Executive Summary

Agentic RAG systems combine: 
 ●       Reasoning models  
 ●       Retrieval systems 
 ●       Tool use (increasingly via MCP) 
 ●       Memory (short-term, long-term, and now tiered/benchmarked) 
 ●       Workflow orchestration 
 ●       Governance controls 
 ●       A harness layer that governs all of the above 
 The goal is not maximum autonomy. The goal is reliable, auditable, policy-aligned autonomous execution. Modern production systems optimize for: 
 ●       Accuracy 
 ●       Cost efficiency 
 ●       Safety 
 ●       Explainability 
 ●       Human oversight 
 ●       Operational resilience 
 NEW IN 2026  
 The single biggest framing shift: the model is now understood as the smallest part of the system. Practitioners increasingly describe production agents with the equation  
 Agent = Model + Harness,  
 where the harness — not the model — determines reliability, and benchmarks such as Harness-Bench show that swapping the harness around an identical model pool can move task-success scores by over 20 points.