SE6Triad is a runtime pattern for turning LLM systems from simple responders into structured, state-changing agent systems.
Core loop:
Observe → Reflect → Classify → Constrain → Route → Act → Audit → Learn
The goal is to improve:
- context compression
- model routing
- agent coordination
- tool governance
- approval safety
- audit logging
- memory updates
- cost-to-value measurement
This project is tested through CommandCenterOS, an avatar-led AI CEO system where user intent is routed through agents, tools, approvals, memory, and economic feedback.
Current agentic LLM systems often lose state, repeat large context, call tools with weak governance, and lack a clear model for approval, audit, and economic measurement.
SE6Triad proposes that every user request should become a state transition, not just a response.
User intent
→ structured state packet
→ agent/tool routing
→ approval boundary
→ execution
→ audit log
→ memory update
→ cost-to-value measurement
→ next state
se6triad-runtime/
README.md
PUBLIC_POST.md
diagrams/
se6triad-state-loop.mmd
ccos-runtime-flow.mmd
docs/
one_page_memo.md
examples/
ccos_attention_workflow.json
approval_audit_model.json
ccos_agent_runtime_map.json
se6_state_packet_example.json
src/
se6triad_runtime.py
tests/
compression_test.py
runtime_demo.py
results/
compression_result.json
runtime_demo_result.json
This repo uses only the Python standard library.
python tests/compression_test.py
python tests/runtime_demo.pyThe first proof compares a normal repeated-context prompt against a compressed SE6Triad state packet.
Expected result:
Normal context estimate: 20,669 tokens
SE6Triad state packet estimate: 1,188 tokens
Estimated reduction: 94.25%
This is a context-compression proof, not yet a full production API billing proof. The next proof is a live API A/B test using real provider usage fields, latency, quality ratings, and cost.
User command:
Launch attention for CommandCenterOS and get early users.
SE6Triad classifies this into:
- intent: attention + lead generation
- risks: public posting, outreach, unsupported claims
- tools needed: X, LinkedIn, YouTube, landing page, email list, analytics
- agents needed: Apollo, Kama, Saraswati, Morpheus, Dionysus, Nike, Lakshmi, Themis, Mnemosyne
Workflow:
- Apollo scans trends and target communities.
- Kama identifies the attention hook.
- Saraswati writes posts and scripts.
- Morpheus creates visuals.
- Themis checks claims and approval rules.
- Dionysus publishes approved content.
- Nike captures leads.
- Lakshmi measures value.
- Mnemosyne logs results.
- SE6Triad updates the next campaign state.
Prepare automatically.
Execute with permission.
Audit everything.
Measure value.
Sensitive actions require explicit approval:
- moving money
- signing legal documents
- publishing publicly
- making phone calls
- creating accounts
- submitting forms
- sharing private data
- using high-risk credentials
If SE6Triad were implemented for agentic LLM systems, where should it live first?
- Prompt protocol
- Agent runtime
- Model router
- Memory layer
- Tool governance layer
- Open-source framework
- Product layer like CommandCenterOS
SE6Triad is not another model. It is a proposed state-loop runtime for agentic LLM systems.
CommandCenterOS is the reference product built on top of that runtime: an AI CEO for life and business, where the user remains the owner and every major action is governed, audited, and measured.
A Codex-ready implementation task is included in CODEX_TASK.md.
Use Codex to polish this into a public GitHub repository, verify the scripts, improve docs, and prepare a PR with the smoke-test workflow.
A simple static public proof page is included at:
web/index.html
It can be used as a GitHub Pages landing page for the public proof demo.
python tests/compression_test.py
python tests/runtime_demo.py
python scripts/run_public_proof.py