Tagged articles

Glean

4 articles · Page 1 of 1

Sep 25, 2026 · Artificial Intelligence

Why LLMs Shouldn't Think Everything: Jev, Codex, and the Heterogeneous Agent Revolution

The article argues that AI agents waste compute by using large language models for low-entropy decisions, and introduces Jev, a lightweight decision model that handles tool routing, code search, and log triage in milliseconds, enabling a System 1/System 2 architecture where Jev filters noise before Codex performs deep reasoning, a pattern mirrored by Glean's enterprise search stack.

AI AgentsCodexGlean

0 likes · 16 min read

Why LLMs Shouldn't Think Everything: Jev, Codex, and the Heterogeneous Agent Revolution

Tech Architecture Stories

Sep 13, 2026 · Artificial Intelligence

Engram's $98M Bet: Moving Enterprise AI Beyond RAG to Continuous Learning

Engram raises $98M to replace inference-time RAG with offline 'Study Compute,' using parametric memory, structured notes, and raw retrieval to let models continuously learn enterprise context, demonstrated via a synthetic law firm experiment showing 10x lower cost and better search behavior than base models.

Continuous LearningEngramEnterprise AI

0 likes · 18 min read

Engram's $98M Bet: Moving Enterprise AI Beyond RAG to Continuous Learning

Tech Architecture Stories

Sep 12, 2026 · Artificial Intelligence

Glean's $300M ARR Horizontal Neo-Lab: Context-First AI, Trace Learning & Waldo Model

This article analyzes Glean's evolution into a $300M ARR horizontal enterprise AI platform, detailing its context-first architecture, harness engineering with sandbox isolation, trace learning loop for continuous improvement, specialized Waldo model for search planning, private evaluation framework, and strategic comparison with vertical rival Harvey, offering five engineering takeaways for enterprise AI builders.

Enterprise AIGleanHarvey Comparison

0 likes · 15 min read

Glean's $300M ARR Horizontal Neo-Lab: Context-First AI, Trace Learning & Waldo Model

ThinkingAgent

Sep 4, 2026 · Industry Insights

Enterprise AI's Real Moat: How Glean, Palantir, and OpenAI Build Context

This analysis compares three proven enterprise AI context-building approaches: Glean's knowledge-centric Enterprise Graph, Palantir's decision-centric Ontology, and OpenAI's task-centric Harness framework, showing how each addresses different organizational needs and why context—not models—is the lasting competitive advantage.

AI AgentsContext EngineeringEnterprise AI

0 likes · 27 min read

Enterprise AI's Real Moat: How Glean, Palantir, and OpenAI Build Context