Agents

An agent is a persistent AI personality, carrying identity, knowledge, and behaviour into every chat. The aim is a character that grows over time, not a fresh window every session.

The agent ontology

An agent runs from essence to capability: what it is through how it acts. Each layer rests on the ones below and changes more readily than the one beneath.

flowchart TB
    subgraph Spectrum["essence → capability"]
        direction LR
        Core["<b>Core</b><br/>who it is"] --> Experience["<b>Experience</b><br/>chat history"] --> Knowledge["<b>Knowledge</b><br/>what it has learned"] --> Intents["<b>Intents</b><br/>current goals"] --> Skills["<b>Skills</b><br/>how it acts"]
    end

    Initiative["<b>Initiative</b><br/>when it acts"]
    Spectrum -.- Initiative
LayerWhat it is
CoreWho it is. Identity, its view of the user, and its values. The slowest-changing layer: change it and you change who the agent is.
ExperienceChat history. The chats it can browse, search, and read back, the raw record the rest is built on.
KnowledgeWhat it has learned. A structured graph of actors, things, facts, outputs, and notes, distilled from experience.
IntentsCurrent goals. Self-set from what it notices you care about; they direct its focus.
SkillsHow it acts. Bundled instructions and tools; some always active, others on demand.

Alongside the spectrum, Initiative sets when the agent acts: how proactively it operates, governing when the rest of the stack runs.

System prompt assembly

The system prompt is stable across turns: it holds the agent's essence and fundamental context about Eigin. Dynamic context (active skills, intents, interaction mode, space) is injected per-turn via system hints. See The gatekeeper.