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Decision Layer

8 articles · Page 1 of 1
DataFunTalk
DataFunTalk
Oct 4, 2026 · Artificial Intelligence

Why AI Agents Need a Dedicated Decision Layer: Inside Jev's Structured Judgment Model

TypeSafe's Jev model introduces a dedicated Decision Layer for AI agents, replacing free-text generation with structured judgments (Choice, Score, Noul) for high-frequency routing, tool selection, guardrails, and context compaction, enabling faster, cheaper, and more calibrated decisions than full LLM calls.

Agent architectureDecision LayerJev
0 likes · 24 min read
Why AI Agents Need a Dedicated Decision Layer: Inside Jev's Structured Judgment Model
DataFunSummit
DataFunSummit
Sep 30, 2026 · Artificial Intelligence

Why AI Agents Need a Dedicated Decision Layer: Inside Jev's System One Model

TypeSafe AI's Jev introduces a specialized 'System One Model' that replaces free-form LLM generation with fast, cheap structured decisions (Choice, Score, Noul) for high-frequency Agent control tasks like tool routing, guardrails, and context compaction, revealing an emerging Decision Layer in Agent architecture.

AI AgentsDecision LayerHeterogeneous Models
0 likes · 21 min read
Why AI Agents Need a Dedicated Decision Layer: Inside Jev's System One Model
DataFunTalk
DataFunTalk
Sep 28, 2026 · Artificial Intelligence

Why Agents Are Splitting Off a Decision Layer: Jev and the System One Model

TypeSafe's Jev introduces a specialized 'System One' model for high-frequency structured decisions like routing and tool selection, enabling agents to offload bounded judgments from expensive generative LLMs into a fast, calibrated decision layer that sits between rule-based logic and complex reasoning.

Agent architectureDecision LayerJev
0 likes · 23 min read
Why Agents Are Splitting Off a Decision Layer: Jev and the System One Model
DataFunSummit
DataFunSummit
Sep 20, 2026 · Industry Insights

Palantir 2026 Roadmap: Enterprise Agents' Next Battle Is Trust, Not Capability

Palantir's 2026 roadmap reveals a shift from AI model capabilities to trustworthy enterprise agents, emphasizing Ontology-defined business actions, engineering safeguards like branching and permission debugging, and a competitive landscape focused on controlling the decision layer in organizations.

AI AgentsAIPChina Market
0 likes · 20 min read
Palantir 2026 Roadmap: Enterprise Agents' Next Battle Is Trust, Not Capability
DataFunSummit
DataFunSummit
Sep 17, 2026 · Industry Insights

Palantir's True Moat: The Agent Engineering Stack for Trusted Enterprise Decisions

Palantir's 2026 product updates reveal its competitive advantage lies not in models or ontology alone, but in a complete engineering system—global branching, permission debugging, and MCP integration—that lets AI agents safely execute real business actions while maintaining governance, shifting enterprise AI budgets from model procurement to decision-process reconstruction.

AI AgentsAIPAgent Engineering
0 likes · 21 min read
Palantir's True Moat: The Agent Engineering Stack for Trusted Enterprise Decisions
DataFunSummit
DataFunSummit
Sep 16, 2026 · Industry Insights

Palantir's Moat: The Engineering System That Lets AI Agents Safely Run Business

Palantir's 2026 updates reveal its true competitive advantage: not just Ontology or AIP, but a complete engineering system—including Global Branching, permission debugging, and MCP integration—that lets AI agents safely execute real business actions while maintaining audit trails and governance, shifting enterprise AI budgets from model procurement to decision-process reconstruction.

AI AgentsAIPDecision Layer
0 likes · 20 min read
Palantir's Moat: The Engineering System That Lets AI Agents Safely Run Business
Data Bricklaying Diary
Data Bricklaying Diary
Aug 11, 2026 · Artificial Intelligence

Three-Layer Ontology Intelligence: Separating Semantics, Decisions, and Actions

This article explains why ontology intelligence systems require a three-layer architecture—semantic layer for defining business meaning, decision layer for forming explainable action plans, and action layer for controlled state changes—with explicit handoff contracts to avoid mixing rules and side effects into prompts.

AI architectureDecision LayerThree-Layer Architecture
0 likes · 20 min read
Three-Layer Ontology Intelligence: Separating Semantics, Decisions, and Actions
Data Bricklaying Diary
Data Bricklaying Diary
Aug 6, 2026 · Artificial Intelligence

From Ontology Semantics to Ontology Intelligence: Why Understanding Business ≠ Driving It

The article distinguishes ontology semantics (AI understanding business objects, states, rules) from ontology intelligence (explainable decisions, contract-constrained actions, auditable loops), outlining seven engineering capabilities needed to close the loop and emphasizing a minimal viable loop around high-value decisions.

Agent RuntimeAgent architectureDecision Layer
0 likes · 16 min read
From Ontology Semantics to Ontology Intelligence: Why Understanding Business ≠ Driving It