| name | agentic-control-kernel |
| description | Unifying control-systems metalayer for LLM-as-controller agent development. Bootstrap any repository with typed plant/action/trace schemas, safety shield conventions, multi-rate loop hierarchy, EGRI-compatible evaluator interfaces, and the full consciousness stack (governance + knowledge graph + episodic memory). Use when: (1) setting up agentic control primitives in a new or existing project, (2) designing LLM-as-controller architectures with safety shields and typed directives, (3) wiring EGRI/autoany loops for controller or artifact improvement, (4) bootstrapping the consciousness stack (control-metalayer + knowledge-graph + conversation bridge), (5) integrating symphony-style orchestration patterns, (6) defining plant interfaces, state estimators, or world models for agent-controlled systems, (7) user says "control kernel", "agentic control", "safety shield", "plant interface", "control metalayer", "agent controller", "multi-rate loop", "LLM control law".
|
Agentic Control Kernel
A purely knowledge-based metalayer that unifies six subsystems into a single
installable skill for any project:
| Layer | Source / Crates | Role |
|---|
| Governance | control-metalayer-loop | Setpoints, sensors, gates, policy, profiles |
| Improvement | autoany_core + autoany-aios + autoany-lago | EGRI microkernel, Arcan execution, Lago ledger |
| Orchestration | symphony-orchestrator + symphony-arcan | Poll/dispatch/worker/reconcile via Arcan HTTP |
| Runtime | Life (arcan, lago, autonomic, praxis, spaces) | Agent sessions, event journal, homeostasis, networking |
| Protocol | aios-protocol | Canonical types — shared vocabulary across all crates |
| Episodic Memory | knowledge-graph-memory | Conversation logs -> Obsidian bridge |
| Consciousness | agent-consciousness | Three-substrate persistent context |
| QA/Actuation | gstack | Headless browser, workflow skills |
| Control Kernel | this skill | Plant interface, safety shields, typed schemas, multi-rate hierarchy |
Core Law
Do not grant an agent more mutation freedom than your evaluator can reliably judge.
In control terms: do not let the LLM's action space exceed what your runtime monitors,
safety filters, and evaluators can certify.
Quick Start
1. Bootstrap a project
python3 scripts/control_kernel_init.py <repo-path> [--profile governed] [--runtime arcan] [--ledger lago]
This installs into the target repo:
.control/policy.yaml — control-systems-aware setpointsschemas/ — state, action, trace, evaluator JSON schemasMETALAYER.md — control loop definition with plant/shield/estimator sections- Harness gates wired to
make smoke, make check, make control-audit
2. Define the plant interface
Edit .control/plant.yaml with typed state and action schemas for your system.
See references/plant-interface.md for the full API spec.
3. Wire safety shields
See references/safety-shields.md for CBF-QP patterns,
policy gates, and containment invariants.
4. Set up EGRI for controller improvement
Use the problem-spec template in assets/templates/problem-spec.control.yaml
to define an autoany loop over your controller artifacts.
See references/egri-for-controllers.md.
Architecture Overview
The LLM emits typed control directives θ_t — not raw actuations u_t.
Deterministic controller modules execute, safety shields filter, and the runtime
logs traces to an append-only ledger.
Plant → observe() → Runtime → update estimator → b_t
→ LLM Agent: request decision(b_t) → θ_t (typed directive)
→ Controller: propose(b_t, θ_t) → proposed u_t
→ Safety Shield: filter(u_t, b_t) → safe u_t + certificate
→ Plant: apply(safe u_t) → result
→ Evaluator/Ledger: append trace + score
See references/architecture.md for the full 5-layer diagram.
Multi-Rate Hierarchy
| Loop | Cadence | LLM here? | What runs |
|---|
| Servo | ms | No | PID, state feedback, deterministic |
| Constrained execution | 10-100ms | No (param updates only) | MPC/CBF-QP solvers |
| Supervisory planning | seconds | Yes | Goal setting, mode switching, tool selection |
| Auto-tuning (EGRI) | minutes-days | Yes | Controller synthesis, model learning |
See references/multi-rate-hierarchy.md.
LLM Roles in the Control Stack
| Role | Outputs | When to use |
|---|
| Supervisory controller | setpoints, mode switches, constraints | Default — long-horizon reasoning |
| Meta-controller | tool/module selection, identification triggers | Modular systems with multiple controllers |
| Controller synthesizer | code, configs, tests | Offline — gated by harness CI |
| EGRI loop compiler | problem-spec, evaluator design, promotion rules | Continuous improvement cycles |
See references/architecture.md for the full role table.
Reference Guide
- architecture.md — 5-layer stack, realized crate graph, control-flow diagram, component mapping
- integration-map.md — Adapter crate boundary map, configuration, direction rule
- plant-interface.md — Plant/Estimator/Controller/Shield/Evaluator API specs
- safety-shields.md — CBF-QP, policy gates, containment, failure modes
- multi-rate-hierarchy.md — Loop rates, LLM placement, heuristics
- world-models.md — Koopman, DeePC, digital twins, learned dynamics
- egri-for-controllers.md — Autoany applied to controller optimization
- orchestration-patterns.md — Symphony daemon patterns for multi-agent dispatch
- consciousness-stack.md — Memory/knowledge/episodic integration
- failure-modes.md — Mitigations catalog for LLM-in-the-loop control
- deep-research-report.md — Original research report and project plan: formal control theory, literature survey, prototype roadmap
Schemas
JSON Schemas in schemas/ enforce typed interfaces:
state.schema.json — Plant/belief stateaction.schema.json — Control directives (θ_t)trace.schema.json — Ledger entries (autoany-compatible)evaluator.schema.json — Score vectors, promotion decisionsegri-event.schema.json — EGRI trial events for Lago persistence via EventKind::Custom
Existing Skill Dependencies
This skill synthesizes and references (does not duplicate) these existing skills:
- control-metalayer-loop — Use for
.control/ bootstrapping and governance primitives
- autoany — EGRI loop execution via
autoany-aios (Arcan sessions) and autoany-lago (Lago ledger)
- symphony — Orchestration dispatch via
symphony-arcan (Arcan HTTP runtime)
- life —
arcan (agent sessions), lago (event journal), autonomic (homeostasis), spaces (networking)
- aios-protocol — Canonical types shared across all adapter crates
- agent-consciousness — Use for consciousness stack setup
- knowledge-graph-memory — Use for conversation bridge to Obsidian
- gstack — Use for QA actuation via headless browser
