diff --git a/pipeline/test_loop_engineering_meta_prompt.py b/pipeline/test_loop_engineering_meta_prompt.py
index a2cd995..907ac5a 100644
--- a/pipeline/test_loop_engineering_meta_prompt.py
+++ b/pipeline/test_loop_engineering_meta_prompt.py
@@ -12,6 +12,10 @@ def test_loop_engineering_meta_prompt_locks_safety_fields():
         "git-proxy:8099/deploy",
         "auto_patch_proven",
         "rsi_canary_recovery_evidence",
+        "Agent harness",
+        "end-loop guardrails",
+        "latency, token use",
+        "YouTube `GrNbuWWJYiI`",
         "Model Workspace Protocol",
         "00-intake/",
         "40-ship/",
diff --git a/plans/meta-prompts/loop_engineering.md b/plans/meta-prompts/loop_engineering.md
index 3d52ae6..b58cc14 100644
--- a/plans/meta-prompts/loop_engineering.md
+++ b/plans/meta-prompts/loop_engineering.md
@@ -34,6 +34,22 @@ At minimum, plans must name the checks that prove:
 
 Prefer the boring loop that compounds over the clever prompt that works once. If the plan needs a new framework, first prove a markdown file, JSON receipt, and pytest assertion cannot hold the invariant.
 
+## Agent harness note
+
+A loop is not just repeated model calls. Every agent plan must name the harness around the model:
+
+- context and working memory;
+- durable semantic memory / RAG sources;
+- episodic memory / traces from prior runs;
+- tool allowlist and signed deploy boundaries;
+- end-loop guardrails that define when to stop;
+- tracing for retrievals, tool calls, latency, token use, and errors;
+- evals/receipts that decide whether prompt or config changes can feed back into the next run.
+
+LLMOps/hill-climbing only promotes a changed prompt, retrieval config, tool config, or model parameter when traces plus evals show the new loop is safer or better.
+
+Reference: Sean's AI Stories, "You Can Learn AI Agent Harness & Loop Engineering In 19 Min" (YouTube `GrNbuWWJYiI`).
+
 ## Model Workspace Protocol note
 
 For sequential workflows with human review between stages, prefer folder-structured orchestration before multi-agent framework code. The Model Workspace Protocol pattern treats numbered folders as stages, markdown files as role/context carriers, and local scripts as the boring mechanical layer.