The last backend piece: when the agent hits a decision only the operator can
make, it surfaces a structured question instead of guessing or stalling.
- ask_operator(prompt, why?, options?, context_entities?): nomos-local tool
that records a session_questions row, moves the task to awaiting_input, emits
question.raised, and ENDS the turn (the agent loop returns after it, so the
agent can't barrel past its own question). The prompt becomes the assistant's
visible message so the question also shows inline in the transcript.
- Two resume paths, both close the question + emit question.answered + return
the task to executing:
- Panel: POST /sessions/{id}/questions/{qid}/answer → resumes the agent in the
background with the answer injected (reusing the continuation machinery,
refactored continueSession → resumeSession). Returns 202; the reply lands via
message polling.
- Chat reply: the next chat message on a task with an open question IS the
answer — auto-closed in handleChat; the turn itself is the resume.
Verified end-to-end: forcing a decision paused the task at awaiting_input with
the structured question (prompt/why/options/entities); a panel answer resumed
the agent (it acknowledged host:strong and continued); a plain chat reply
auto-closed a second question. Cleanup + tests green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Gives a task a legible, live-advancing plan via three more nomos-local tools:
- set_goal(goal): records the task goal, status → planning, emits goal.set.
- propose_plan(steps[]): persists ordered steps (clean replace for v1 — a
revision starts a new list), status → executing, emits plan.proposed with
the persisted steps (id+seq) so the panel can address them.
- update_plan_step(seq, status, execution_id?): advances a step, stamping
started_at/finished_at, emits plan.step.started/finished. Anchors the event
to the step's target entity when it has one.
Belt-and-suspenders: when an execution linked to a step reaches a terminal
state, the api auto-closes the step (closePlanStepForExecution in
emitExecutionEvent) and emits plan.step.finished — so the board stays honest
even if the agent forgets to close a step it started.
Verified end-to-end: a goal-driven task fired goal.set → plan.proposed →
2× step.started/finished → task.status on the SSE stream; both steps persisted
done with start/finish timestamps; status progressed planning→executing→done.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds the compounding knowledge loop the task model is built around:
- complete_task(outcome, summary): a nomos-LOCAL, session-scoped tool (the
shared MCP server has no session id). Introduces the local-tool mechanism —
buildTools appends task tools, the agent loop routes them to handleTaskTool
instead of the MCP client. Sets the task's terminal status/outcome/summary,
mirrors it onto the task entity, and emits task.status.
- Knowledge → task linkage: after a successful upsert_knowledge in a task,
nomos links the note to the task entity (documents) and emits
knowledge.recorded, so the task's outcome view shows what it learned. The
note's about-link to the involved entity (written by upsert_knowledge) is the
retrieval path future tasks use.
- SOUL: every chat is a task loop — retrieve prior knowledge FIRST
(get_entity_knowledge on the target), plan, execute, record learnings, then
complete_task. Scales down for trivial read-only tasks.
- deleteSession now cleans up the task entity, its relationships, and its
task-scoped events (was orphaning them); the knowledge doc itself and its
about-links survive, as knowledge should outlive the task.
Verified end-to-end: a task recorded a note and completed; task.status +
knowledge.recorded hit the SSE stream; status=done/outcome=success persisted;
the note linked to both lxc:caddy (retrieval) and the task; a future
get_entity_knowledge(lxc:caddy) surfaces it; delete cleaned edges+events (0/0/0)
while the knowledge survived.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
