# Executive Summary

# 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.