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<main>
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<h1 id="adr-019-temporal-execution-history-con-daily-windowing"><a class="header" href="#adr-019-temporal-execution-history-con-daily-windowing">ADR-019: Temporal Execution History con Daily Windowing</a></h1>
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<p><strong>Status</strong>: Accepted | Implemented
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<strong>Date</strong>: 2024-11-01
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<strong>Deciders</strong>: Knowledge Graph Team
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<strong>Technical Story</strong>: Tracking agent execution history with daily aggregation for learning curves</p>
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<hr />
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<h2 id="decision"><a class="header" href="#decision">Decision</a></h2>
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<p>Implementar <strong>temporal execution history</strong> con daily windowed aggregations para computar learning curves.</p>
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<hr />
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<h2 id="rationale"><a class="header" href="#rationale">Rationale</a></h2>
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<ol>
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<li><strong>Learning Curves</strong>: Daily aggregations permiten ver trends (improving/stable/declining)</li>
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<li><strong>Causal Reasoning</strong>: Histórico permite rastrear problemas a raíz</li>
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<li><strong>Temporal Analysis</strong>: Comparer performance across days/weeks</li>
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<li><strong>Efficient Queries</strong>: Daily windows permiten group-by queries eficientes</li>
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</ol>
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<hr />
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<h2 id="alternatives-considered"><a class="header" href="#alternatives-considered">Alternatives Considered</a></h2>
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<h3 id="-per-execution-only-no-aggregation"><a class="header" href="#-per-execution-only-no-aggregation">❌ Per-Execution Only (No Aggregation)</a></h3>
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<ul>
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<li><strong>Pros</strong>: Maximum detail</li>
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<li><strong>Cons</strong>: Queries slow, hard to identify trends</li>
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</ul>
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<h3 id="-monthly-aggregation-only"><a class="header" href="#-monthly-aggregation-only">❌ Monthly Aggregation Only</a></h3>
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<ul>
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<li><strong>Pros</strong>: Compact</li>
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<li><strong>Cons</strong>: Misses weekly trends, loses detail</li>
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</ul>
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<h3 id="-daily-windows-chosen"><a class="header" href="#-daily-windows-chosen">✅ Daily Windows (CHOSEN)</a></h3>
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<ul>
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<li>Good balance: detail + trend visibility</li>
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</ul>
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<hr />
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<h2 id="trade-offs"><a class="header" href="#trade-offs">Trade-offs</a></h2>
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<p><strong>Pros</strong>:</p>
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<ul>
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<li>✅ Trends visible at daily granularity</li>
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<li>✅ Learning curves computable</li>
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<li>✅ Efficient aggregation queries</li>
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<li>✅ Retention policy compatible</li>
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</ul>
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<p><strong>Cons</strong>:</p>
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<ul>
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<li>⚠️ Storage overhead (daily windows)</li>
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<li>⚠️ Intra-day trends hidden (needs hourly for detail)</li>
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<li>⚠️ Rollup complexity</li>
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</ul>
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<hr />
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<h2 id="implementation"><a class="header" href="#implementation">Implementation</a></h2>
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<p><strong>Execution Record Model</strong>:</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
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</span><span class="boring">fn main() {
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</span>// crates/vapora-knowledge-graph/src/models.rs
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pub struct ExecutionRecord {
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pub id: String,
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pub agent_id: String,
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pub task_id: String,
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pub task_type: String,
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pub success: bool,
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pub quality_score: f32,
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pub latency_ms: u32,
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pub cost_cents: u32,
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pub timestamp: DateTime<Utc>,
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pub daily_window: String, // YYYY-MM-DD
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}
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pub struct DailyAggregation {
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pub id: String,
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pub agent_id: String,
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pub task_type: String,
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pub day: String, // YYYY-MM-DD
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pub execution_count: u32,
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pub success_count: u32,
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pub success_rate: f32,
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pub avg_quality: f32,
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pub avg_latency_ms: f32,
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pub total_cost_cents: u32,
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pub trend: TrendDirection, // Improving, Stable, Declining
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}
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#[derive(Debug, Clone, Copy, PartialEq, Eq)]
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pub enum TrendDirection {
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Improving,
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Stable,
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Declining,
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}
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<span class="boring">}</span></code></pre></pre>
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<p><strong>Recording Execution</strong>:</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
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</span><span class="boring">fn main() {
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</span>pub async fn record_execution(
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db: &Surreal<Ws>,
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record: ExecutionRecord,
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) -> Result<String> {
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// Set daily_window automatically
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let mut record = record;
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record.daily_window = record.timestamp.format("%Y-%m-%d").to_string();
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// Insert execution record
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let id = db
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.create("executions")
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.content(&record)
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.await?
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.id
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.unwrap();
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// Trigger daily aggregation (async)
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tokio::spawn(aggregate_daily_window(
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db.clone(),
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record.agent_id.clone(),
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record.task_type.clone(),
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record.daily_window.clone(),
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));
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Ok(id)
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}
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<span class="boring">}</span></code></pre></pre>
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<p><strong>Daily Aggregation</strong>:</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
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</span><span class="boring">fn main() {
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</span>pub async fn aggregate_daily_window(
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db: Surreal<Ws>,
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agent_id: String,
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task_type: String,
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day: String,
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) -> Result<()> {
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// Query all executions for this day/agent/tasktype
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let executions = db
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.query(
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"SELECT * FROM executions \
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WHERE agent_id = $1 AND task_type = $2 AND daily_window = $3"
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)
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.bind((&agent_id, &task_type, &day))
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.await?
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.take::<Vec<ExecutionRecord>>(0)?
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.unwrap_or_default();
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if executions.is_empty() {
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return Ok(());
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}
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// Compute aggregates
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let execution_count = executions.len() as u32;
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let success_count = executions.iter().filter(|e| e.success).count() as u32;
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let success_rate = success_count as f32 / execution_count as f32;
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let avg_quality: f32 = executions.iter().map(|e| e.quality_score).sum::<f32>() / execution_count as f32;
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let avg_latency_ms: f32 = executions.iter().map(|e| e.latency_ms as f32).sum::<f32>() / execution_count as f32;
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|
let total_cost_cents: u32 = executions.iter().map(|e| e.cost_cents).sum();
|
||
|
|
|
||
|
|
// Compute trend (compare to yesterday)
|
||
|
|
let yesterday = (chrono::NaiveDate::parse_from_str(&day, "%Y-%m-%d")?
|
||
|
|
- chrono::Duration::days(1))
|
||
|
|
.format("%Y-%m-%d")
|
||
|
|
.to_string();
|
||
|
|
|
||
|
|
let yesterday_agg = db
|
||
|
|
.query(
|
||
|
|
"SELECT success_rate FROM daily_aggregations \
|
||
|
|
WHERE agent_id = $1 AND task_type = $2 AND day = $3"
|
||
|
|
)
|
||
|
|
.bind((&agent_id, &task_type, &yesterday))
|
||
|
|
.await?
|
||
|
|
.take::<Vec<DailyAggregation>>(0)?;
|
||
|
|
|
||
|
|
let trend = if let Some(prev) = yesterday_agg.first() {
|
||
|
|
let change = success_rate - prev.success_rate;
|
||
|
|
if change > 0.05 {
|
||
|
|
TrendDirection::Improving
|
||
|
|
} else if change < -0.05 {
|
||
|
|
TrendDirection::Declining
|
||
|
|
} else {
|
||
|
|
TrendDirection::Stable
|
||
|
|
}
|
||
|
|
} else {
|
||
|
|
TrendDirection::Stable
|
||
|
|
};
|
||
|
|
|
||
|
|
// Create or update aggregation record
|
||
|
|
let agg = DailyAggregation {
|
||
|
|
id: format!("{}-{}-{}", &agent_id, &task_type, &day),
|
||
|
|
agent_id,
|
||
|
|
task_type,
|
||
|
|
day,
|
||
|
|
execution_count,
|
||
|
|
success_count,
|
||
|
|
success_rate,
|
||
|
|
avg_quality,
|
||
|
|
avg_latency_ms,
|
||
|
|
total_cost_cents,
|
||
|
|
trend,
|
||
|
|
};
|
||
|
|
|
||
|
|
db.upsert(&agg.id).content(&agg).await?;
|
||
|
|
|
||
|
|
Ok(())
|
||
|
|
}
|
||
|
|
<span class="boring">}</span></code></pre></pre>
|
||
|
|
<p><strong>Learning Curve Query</strong>:</p>
|
||
|
|
<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
|
||
|
|
</span><span class="boring">fn main() {
|
||
|
|
</span>pub async fn get_learning_curve(
|
||
|
|
db: &Surreal<Ws>,
|
||
|
|
agent_id: &str,
|
||
|
|
task_type: &str,
|
||
|
|
days: u32,
|
||
|
|
) -> Result<Vec<DailyAggregation>> {
|
||
|
|
let since = (Utc::now() - chrono::Duration::days(days as i64))
|
||
|
|
.format("%Y-%m-%d")
|
||
|
|
.to_string();
|
||
|
|
|
||
|
|
let curve = db
|
||
|
|
.query(
|
||
|
|
"SELECT * FROM daily_aggregations \
|
||
|
|
WHERE agent_id = $1 AND task_type = $2 AND day >= $3 \
|
||
|
|
ORDER BY day ASC"
|
||
|
|
)
|
||
|
|
.bind((agent_id, task_type, since))
|
||
|
|
.await?
|
||
|
|
.take::<Vec<DailyAggregation>>(0)?
|
||
|
|
.unwrap_or_default();
|
||
|
|
|
||
|
|
Ok(curve)
|
||
|
|
}
|
||
|
|
<span class="boring">}</span></code></pre></pre>
|
||
|
|
<p><strong>Trend Analysis</strong>:</p>
|
||
|
|
<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
|
||
|
|
</span><span class="boring">fn main() {
|
||
|
|
</span>pub fn analyze_trend(curve: &[DailyAggregation]) -> TrendAnalysis {
|
||
|
|
if curve.len() < 2 {
|
||
|
|
return TrendAnalysis::InsufficientData;
|
||
|
|
}
|
||
|
|
|
||
|
|
let improving_days = curve.iter().filter(|d| d.trend == TrendDirection::Improving).count();
|
||
|
|
let declining_days = curve.iter().filter(|d| d.trend == TrendDirection::Declining).count();
|
||
|
|
|
||
|
|
if improving_days > declining_days {
|
||
|
|
TrendAnalysis::Improving
|
||
|
|
} else if declining_days > improving_days {
|
||
|
|
TrendAnalysis::Declining
|
||
|
|
} else {
|
||
|
|
TrendAnalysis::Stable
|
||
|
|
}
|
||
|
|
}
|
||
|
|
<span class="boring">}</span></code></pre></pre>
|
||
|
|
<p><strong>Key Files</strong>:</p>
|
||
|
|
<ul>
|
||
|
|
<li><code>/crates/vapora-knowledge-graph/src/models.rs</code> (models)</li>
|
||
|
|
<li><code>/crates/vapora-knowledge-graph/src/aggregation.rs</code> (daily aggregation)</li>
|
||
|
|
<li><code>/crates/vapora-knowledge-graph/src/learning.rs</code> (learning curves)</li>
|
||
|
|
</ul>
|
||
|
|
<hr />
|
||
|
|
<h2 id="verification"><a class="header" href="#verification">Verification</a></h2>
|
||
|
|
<pre><code class="language-bash"># Test execution recording with daily window
|
||
|
|
cargo test -p vapora-knowledge-graph test_execution_recording
|
||
|
|
|
||
|
|
# Test daily aggregation
|
||
|
|
cargo test -p vapora-knowledge-graph test_daily_aggregation
|
||
|
|
|
||
|
|
# Test learning curve computation (7 days)
|
||
|
|
cargo test -p vapora-knowledge-graph test_learning_curve_7day
|
||
|
|
|
||
|
|
# Test trend detection
|
||
|
|
cargo test -p vapora-knowledge-graph test_trend_detection
|
||
|
|
|
||
|
|
# Integration: full lifecycle
|
||
|
|
cargo test -p vapora-knowledge-graph test_temporal_history_lifecycle
|
||
|
|
</code></pre>
|
||
|
|
<p><strong>Expected Output</strong>:</p>
|
||
|
|
<ul>
|
||
|
|
<li>Executions recorded with daily_window set</li>
|
||
|
|
<li>Daily aggregations computed correctly</li>
|
||
|
|
<li>Learning curves show trends</li>
|
||
|
|
<li>Trends detected accurately (improving/stable/declining)</li>
|
||
|
|
<li>Queries efficient with daily windows</li>
|
||
|
|
</ul>
|
||
|
|
<hr />
|
||
|
|
<h2 id="consequences"><a class="header" href="#consequences">Consequences</a></h2>
|
||
|
|
<h3 id="data-retention"><a class="header" href="#data-retention">Data Retention</a></h3>
|
||
|
|
<ul>
|
||
|
|
<li>Daily aggregations permanent (minimal storage)</li>
|
||
|
|
<li>Individual execution records archived after 30 days</li>
|
||
|
|
<li>Trend analysis available indefinitely</li>
|
||
|
|
</ul>
|
||
|
|
<h3 id="trend-visibility"><a class="header" href="#trend-visibility">Trend Visibility</a></h3>
|
||
|
|
<ul>
|
||
|
|
<li>Daily trends visible immediately</li>
|
||
|
|
<li>Week-over-week comparisons possible</li>
|
||
|
|
<li>Month-over-month trends computable</li>
|
||
|
|
</ul>
|
||
|
|
<h3 id="performance"><a class="header" href="#performance">Performance</a></h3>
|
||
|
|
<ul>
|
||
|
|
<li>Aggregation queries use indexes (efficient)</li>
|
||
|
|
<li>Daily rollup automatic (background task)</li>
|
||
|
|
<li>No real-time overhead</li>
|
||
|
|
</ul>
|
||
|
|
<h3 id="monitoring"><a class="header" href="#monitoring">Monitoring</a></h3>
|
||
|
|
<ul>
|
||
|
|
<li>Trends inform agent selection decisions</li>
|
||
|
|
<li>Declining agents flagged for investigation</li>
|
||
|
|
<li>Improving agents promoted</li>
|
||
|
|
</ul>
|
||
|
|
<hr />
|
||
|
|
<h2 id="references"><a class="header" href="#references">References</a></h2>
|
||
|
|
<ul>
|
||
|
|
<li><code>/crates/vapora-knowledge-graph/src/aggregation.rs</code> (implementation)</li>
|
||
|
|
<li><code>/crates/vapora-knowledge-graph/src/learning.rs</code> (usage)</li>
|
||
|
|
<li>ADR-013 (Knowledge Graph)</li>
|
||
|
|
<li>ADR-014 (Learning Profiles)</li>
|
||
|
|
</ul>
|
||
|
|
<hr />
|
||
|
|
<p><strong>Related ADRs</strong>: ADR-013 (Knowledge Graph), ADR-014 (Learning Profiles), ADR-020 (Audit Trail)</p>
|
||
|
|
|
||
|
|
</main>
|
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|
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|
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