docs(tac): add autonomous work agent guidance

Capture the NanoClaw autonomous work-agent blueprint in the TAC loop-engineering meta-prompt. Require plans to distinguish shared agent factories from personal work agents, isolate each agent, separate credentials through vault/proxy injection, define access policies, and include management/revocation surfaces. Tests lock the new markers.

pipeline/test_loop_engineering_meta_prompt.py

@@ -19,6 +19,10 @@ def test_loop_engineering_meta_prompt_locks_safety_fields():
         "Open-source model portability",
         "quality gate checklist",
         "YouTube `dJI2GRG1GEE`",
+        "Autonomous work agent",
+        "Team manages agents",
+        "vault/proxy credential injection",
+        "YouTube `hLUGXO5DSpo`",
         "Model Workspace Protocol",
         "00-intake/",
         "40-ship/",

plans/meta-prompts/loop_engineering.md

@@ -66,6 +66,25 @@ For outbound/productized work, require:
 
 Reference: Jordan Urbs, "GLM 5.2 Proves Open Source AI Can Match Fable 5 (AI Harness Engineering)" (YouTube `dJI2GRG1GEE`).
 
+## Autonomous work agent note
+
+For business adoption, distinguish two operating modes before designing the harness:
+
+1. **Team manages agents** — an agent factory or admin team creates, updates, and governs shared agents.
+2. **Agents assist people** — each human gets a personal work agent that learns their context, skills, preferences, and second-brain material.
+
+Every autonomous work-agent plan must include:
+
+- per-agent isolation boundary, preferably one container or equivalent sandbox per agent;
+- credential separation: no raw secrets inside the agent environment;
+- vault/proxy credential injection for only the requests the agent is allowed to make;
+- explicit access policies for tools, files, APIs, and outbound network;
+- skill/instruction iteration loop for tuning the agent's actual output;
+- management surface for upgrading, maintaining, and revoking agents after deployment;
+- second-brain/context source plan when personal-agent behavior depends on private knowledge.
+
+Reference: Latent Space, "The Blueprint for Autonomous Work Agents" with Gavriel Cohen / NanoClaw (YouTube `hLUGXO5DSpo`).
+
 ## 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.