Refactor agent runtime: state machine, feedback processing, execution log

- Add state.rs with AgentState/Step/StepStatus/AgentPhase as single source of truth
- Extract prompts to markdown files loaded via include_str!
- Replace plan_steps table with execution_log + agent_state_snapshots
- Implement user feedback processing with docker-build-cache plan diff:
  load snapshot → LLM revise_plan → diff (title, description) → invalidate from first mismatch → resume
- run_agent_loop accepts optional initial_state for mid-execution resume
- Broadcast plan step status (done/running/pending) to frontend on step transitions
- Rewrite frontend types/components to match new API (ExecutionLogEntry, PlanStepInfo with status)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/api/workflows.rs

@@ -9,7 +9,7 @@ use axum::{
 use serde::Deserialize;
 use crate::AppState;
 use crate::agent::AgentEvent;
-use crate::db::{Workflow, PlanStep, Comment};
+use crate::db::{Workflow, ExecutionLogEntry, Comment};
 use super::{ApiResult, db_err};
 
 #[derive(serde::Serialize)]
@@ -77,9 +77,9 @@ async fn create_workflow(
 async fn list_steps(
     State(state): State<Arc<AppState>>,
     Path(workflow_id): Path<String>,
-) -> ApiResult<Vec<PlanStep>> {
-    sqlx::query_as::<_, PlanStep>(
-        "SELECT * FROM plan_steps WHERE workflow_id = ? ORDER BY step_order"
+) -> ApiResult<Vec<ExecutionLogEntry>> {
+    sqlx::query_as::<_, ExecutionLogEntry>(
+        "SELECT * FROM execution_log WHERE workflow_id = ? ORDER BY created_at"
     )
     .bind(&workflow_id)
     .fetch_all(&state.db.pool)