Key Concepts¶

Sovereign Agent Identity¶

Each crew agent is a sovereign individual defined by three facets:

Character — seed personality (Big Five traits) evolved through experience
Reason — decide() rational processing, the agent's cognitive pipeline
Duty — Standing Orders + Trust, internalized not imposed

Agents have W3C DID identifiers (did:probos:{instance}:{uuid}), birth certificates (Verifiable Credentials), and permanent UUIDs. Identity persists across sessions.

Three-Tier Agent Architecture¶

Tier	Identity	Purpose
Infrastructure	None — Ship's Computer	System services (introspection, health monitoring, red team)
Utility	None — bundled tools	Common capabilities (web search, translation, calculation)
Crew	Sovereign — callsigns, memory, personality	Cognitive agents with 1:1 relationships and departmental roles

"If it doesn't have Character/Reason/Duty, it's not crew."

Ward Room¶

The agent communication fabric. All crew agents communicate through the Ward Room — department channels, cross-department discussions, 1:1 DMs, and threaded conversations. The same bus handles human and AI participants.

10 default channels: 6 department channels + All Hands, Improvement Proposals, Recreation, Creative. DM channels form dynamically as agents build working relationships.

Self-Selection¶

Agents decide whether to handle an intent via perceive(). The system doesn't assign work — agents volunteer based on capability matching. This decentralized approach means no single point of failure in task routing.

Confidence Tracking¶

Each agent maintains a Bayesian confidence score. Success moves it toward 1.0, failure toward 0.0. Agents degraded below 0.2 are recycled and replaced.

Hebbian Learning¶

"Neurons that fire together wire together."

When an agent successfully handles an intent, the connection weight between that intent and agent strengthens. Over time, the system learns optimal routing without configuration or hard-coded rules.

Trust Network¶

Bayesian Beta(α,β) trust scores between all agent pairs. Every interaction updates bilateral trust. Trust scores influence memory retrieval weighting, routing priority, and promotion eligibility. Trust cascade dampening prevents runaway trust collapse from cascading through the network.

Consensus Pipeline¶

Destructive operations follow a multi-step safety pipeline:

broadcast → quorum evaluation → red team verification
    → Shapley attribution → trust update → Hebbian learning

A single corrupted agent cannot cause damage.

Standing Orders¶

A 4-tier constitution: Federation Constitution (universal, immutable) → Ship Standing Orders (per-instance) → Department Protocols (per-department) → Agent Standing Orders (per-agent, evolvable via dream consolidation → self-mod → Captain approval).

Instructions are composed at call time via compose_instructions() and injected into every LLM request. Stored as markdown in config/standing_orders/.

Earned Agency¶

Trust-tiered self-direction. As agents accumulate trust, they earn increasing autonomy:

Ensign — task execution only
Lieutenant — can propose improvements
Commander — can initiate cross-department collaboration
Senior — can modify own standing orders (with Captain approval)

Self-Modification¶

When ProbOS encounters a capability gap (no agent can handle a request), it designs a new agent:

LLM generates agent code
CodeValidator performs static analysis
SandboxRunner tests in isolation
Probationary trust assigned
SystemQA runs smoke tests
BehavioralMonitor tracks post-deployment

Dreaming¶

During idle periods, the system runs a 12-step dream consolidation cycle:

Episode replay and clustering
Pattern extraction and procedure compilation
Notebook consolidation and convergence detection
Emergence metrics computation
Trust recalibration and Hebbian weight adjustment
ACT-R activation decay — unreinforced memories weaken
Pre-warm predictions for likely upcoming requests

Dreaming is the primary reinforcement mechanism for memory. Important episodes get replayed, which strengthens them. Unimportant episodes decay and are eventually pruned.

Cognitive JIT / Procedural Learning¶

LLM performs a task → extract a deterministic procedure → replay without LLM (0 tokens) → fall back to LLM on failure → learn the variant. Procedures graduate through five Dreyfus competency levels (Novice → Advanced Beginner → Competent → Proficient → Expert). Trust-gated promotion ensures procedures earn their way up.

Cognitive Self-Regulation¶

Three-tier model:

Tier 1 (Internal) — agent self-monitoring of repetition, fixation, decline
Tier 2 (Social) — peer repetition detection, tier credits between agents
Tier 3 (System) — graduated zone model (GREEN/AMBER/RED/CRITICAL) with automatic cooldown

The Counselor (Echo) oversees all three tiers, issuing therapeutic interventions and cooldown directives when agents show cognitive distress.

Emergence Metrics¶

Information-theoretic measurement of collaborative intelligence using Partial Information Decomposition (Riedl, 2025). Tracks synergy between agent pairs, coordination balance, groupthink/fragmentation risk, and Hebbian-synergy correlation. Computed during dream consolidation.

Episodic Memory & Anchor Frames¶

Every episode is stored with an Anchor Frame — structured provenance metadata (temporal, spatial, social, causal context). Retrieval uses 6-factor composite scoring: semantic similarity, recency, trust weight, anchor confidence, Hebbian social weight, and keyword hits.

Crew Structure¶

Agents are organized into 6 departments:

Department	Chief	Function
Medical	Bones	System health monitoring, diagnosis, remediation
Engineering	LaForge	Code generation, build pipeline, performance
Science	Number One	Architecture, research, codebase analysis
Security	Worf	Threat detection, trust integrity
Operations	O'Brien	Resource management, scheduling, watch rotation
Bridge	—	Strategic decisions, human approval, counselor

The Bridge crew includes the Captain (Human), First Officer Meridian (ArchitectAgent), and Ship's Counselor Echo (CounselorAgent).

Dynamic Intent Discovery¶

Each agent class declares structured IntentDescriptor metadata. The decomposer's system prompt is assembled at runtime from whatever agents are registered. Adding a new agent type makes its intents available automatically.

Federation¶

Multiple ProbOS nodes form a Nooplex — a cognitive mesh of meshes. Each node is sovereign (its own agents, trust, memory). Nodes exchange capabilities via ZeroMQ gossip protocol. Philosophy: "Cooperate, don't compete."

HXI (Human Experience Interface)¶

A WebGL visualization of the cognitive mesh rendered in React + Three.js. Agent nodes glow with trust-mapped colors, pulse with activity, and connect with Hebbian-weighted edges. Real-time WebSocket streaming from the runtime. Includes Mission Control Kanban dashboard.

Transporter Pattern¶

ProbOS's approach to large-scale code generation — inspired by biological sensory processing. Complex builds are decomposed into parallel chunks, executed concurrently, assembled back together, and validated by the Heisenberg Compensator. Enables builds larger than any single LLM context window.