oikos/plans/done/2026-07-11-goal-oriented-chat-control-panel.md

# 2026-07-11 — Tasks: the chat page as goal-structured autonomous work

**Status:** Done — 2026-07-11. All 7 phases shipped and deployed (SHA
`e30813a`): task schema + entity anchor, `entity.touched`/`involves` live
tracking, the `complete_task`/knowledge-retrieval loop, structured
`propose_plan`/`update_plan_step` (with an append-not-replace fix for
mid-flight re-proposals), `ask_operator` pause/resume, the Tasks board, and
the live `TaskContextPanel`. Follow-up hardening tracked separately in
[concurrent-task-execution](../2026-07-11-concurrent-task-execution.md).
Supersedes the sidebar-only framing and the Chat portion of
[control-room-webui](../2026-07-08-control-room-webui.md), which described
chat as a free-form session list.

## The vision (operator, distilled)

> Structure the whole chat page as **tasks**. A task is a card — you see its
> status (running / completed / failed), its description. A task *is* a goal:
> "install the service", "give me the key status of X". The agent takes the
> goal, finds what it needs, **proposes a plan, the operator approves it once —
> that single approval is the only one needed — and the agent then executes the
> whole plan autonomously until the goal is achieved.** Every task has a
> completion status: successful or not, and its **learnings move to knowledge**,
> attached via **relationships** to the entities that were involved, so future
> tasks — successful or unsuccessful — make the agent better over time. Inside a
> task is the conversation (tools, thinking, questions if needed); the sidebar
> shows the live context: which entities the agent is exploring, the steps and
> their status, whether the task succeeded, and the knowledge it recorded — all
> populated in **real time** as the agent works.

Three pillars: **task as the unit**, **one approval → autonomous execution**,
**a knowledge loop that compounds**.

## The reframe

Today a "session" is a title + a flat message list
([migrations/015](../../migrations/015_agent_sessions.up.sql)); a "plan" is prose
the model types; there is no goal, status, outcome, or step object. We elevate
the session into a **task**:

- **A task = a session with a goal, a plan, a lifecycle status, and an
  outcome.** One task per chat. The chat page becomes a **task board** of
  status cards; opening a card shows the task: conversation in the center, live
  context in the sidebar.
- **The plan is approved once.** Machinery already exists — the assent window +
  event-driven auto-continuation shipped in
  [autonomous-plan-execution](2026-07-10-autonomous-plan-execution.md)
  ([continue.go](../../cmd/nomos/continue.go), [assent.go](../../cmd/nomos/assent.go))
  already turn a single approval into an autonomy grant the agent runs to
  completion. This plan gives that flow a **structured surface**: the one thing
  the operator approves is a named, stepped plan, and progress is visible.
- **On completion the task deposits knowledge**, linked by relationships to the
  entities involved *and to the task itself*, tagged success/failure — and
  **future tasks read it back at planning time.** The substrate exists:
  `upsert_knowledge` writes a knowledge doc-entity and a `documents`
  relationship ([server.go:1519](../../internal/mcp/server.go));
  `get_entity_knowledge` reads it ([server.go:170](../../internal/mcp/server.go)).
  We add task-linkage, an outcome flavor, and retrieval-at-planning.

## Builds on / aligns with

- [general-gated-execution](../2026-07-10-general-gated-execution.md) — the
  classifier + `run` primitive is the execution substrate; a plan step is just
  a described unit of work mapping to a `run`/`request_execution` call. **No
  fixed step enum.**
- [autonomous-plan-execution](2026-07-10-autonomous-plan-execution.md) —
  the single-approval autonomy window + auto-continuation loop.
- The knowledge tools + relationships graph (`upsert_knowledge`,
  `get_entity_knowledge`, `get_relations`, the temporal `relationships` table).

## Data model (migration `018_tasks.up.sql`)

Elevate the session into a task; add plan steps, questions, and the
task→knowledge linkage.

```sql
ALTER TABLE agent_sessions
  ADD COLUMN goal     TEXT NOT NULL DEFAULT '',
  ADD COLUMN status   TEXT NOT NULL DEFAULT 'active',
           -- active | planning | awaiting_approval | executing
           -- | awaiting_input | done | failed | abandoned
  ADD COLUMN outcome  TEXT,             -- success | failure | partial (NULL until done)
  ADD COLUMN summary  TEXT NOT NULL DEFAULT '',  -- one-line result, shown on the card
  ADD COLUMN entity_id UUID;            -- the task's OWN entity (type 'task'), for
                                        -- knowledge/relationship linkage (see below)

CREATE TABLE session_plan_steps (
  id           UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  session_id   UUID NOT NULL REFERENCES agent_sessions(id) ON DELETE CASCADE,
  seq          INT  NOT NULL,
  title        TEXT NOT NULL,
  detail       TEXT NOT NULL DEFAULT '',
  status       TEXT NOT NULL DEFAULT 'pending',
               -- pending | running | done | failed | skipped | blocked
  execution_id UUID,
  target_slug  TEXT,
  started_at   TIMESTAMPTZ,
  finished_at  TIMESTAMPTZ,
  created_at   TIMESTAMPTZ NOT NULL DEFAULT now()
);
CREATE INDEX idx_plan_steps_session ON session_plan_steps(session_id, seq);

CREATE TABLE session_questions (
  id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  session_id  UUID NOT NULL REFERENCES agent_sessions(id) ON DELETE CASCADE,
  prompt      TEXT NOT NULL,
  context     JSONB NOT NULL DEFAULT '{}',   -- { entities:[], options:[], why:"" }
  status      TEXT NOT NULL DEFAULT 'open',  -- open | answered | dismissed
  answer      TEXT,
  created_at  TIMESTAMPTZ NOT NULL DEFAULT now(),
  answered_at TIMESTAMPTZ
);
CREATE INDEX idx_questions_session_open
  ON session_questions(session_id) WHERE status = 'open';
```

**The task as an entity.** Each task gets a row in `entities` (type `task`,
slug `task:<short-id>`), stored in `agent_sessions.entity_id`. This is what
makes the knowledge loop use the *existing* graph machinery unchanged:
knowledge and involved-entity links hang off the task entity via
`relationships`, exactly like any other entity.

## Task lifecycle

```
created → planning → awaiting_approval → executing ⇄ awaiting_input → done
                                              │                         (outcome:
                                              └──────────────→ failed    success/
                                                                         failure/
                                                                         partial)
```

- **planning**: agent calls `get_entity_knowledge` on the target(s) first (prior
  learnings), then `set_goal` + `propose_plan`.
- **awaiting_approval**: the plan is the single approval gate. Operator approves
  → opens the assent window (existing) → **executing**.
- **executing**: steps flip pending→running→done via `update_plan_step` and the
  execution→step auto-close (below); the agent runs autonomously
  (auto-continuation) with no per-step re-approval.
- **awaiting_input**: only when the agent hits a real decision → `ask_operator`;
  answering resumes execution.
- **done/failed**: agent sets `outcome` + `summary` and deposits knowledge.

## Single approval → autonomous execution

Already the behaviour of the assent window + auto-continuation. This plan makes
the **approved object** a structured plan rather than an individual command:
approving the plan (one click / one "go ahead") authorizes every
read-only + config-mutation step in it. Destructive steps still require typed
confirmation *unless* named in the approved plan (the existing pre-authorized
destructive-step rule). Nothing new in the execution engine — we're giving it a
legible unit to approve and to show progress against.

## The knowledge loop (capture → link → retrieve)

**Capture (task end).** On `done`/`failed`, the agent (nudged by SOUL, enforced
by a server-side fallback) calls `upsert_knowledge` with the concrete learning —
what worked, what didn't, the gotcha — and we link the resulting knowledge
doc-entity to:
- the **entities involved** (already supported via `about`), and
- the **task entity** (`agent_sessions.entity_id`), via a new
  `outcome_of` / `produced_by` relationship, tagged
  `{"outcome":"success|failure"}`.

**Link.** Involved entities are captured cheaply: every `entity.touched` (below)
records a `relationships` edge `task —involved→ entity`. So a task's entity
neighborhood *is* its involved-entity set — queryable with the existing
`get_relations`.

**Retrieve (task start).** At **planning**, before proposing, the agent pulls
prior knowledge for the target entities (`get_entity_knowledge`) — which now
surfaces both successful and failed prior tasks (the outcome tag lets it weight
"last time `apt install docker.io` failed on Debian, used get.docker.com
instead"). This is the compounding: each task's outcome becomes the next task's
prior. SOUL makes this the first planning move.

## UI

### Task board (replaces the raw session rail / empty chat state)

[Sessions.svelte](../../web/src/pages/Sessions.svelte) /
[SessionRail.svelte](../../web/src/lib/components/SessionRail.svelte) become a
**board of task cards**. Each card:
- goal as the title, one-line `summary`,
- a **status pill** (running ◐ / awaiting you / done ✓ / failed ✗) with the
  step progress (`4/6`),
- outcome color on completion, knowledge-count badge (♦ 2 learned),
- click → open the task.

Grouped/filterable by status (Running, Needs input, Done, Failed). "New task"
replaces "new chat" — the empty state asks for a goal.

### Task detail = conversation + live context sidebar

Center column: the existing chat transcript (tools, thinking, questions inline)
— unchanged rendering ([Chat.svelte](../../web/src/pages/Chat.svelte)).

Right sidebar becomes `TaskContextPanel.svelte`, populated **in real time**, top
to bottom:
1. **GoalHeader** — goal + status pill + outcome (once done); editable goal.
2. **PlanProgress** — ordered steps, live status icons, `4/6` bar, click a step
   → scroll chat to its tool call / open its execution output.
3. **OperatorQuestion** — pinned structured card when a question is open: prompt,
   `why`, context-entity chips (→ EntitySheet), option buttons or free-text.
   Answering POSTs the answer and resumes the agent. Same card also renders
   inline in the transcript at the point it was raised. (The operator's
   "structured component with relevant context.")
4. **LiveEntityPanel** — the [SessionGraph](../../web/src/lib/components/SessionGraph.svelte)
   upgraded from passive to live: `entity.touched` → the node **pulses** +
   "now touching `lxc:foo`"; `health.changed` → recolor + transient
   `healthy→degraded` diff badge.
5. **Outcome & Knowledge** — on completion: success/failure banner, the
   `summary`, and the knowledge notes recorded (links to the knowledge
   entities), i.e. the [SessionDigest](../../web/src/lib/components/SessionDigest.svelte)
   evolved into a task-outcome card.

## Real-time event contract (global `/events/stream`)

The panel is driven by the **always-on** [events stream](../../web/src/lib/stores/events.ts),
not the per-turn chat SSE — so it stays live during server-side
auto-continuation (when no chat turn is open) and survives a tab reload. New
`type`s, each carrying `correlation_id = session_id`:

| type | data |
| ---- | ---- |
| `task.status` | `{ status, outcome?, summary? }` |
| `goal.set` | `{ goal }` |
| `plan.proposed` | `{ steps:[{seq,title,detail,target_slug}] }` |
| `plan.step.started` / `plan.step.finished` | `{ step_id, seq, status, execution_id? }` |
| `question.raised` / `question.answered` | `{ question_id, prompt?, context?, answer? }` |
| `entity.touched` | `{ slug, tool }` |
| `knowledge.recorded` | `{ title, about, outcome }` |

`entity.touched` is emitted from the `withActivityLogging` wrapper
([server.go:832](../../internal/mcp/server.go)) — it wraps every tool call, so
touched-entity tracking needs **zero agent changes**; it also writes the
`task —involved→ entity` relationship. `health.changed` already exists.

## Agent surface (new MCP tools + SOUL)

Thin declarations that write the tables/relationships and publish the event
in-process (event and row commit together):
- `set_goal(goal)`
- `propose_plan(steps:[{title,detail?,target_slug?}])`
- `update_plan_step(seq,status,execution_id?)` — plus the execution's terminal
  status **auto-closes** its linked step where
  [phase3.go](../../internal/httpapi/phase3.go) finalizes executions (belt and
  suspenders).
- `ask_operator(prompt,options?,context_entities?,why?)` — creates the question,
  status→`awaiting_input`, ends the turn; answer resumes via the existing
  assent/continuation path.
- `complete_task(outcome,summary)` — sets outcome/summary, status→done/failed;
  server enforces "a completed task must have deposited ≥1 knowledge note"
  (fallback: auto-summarize into one if the model forgot).

SOUL: "Every task has a goal. **First**, read prior knowledge for the target
entities (`get_entity_knowledge`) — learn from past tasks, successful or not.
Then `set_goal` + `propose_plan`. Execute autonomously after approval, marking
steps. Ask via `ask_operator` only for real decisions. When the goal is
verified, `complete_task` with the outcome and record what you learned."

## Implementation order

1. **Migration `018` + task-entity creation** (a `task` entity per session) +
   store methods. Sessions gain goal/status/outcome/summary; no behaviour change.
2. **`entity.touched` + `task —involved→ entity`** from `withActivityLogging` —
   cheapest live win; graph starts pulsing, involved-set is captured for free.
3. **Knowledge loop close**: `complete_task` + retrieval-at-planning in SOUL +
   outcome-tagged `outcome_of` link. Makes tasks compound.
4. **`set_goal`/`propose_plan`/`update_plan_step`** + execution→step auto-close.
5. **`ask_operator`** end-to-end (tool → question → pinned card inline+panel →
   answer resumes).
6. **UI: TaskContextPanel** (GoalHeader, PlanProgress, OperatorQuestion,
   LiveEntityPanel, Outcome/Knowledge) + `workspace.ts` store + REST hydration
   (`GET /sessions/{id}/{plan,questions}`).
7. **UI: Task board** — session rail/list → status-card board, "new task" flow.

Each step ships value: 2 = live entity awareness; 3 = compounding knowledge;
4-5 = plan progress + interactive questions; 6-7 = the full task surface.

## Verification

- Run "deploy TypeType as an LXC on strong" as a task. Expect: at planning the
  agent reads prior knowledge for `host:strong`; `propose_plan` renders steps;
  operator approves **once**; steps flip live; the touched node pulses; a
  mid-flow ambiguity surfaces as an `ask_operator` card answered in the panel;
  on success `complete_task` sets outcome=success, deposits a knowledge note
  linked to `lxc:typetype`, `host:strong`, and the task entity.
- Start a **second** task touching `host:strong`; confirm the first task's
  knowledge surfaces at planning (`get_entity_knowledge`) — the compounding loop.
- Reload the tab mid-execution → panel rehydrates from REST and keeps updating
  from the global stream (proves it isn't chat-SSE-bound).
- Board shows the task moving Running → Done with the right outcome color and
  knowledge badge. `SELECT status, outcome FROM agent_sessions` shows a real
  lifecycle, not all `active`.
- `get_relations` on the task entity returns its involved entities + produced
  knowledge.

## Open questions

- **One task per session vs sequential tasks in a chat** — v1: one task = one
  session (matches "each chat is a goal"). A new goal starts a new task.
  Multi-task threads are a later extension.
- **Failure knowledge weighting** — do we just tag `outcome:failure` and let the
  model judge, or add explicit "avoid this" surfacing at planning? Lean tag-only
  first; revisit if the agent repeats known-failed approaches.
- **`complete_task` enforcement** — hard-require a knowledge note (block
  completion) or soft (auto-generate a stub)? Lean soft, so a trivial "give me
  status" task isn't forced to invent a learning.
- **Board vs thread** for very short tasks ("key status of X") — a status query
  is a degenerate task (no plan, instant done). Render it as a lightweight card
  that never shows an approval, so the board isn't cluttered with heavyweight
  chrome for one-shot questions.