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- Agent — the primary entity. Has a
name/slug, an LLM (provider/model),
an instruction (its task definition), an optional system_prompt
(personality), an allowed toolset, and mounted skills. execution_mode is
auto / manual / paused; status is active / archived.
- Instruction & revisions — instruction edits land on a draft revision.
They are NOT live until you
deploy_agent, which activates the draft. get_agent
shows instruction (active) plus draft_instruction / has_undeployed_draft.
- Building blocks (reusable, org-scoped) the agent composes:
- Code tool (
code_tool) — a custom Python tool; params/description come
from the code’s manifest.
- Prompt — a reusable prompt template with
{{variables}}. Optionally
carries an extraction schema (fields, see prompt-fields) — when set,
running it pulls structured fields out of input instead of generating
free text.
- Skill — a bundle of {instructions + a tool subset + reference material}
an agent mounts by slug. Live immediately on create/update.
- Integration — a connection to an external SaaS / MCP server (read-only here;
connect new ones in the web UI — many need an interactive OAuth flow).
- Chat — the unit of work. You
start_chat with a message; the agent runs a
turn. Success is recorded as outcomes (not a terminal status).
- Outcome — an append-only record the agent emits when it completes a goal
(status success / failed / partial, with a summary).
- HITL — human-in-the-loop. A chat can pause awaiting approval or input; you
answer with
respond_to_hitl to resume it.