{"id":1942,"date":"2026-07-18T06:06:19","date_gmt":"2026-07-18T13:06:19","guid":{"rendered":"http:\/\/macdaddy4sure.ai\/?p=1942"},"modified":"2026-07-18T06:06:19","modified_gmt":"2026-07-18T13:06:19","slug":"_augmentedintelligence-agent-orchestration","status":"publish","type":"post","link":"http:\/\/macdaddy4sure.ai\/index.php\/2026\/07\/18\/_augmentedintelligence-agent-orchestration\/","title":{"rendered":"_AugmentedIntelligence Agent Orchestration"},"content":{"rendered":"\n<p>This document covers the unified agent &#8230; command namespace added around the existing native and Lua-backed agents.<\/p>\n\n\n\n<p>Related docs:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>docs\/SimpleAgents.md<\/li>\n\n\n\n<li>docs\/KnowledgeRL.md<\/li>\n\n\n\n<li>docs\/GamingRL.md<\/li>\n\n\n\n<li>docs\/DrivingRL.md<\/li>\n\n\n\n<li>docs\/GameModBridges.md<\/li>\n\n\n\n<li>docs\/AugmentedIntelligenceSystems.md<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">What Was Added<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Central agent registry for native agents and Lua agents from the workbook.<\/li>\n\n\n\n<li>Fleet profiles for common operating modes.<\/li>\n\n\n\n<li>Runtime commands for start, stop, pause, resume, restart, status, config, and emergency stop.<\/li>\n\n\n\n<li>In-memory approval queue for gated actions.<\/li>\n\n\n\n<li>JSONL event log at agent_runtime\\events.jsonl.<\/li>\n\n\n\n<li>JSONL handoff log at agent_runtime\\handoffs.jsonl.<\/li>\n\n\n\n<li>Per-agent local memory notes in agent_runtime\\memory\\.<\/li>\n\n\n\n<li>Windows integration catalog at windows_integration\\commands\\agents-actions.json.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Core Commands<\/h2>\n\n\n\n<p><em>[text]<br><\/em>agent help<br>agent registry<br>agent profiles<br>agent profile show &lt;profile&gt;<br>agent profile apply &lt;profile&gt; [dry-run]<br>agent list<br>agent status [agent]<br>agent start &lt;agent&gt;<br>agent stop &lt;agent|all|type &lt;type&gt;&gt;<br>agent pause &lt;agent&gt;<br>agent resume &lt;agent&gt;<br>agent restart &lt;agent&gt;<br>agent config<br>agent config max_concurrent &lt;count&gt;<br>agent rl status<br>agent rl context<br>agent emergency stop<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Shared RL Context<\/h2>\n\n\n\n<p>Every agent runtime Lua state now receives the Knowledge RL and Gaming RL Lua<\/p>\n\n\n\n<p>globals. Blueprint-backed agents also append two review-safe steps to their plan<\/p>\n\n\n\n<p>and run sequence:<\/p>\n\n\n\n<p><em>[text]<br><\/em>Knowledge RL status<br>Gaming RL status<\/p>\n\n\n\n<p>Useful commands:<\/p>\n\n\n\n<p><em>[text]<br><\/em>agent rl status<br>simple agent rl status<br>knowledge rl stats<br>gaming rl status<\/p>\n\n\n\n<p>Per-agent opt-out is available with &lt;AGENT_ENV_PREFIX&gt;_RL_CONTEXT=0. For<\/p>\n\n\n\n<p>example, CFO_RL_CONTEXT=0 disables the automatic blueprint RL context steps<\/p>\n\n\n\n<p>for the CFO simple agent.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Simple Agent Commands<\/h2>\n\n\n\n<p>The simple agents section from C:\\Users\\Temp\\Desktop\\_AugmentedIntelligence Commands.xlsx<\/p>\n\n\n\n<p>is available through both the normal agent &#8230; namespace and a convenience<\/p>\n\n\n\n<p>simple agent &#8230; namespace.<\/p>\n\n\n\n<p>See docs\/SimpleAgents.md for the full simple-agent reference.<\/p>\n\n\n\n<p><em>[text]<br><\/em>simple agent registry<br>simple agent &lt;agent name&gt;<br>simple agent start &lt;agent name&gt;<br>simple agent status &lt;agent name&gt;<br>simple agent permissions &lt;agent name&gt;<br>agent profile show spreadsheet_simple_agents<br>agent profile apply spreadsheet_simple_agents dry-run<\/p>\n\n\n\n<p>Examples:<\/p>\n\n\n\n<p><em>[text]<br><\/em>simple agent cfo<br>simple agent human resources agent<br>simple agent mechanical engineer<br>simple agent investment strategist agent<br>simple agent stocks<\/p>\n\n\n\n<p>These agents use lua_scripts\\Simple-Agent-Profiles.lua and are review-only by<\/p>\n\n\n\n<p>default. Medical, mental-health, legal, finance, security, and cannabis-related<\/p>\n\n\n\n<p>profiles are framed as information\/research support, not professional advice.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Approval Commands<\/h2>\n\n\n\n<p><em>[text]<br><\/em>agent approvals list<br>agent approval request &lt;agent&gt; &lt;action&gt; [detail]<br>agent approval approve &lt;id&gt;<br>agent approval deny &lt;id&gt;<br>agent approval clear<\/p>\n\n\n\n<p>Approval records are currently local runtime records. They are useful for operator workflow and audit trails. Agents that perform high-impact work should consult this queue before taking live actions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Handoff, Task, And Memory Commands<\/h2>\n\n\n\n<p><em>[text]<br><\/em>agent handoff &lt;from&gt; &lt;to&gt; &lt;summary&gt;<br>agent task &lt;agent&gt; &lt;task&gt;<br>agent memory show &lt;agent&gt;<br>agent memory append &lt;agent&gt; &lt;note&gt;<br>agent memory clear &lt;agent&gt;<br>agent memory export &lt;agent&gt; &lt;path&gt;<br>agent events [count]<br>agent logs [count]<\/p>\n\n\n\n<p>agent task records a task in the target agent&#8217;s memory file and creates an approval request before action.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Built-In Agents<\/h2>\n\n\n\n<p>Native agents:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>human<\/li>\n\n\n\n<li>desktop<\/li>\n\n\n\n<li>cybernetic<\/li>\n\n\n\n<li>rsi<\/li>\n<\/ul>\n\n\n\n<p>Lua-backed agents:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>sysadmin<\/li>\n\n\n\n<li>electrical<\/li>\n\n\n\n<li>cybersecurity<\/li>\n\n\n\n<li>red_blue<\/li>\n\n\n\n<li>android<\/li>\n\n\n\n<li>federal<\/li>\n\n\n\n<li>memory_curator<\/li>\n\n\n\n<li>safety_auditor<\/li>\n\n\n\n<li>training<\/li>\n\n\n\n<li>build_test<\/li>\n\n\n\n<li>remote_llm<\/li>\n\n\n\n<li>minecraft_rl<\/li>\n\n\n\n<li>parenting_rl<\/li>\n<\/ul>\n\n\n\n<p>Spreadsheet simple agents:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>cfo, cto, cio<\/li>\n\n\n\n<li>doctor, counselor, psychologist, psychiatrist<\/li>\n\n\n\n<li>tyler, blogger, leonardo_da_vinci, steven_hawking<\/li>\n\n\n\n<li>mechanical_engineer, electrical_engineer, neuroscientist, software_engineer<\/li>\n\n\n\n<li>human_resources, debug_agent, tester, research_agent, security<\/li>\n\n\n\n<li>manager, assistant, it, marketing, lawyer, legal_secretary<\/li>\n\n\n\n<li>friend, comic, investment_strategist, technical_support<\/li>\n\n\n\n<li>guy_contractor, gal_support, brainstorming_person, event_summation<\/li>\n\n\n\n<li>researcher, marijuana_guru, stocks<\/li>\n<\/ul>\n\n\n\n<p>Use agent registry for descriptions, script paths, aliases, and permission labels.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Built-In Profiles<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>voice_first_assistant<\/li>\n\n\n\n<li>help_desk_operations<\/li>\n\n\n\n<li>multi_agent_orchestrator<\/li>\n\n\n\n<li>cybernetic_control_lab<\/li>\n\n\n\n<li>ai_red_team_lab<\/li>\n\n\n\n<li>neural_rl_lab<\/li>\n\n\n\n<li>minecraft_rl_lab<\/li>\n\n\n\n<li>safe_family_assistant<\/li>\n\n\n\n<li>engineering_lab<\/li>\n\n\n\n<li>spreadsheet_simple_agents<\/li>\n<\/ul>\n\n\n\n<p>Examples:<\/p>\n\n\n\n<p><em>[text]<br><\/em>agent profile apply minecraft_rl_lab dry-run<br>agent profile apply help_desk_operations<br>agent profile apply neural_rl_lab<br>agent fleet start<br>agent fleet stop<br>agent fleet debate compare minecraft training reward functions<\/p>\n\n\n\n<p>Profiles that may touch sensitive workflows create approval records. The scripts still start as local runtime scripts; live\/high-impact actions should be gated by approvals and existing safety systems.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Windows Integration<\/h2>\n\n\n\n<p>Search or run agent actions from PowerShell:<\/p>\n\n\n\n<p><em>[powershell]<br><\/em>Get-AugmentedIntegrationActions -Catalog agents -Query &#8220;minecraft&#8221;<br>.\\windows_integration\\integration-action.ps1 -Prompt -Catalog agents -UseResident<br>.\\windows_integration\\integration-action.ps1 -Id agent_profile_minecraft_rl_dry_run -UseResident<\/p>\n\n\n\n<p>The tray menu also includes Integration Actions &gt; Agents.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Neural Network Ideas For Controlling Agents<\/h2>\n\n\n\n<p>These are roadmap ideas for a neural controller above the individual agents. The goal is not one giant model that blindly controls everything. The useful design is a layered controller that proposes routing, priority, and policy choices while safety gates and approvals remain outside the model.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Agent router network<\/li>\n\n\n\n<li>Input: current command, user profile, active context, agent registry, safety labels, runtime status.<\/li>\n\n\n\n<li>Output: best target agent, backup agent, confidence, and whether approval is required.<\/li>\n\n\n\n<li>Training data: historical commands, accepted handoffs, failed routes, and operator corrections.<\/li>\n\n\n\n<li>Multi-agent supervisor policy<\/li>\n\n\n\n<li>Input: fleet state, open tasks, deadlines, agent health, recent errors, approval state.<\/li>\n\n\n\n<li>Output: start\/stop\/pause\/resume decisions and max concurrency recommendations.<\/li>\n\n\n\n<li>Start in shadow mode by comparing recommendations against human choices.<\/li>\n\n\n\n<li>Handoff quality scorer<\/li>\n\n\n\n<li>Input: handoff summary, source agent memory, destination agent scope, task outcome.<\/li>\n\n\n\n<li>Output: score for completeness, risk, ambiguity, and missing context.<\/li>\n\n\n\n<li>Use it to improve agent handoff packets before they are accepted.<\/li>\n\n\n\n<li>Approval risk model<\/li>\n\n\n\n<li>Input: proposed action, target agent, command text, safety labels, environment, recent incidents.<\/li>\n\n\n\n<li>Output: risk class, required reviewer type, and whether dry-run is mandatory.<\/li>\n\n\n\n<li>This should be conservative and auditable, not an automatic approval model.<\/li>\n\n\n\n<li>Agent memory retrieval ranker<\/li>\n\n\n\n<li>Input: current task and each agent&#8217;s memory notes.<\/li>\n\n\n\n<li>Output: top memories to inject into the agent&#8217;s context.<\/li>\n\n\n\n<li>Useful for keeping long-running agents focused without flooding prompts.<\/li>\n\n\n\n<li>Fleet reward model<\/li>\n\n\n\n<li>Input: task outcome, number of agent turns, delays, safety events, human edits, approval denials.<\/li>\n\n\n\n<li>Output: scalar reward for orchestration quality.<\/li>\n\n\n\n<li>Feeds offline RL for routing and scheduling, not direct unrestricted action.<\/li>\n\n\n\n<li>Mixture-of-experts control layer<\/li>\n\n\n\n<li>Each agent is an expert. A gating network chooses one or more experts per task.<\/li>\n\n\n\n<li>Works well when human, desktop, sysadmin, training, safety_auditor, and minecraft_rl have distinct scopes.<\/li>\n\n\n\n<li>Graph neural network over agent topology<\/li>\n\n\n\n<li>Nodes: agents, tasks, tools, memories, approvals, servers.<\/li>\n\n\n\n<li>Edges: can-handoff-to, depends-on, blocks, supervises, shares-memory-with.<\/li>\n\n\n\n<li>Output: routing plan and bottleneck predictions.<\/li>\n\n\n\n<li>Offline imitation model from operator traces<\/li>\n\n\n\n<li>Train on operator choices: which agent was started, what handoff was written, which approval was granted or denied.<\/li>\n\n\n\n<li>Deploy as a recommendation system first.<\/li>\n\n\n\n<li>Hierarchical RL controller<\/li>\n\n\n\n<li>High-level policy chooses profile, agent, and task phase.<\/li>\n\n\n\n<li>Low-level policies stay inside domain subsystems such as Minecraft DQN, desktop automation, or audio\/vision RSI.<\/li>\n\n\n\n<li>Use offline and shadow stages before any live control.<\/li>\n\n\n\n<li>Debate summarizer and judge<\/li>\n\n\n\n<li>Agents propose plans, a small judge model scores consistency, safety, and evidence.<\/li>\n\n\n\n<li>The final output is a recommended plan plus dissent notes, not an automatic action.<\/li>\n\n\n\n<li>Anomaly detector for agent behavior<\/li>\n\n\n\n<li>Input: command rate, repeated failures, approval denials, unusual handoff loops, memory churn.<\/li>\n\n\n\n<li>Output: pause recommendation or emergency-stop recommendation.<\/li>\n\n\n\n<li>Curriculum controller<\/li>\n\n\n\n<li>Chooses which profile or task class to train next based on weaknesses.<\/li>\n\n\n\n<li>Useful for Minecraft RL, desktop support drills, and systems administration tabletop scenarios.<\/li>\n\n\n\n<li>Capability embedding model<\/li>\n\n\n\n<li>Embed every agent description, permission label, command, and script description.<\/li>\n\n\n\n<li>Use nearest-neighbor search plus a reranker to select agents for new tasks.<\/li>\n\n\n\n<li>Safety shield policy<\/li>\n\n\n\n<li>Hard layer around all learned controllers.<\/li>\n\n\n\n<li>Enforces no-go actions, approval requirements, dry-run-first rules, rate limits, and emergency stop.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">Suggested Training Progression<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Log-only mode: collect agent task, agent handoff, agent approvals, agent profile apply, and agent events.<\/li>\n\n\n\n<li>Supervised router: train a classifier to predict the selected agent\/profile from operator commands.<\/li>\n\n\n\n<li>Shadow recommendations: show predicted route and approval risk, but do not act.<\/li>\n\n\n\n<li>Offline fleet reward model: score historical sessions for efficiency, safety, and result quality.<\/li>\n\n\n\n<li>Offline RL scheduler: learn start\/stop\/pause\/handoff suggestions from logged sessions.<\/li>\n\n\n\n<li>Gated dry-run: allow the controller to create plans, handoffs, and approval requests only.<\/li>\n\n\n\n<li>Gated live actions: require explicit approval for live starts, shell-capable agents, desktop control, server changes, and game control.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>This document covers the unified agent &#8230; command namespace added around the existing native and Lua-backed agents. Related docs: What Was Added Core Commands [text]agent helpagent registryagent profilesagent profile show &lt;profile&gt;agent profile apply &lt;profile&gt; [dry-run]agent listagent status [agent]agent start &lt;agent&gt;agent stop &lt;agent|all|type &lt;type&gt;&gt;agent pause &lt;agent&gt;agent resume &lt;agent&gt;agent restart &lt;agent&gt;agent configagent config max_concurrent &lt;count&gt;agent rl statusagent [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1942","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts\/1942","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/comments?post=1942"}],"version-history":[{"count":1,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts\/1942\/revisions"}],"predecessor-version":[{"id":1943,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts\/1942\/revisions\/1943"}],"wp:attachment":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/media?parent=1942"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/categories?post=1942"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/tags?post=1942"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}