Agentic Control Kernel

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  [--profile governed] [--runtime arcan] [--ledger lago]

This installs into the target repo:

• .control/policy.yaml — control-systems-aware setpoints
• schemas/ — state, action, trace, evaluator JSON schemas
• METALAYER.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](references/plant-interface.md) for the full API spec.

3. Wire safety shields

See [references/safety-shields.md](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](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](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](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](references/architecture.md) for the full role table.

Reference Guide

• [architecture.md](references/architecture.md) — 5-layer stack, realized crate graph, control-flow diagram, component mapping
• [integration-map.md](references/integration-map.md) — Adapter crate boundary map, configuration, direction rule
• [plant-interface.md](references/plant-interface.md) — Plant/Estimator/Controller/Shield/Evaluator API specs
• [safety-shields.md](references/safety-shields.md) — CBF-QP, policy gates, containment, failure modes
• [multi-rate-hierarchy.md](references/multi-rate-hierarchy.md) — Loop rates, LLM placement, heuristics
• [world-models.md](references/world-models.md) — Koopman, DeePC, digital twins, learned dynamics
• [egri-for-controllers.md](references/egri-for-controllers.md) — Autoany applied to controller optimization
• [orchestration-patterns.md](references/orchestration-patterns.md) — Symphony daemon patterns for multi-agent dispatch
• [consciousness-stack.md](references/consciousness-stack.md) — Memory/knowledge/episodic integration
• [failure-modes.md](references/failure-modes.md) — Mitigations catalog for LLM-in-the-loop control
• [deep-research-report.md](references/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 state
• action.schema.json — Control directives (θ_t)
• trace.schema.json — Ledger entries (autoany-compatible)
• evaluator.schema.json — Score vectors, promotion decisions
• egri-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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: broomva
• Source: broomva/skills
• License: MIT
• Homepage: https://skills.sh/broomva/skills

Install and usage instructions live in the source repository linked above.