Knuckles-Team agent-utilities — 5-Pillar Architecture
Pillar 1: Graph Orchestration Engine (AU-ORCH.planning.orchestration-overview)
Purpose: Multi-agent coordination, HTN planning, specialist routing, workflow execution.
| Concept | Name | Key Module |
|---|---|---|
ORCH-1.0 |
Intelligence Graph Core | knowledge_graph/core/engine.py |
ORCH-1.1 |
HTN Planning Pipeline | graph/planner.py |
ORCH-1.2 |
Specialist Routing & Discovery | graph/routing.py |
ORCH-1.3 |
Execution Safety & State | graph/executor.py, graph/hsm.py |
ORCH-1.4 |
Capability Wiring Engine | graph/nodes.py |
AU-ORCH.planning.legal-automation-roadmap |
Agent Orchestrator | graph/agent_orchestrator.py |
AU-ORCH.planning.legal-automation-roadmap |
DSTDD Pipeline | sdd/orchestrator.py |
ORCH-1.8 |
Workflow Distillation | workflows/distillation_hook.py |
AU-ORCH.execution.rlm-synthesis-failed-falling |
Workflow-TeamConfig Unification | workflows/skill_compiler.py |
AU-ORCH.execution.autonomous-department-orchestration |
Autonomous Department Orchestration | models/company.py |
Architecture: Tasks flow through the HTN planner → routing selects specialist agents → executor manages state via HSM → results feed back to KG.
Pillar 2: Epistemic Knowledge Graph (AU-KG.compute.kg-x)
Purpose: Semantic storage, OWL reasoning, tiered memory, retrieval, domain knowledge.
| Concept | Name | Key Module |
|---|---|---|
KG-2.0 |
Active Knowledge Graph | knowledge_graph/core/engine.py |
KG-2.1 |
Tiered Memory & Context | knowledge_graph/memory/ |
KG-2.2 |
Ontology & Epistemics | knowledge_graph/ontology*.ttl |
KG-2.3 |
Graph Integrity & Retrieval | knowledge_graph/retrieval/ |
KG-2.4 |
Inductive Knowledge | knowledge_graph/core/ar_graph.py |
KG-2.5 |
Topological Analysis | knowledge_graph/core/topological_analysis_engine.py |
KG-2.6 |
Domain: Finance | domains/finance/ |
KG-2.7 |
Research Intelligence | knowledge_graph/research/ |
KG-2.12 |
Company Operations Domain | ontology_company.ttl, models/company.py |
KG-2.13 |
Company Intelligence Graph | models/company.py |
AU-KG.memory.ground-truth-preamble-declaring |
Skill-Graph ↔ KG Sync | skill-graph-builder |
Architecture: NetworkX in-memory graph + optional LadybugDB backend. OWL ontologies loaded via RDFLib/OWLReady2. Tiered memory: episodic → semantic → procedural with decay/consolidation.
Pillar 3: Agentic Harness Engineering (AU-AHE.optimization.telemetry-optimization)
Purpose: Self-improvement, testing, evolution, team composition, curriculum learning.
| Concept | Name | Key Module |
|---|---|---|
AHE-3.0 |
Agentic Harness Core | harness/ |
AHE-3.1 |
Continuous Evaluation Engine | harness/evaluation_engine.py |
AHE-3.2 |
Agentic Evolution Engine | harness/agentic_evolution_engine.py |
AHE-3.3 |
Team & Synergy Optimization | graph/team_composer.py |
AHE-3.4 |
Distributed Agentic Evolution | harness/distributed_state_manager.py |
AU-AHE.harness.self-evolution-narrative |
Heavy Thinking & Background Intelligence | agentic_evolution/forge.py |
Architecture: Evaluation engine scores agent outputs → evolution engine proposes improvements → team composer optimizes coalition membership → successful teams promoted to TeamConfigNode for reuse.
Pillar 4: Ecosystem & Peripherals (ECO-4.x)
Purpose: MCP servers, API clients, messaging, tools, external integrations.
| Concept | Name | Key Module |
|---|---|---|
ECO-4.0 |
Tool Interface & MCP Factory | mcp/server_factory.py |
ECO-4.1 |
A2A Network & Consensus | protocols/a2a_graph_skill.py |
AU-ECO.toolkit.journey-map-milestones |
Native Messaging Backend | ecosystem/bridge.py |
AU-ECO.toolkit.self-documenting-plugin-bundle |
Self-Documenting Skill-Graph | skill_graphs/agent-utilities/ |
AU-OS.deployment.infra-orchestration |
Company Infrastructure Orchestration | ontology_company_infra.ttl |
AU-OS.deployment.blueprint-library |
Infrastructure Blueprint Library | skill_graphs/infrastructure-blueprints/ |
Architecture: MCP servers expose tools → server_factory creates standardized endpoints → tool_filtering selects relevant tools for each agent.
Pillar 5: Agent OS Infrastructure (OS-5.x)
Purpose: Security, auth, scheduling, guardrails, telemetry, XDG paths.
| Concept | Name | Key Module |
|---|---|---|
OS-5.0 |
Agent OS Kernel & XDG Paths | core/paths.py, core/config.py |
OS-5.1 |
Security & Auth | security/guardrails.py, security/tool_guard.py |
OS-5.2 |
Resource Scheduling | core/cognitive_scheduler.py |
AU-OS.governance.reactive-multi-axis-budget |
Guardrails & Safety | security/guardrails.py |
AU-OS.governance.wasm-micro-agent-sandbox |
Telemetry & Observability | observability/token_tracker.py |
Architecture: XDG-compliant paths → config.json at ~/.config/agent-utilities/ →
cognitive scheduler manages LLM load → guardrails enforce safety contracts.