Host Agent now persists step-level execution detail locally (via a real TaskMetadataStore/Timeline wired into TaskRunner) and reports a bounded in-progress snapshot piggybacked on lease renewal. Cloud persists that snapshot per active assignment and exposes it through the existing task list/detail query path; Cloud Console renders it as a live badge. Host Agent's local console gains authenticated, read-only task list and detail/timeline pages (same-origin, server-rendered) with inlined screenshots. Also fixes a pre-existing gap in the shared Timeline: the actual per-step LLM prompt is now recorded instead of the task goal, benefiting both Runtime and Host Agent consoles. When a host uses the cloud planner transport, each decide call's prompt and resulting tool decision are durably logged in a new planner_decision_log table (with bounded retention) and browsable from Cloud Console; direct-transport hosts explicitly surface a "not reported" state. Includes Alembic migrations 0008 (progress columns on scheduled_tasks) and 0009 (planner_decision_log), bounded Host-Agent-local retention, dual-backend repository parity, and Vitest + pytest coverage. Task 6.5 (manual end-to-end device verification) remains. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
6.5 KiB
Why
Nobody can see a task while it is running. Host Agent's local console only shows a coarse "current assignment" snapshot (task id, device id, goal, started-at) because the TaskRunner it drives is wired with metadata_store=None, timeline=None — every step transition happens in memory and is discarded the instant the assignment finishes. Cloud Control Plane only learns about an assignment at claim, heartbeat, and terminal-result time, so Cloud Console has nothing better to show. The local Runtime (api/rest.py + console/) is a separate process with its own independent TaskMetadataStore/Timeline, so it never sees a task that a Host Agent executed at all. An operator debugging a stuck or misbehaving task currently has no live signal anywhere in the system until the task finishes or times out.
What Changes
- Wire Host Agent's
AssignmentExecutor/create_execution_factories()with a realmetadata_storeandtimeline(or Host-Agent-local equivalents) so the in-processTaskRunneractually records step-by-step status instead of discarding it. - Expose that step-level detail through Host Agent's local console: extend
AgentStatusTracker//api/status(or add a focused endpoint) with current step index, step status, and a short in-progress step log; render it in the dashboard's "Current assignment" section instead of the static started-at-only view. - Add a progress-reporting path from Host Agent to Cloud Control Plane so the control plane learns step-level state near-real-time rather than only at claim/heartbeat/terminal-result. Extend the existing
cloud.internal_api.modelsPydantic schema (the single source of truth Host Agent already imports directly) rather than introducing a parallel schema. - Persist and expose the latest per-assignment progress on the Cloud Control Plane side, and surface it in Cloud Console so an operator watching a remote task sees live step progress, not just "dispatched" / "succeeded" / "failed".
- Give an operator a web console view of Host-Agent-executed task progress without making the
runtime/-owned packages import host or cloud concerns (enforced bytest_runtime_owned_packages_do_not_import_host_or_cloud_concerns). After evaluating the alternative of making the Runtimeconsole/SPA multi-backend (pointing its existing JS bundle at a Host Agent's console origin), this change instead extends Host Agent's own server-rendered local console with read-only task list/detail/timeline pages, reusing the same query shapeapi/console.pyalready exposes to the Runtime SPA. This avoids new cross-origin/session-cookie surface between the SPA and Host Agent, and keepsruntime/untouched. See design.md for the full trade-off analysis. - All three surfaces continue to use polling (matching current behavior); this change does not introduce SSE/WebSocket infrastructure unless design.md finds a compelling reason to.
- Fix the shared
Timeline/TaskRunner/AIPlannerrecording path so a persisted step's "prompt" is the actual prompt sent to the LLM for that step (not the task's overall goal) and the model's resulting decision is captured too — this pre-existing gap affects Runtime and Host Agent alike and undermines the step-level detail this change otherwise adds. - Persist a durable, per-step log of full LLM prompt/response content on the Cloud Control Plane, for centralized troubleshooting — reusing the already-existing
cloud-planner-proxydecide endpoint as the capture point (no new protocol/endpoint) rather than the coarse lease-renewal piggyback used for live index/status. This durable log is populated only for hosts using thecloudplanner transport; hosts on thedirecttransport still get only the coarse index/status via lease renewal. Cloud Console gains a view to browse a task's full LLM interaction history.
Capabilities
New Capabilities
host-agent-task-progress: Host Agent captures step-level execution progress for its in-flight assignment (via a wiredmetadata_store/timeline) and exposes it through its local console/API.cloud-task-progress-visibility: Cloud Control Plane receives, persists, and exposes near-real-time step-level progress for assignments it has dispatched to a Host Agent, and Cloud Console renders it.host-agent-console-task-pages: Host Agent's local server-rendered console gains read-only task list/detail/timeline pages (mirroringapi/console.py's task query shape) so an operator can inspect a Host-Agent-executed task's progress and history without needing the separate Runtimeconsole/SPA or violating theruntime/-package host/cloud isolation boundary.
Modified Capabilities
host-agent-protocol: add a requirement that the Host Agent reports in-progress step-level status updates to the control plane (in addition to the existing heartbeat/claim/renewal/result operations), and that the control plane accepts and stores them per active assignment.cloud-planner-proxy: the existing planner-decision endpoint additionally persists each resolved decision's prompt/response into a durable, bounded-retention per-step log, instead of discarding it after the response is returned.
Impact
apps/device-host-agent/host_agent/app.py,execution.py,assignment.py,status.py,web/app.py— wire a real metadata/timeline store into the in-processTaskRunner, extend status tracking and the local console UI/API.runtime/task.py,runtime/ai_planner.py,runtime/tool_calling_client.py,storage/timeline.py— fix the shared step-recording path so the real per-step prompt and the model's response are captured, not just the task goal and parsed tool call (a correctness fix in already-existing, shared code, not new behavior scope).packages/cloud-platform/cloud/internal_api/models.py,packages/cloud-platform/cloud/internal_api/api.py(decide_planner_call),repository.py/sql_repository.py, a new Alembic migration — new progress-reporting request/response models and a persistence + query path for latest per-assignment coarse progress, and a new durable per-stepplanner_decision_logtable (with its own retention job) populated from the existing planner-decision endpoint.cloud-console/(Vue3 SPA) — new UI to render live per-assignment progress, and a new view to browse a task's full LLM interaction history.apps/device-host-agent/host_agent/web/app.py— new read-only task list/detail/timeline pages backed by the Host-Agent-localTaskMetadataStore/Timeline; no changes anticipated toconsole/(Vue3 SPA).- No changes anticipated to
driver/,device/,core/device-control internals.