agents

Introduction: The Evolution of Coding Agents

Coding agents represent a fundamental shift in how developers interact with their codebases. Unlike traditional autocomplete tools or simple code generation models, modern coding agents operate autonomously across multiple files, maintain context over extended sessions, and can break down complex tasks into manageable subtasks. These systems leverage Large Language Models (LLMs) in sophisticated agentic loops where the model can call tools, observe results, and iteratively work toward task completion.