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<h1 id="adr-014-learning-profiles-con-recency-bias"><a class="header" href="#adr-014-learning-profiles-con-recency-bias">ADR-014: Learning Profiles con Recency Bias</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>: Agent Architecture Team
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<strong>Technical Story</strong>: Tracking per-task-type agent expertise with recency-weighted learning</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>Learning Profiles per-task-type con exponential recency bias</strong> para adaptar selección de agentes a capacidad actual.</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>Recency Bias</strong>: Últimos 7 días pesados 3× más alto (agentes mejoran rápidamente)</li>
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<li><strong>Per-Task-Type</strong>: Un perfil por tipo de tarea (architecture vs code gen vs review)</li>
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<li><strong>Avoid Stale Data</strong>: No usar promedio histórico (puede estar desactualizado)</li>
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<li><strong>Confidence Score</strong>: Requiere 20+ ejecuciones antes de confianza completa</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="-simple-average-all-time"><a class="header" href="#-simple-average-all-time">❌ Simple Average (All-Time)</a></h3>
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<ul>
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<li><strong>Pros</strong>: Simple</li>
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<li><strong>Cons</strong>: Histórico antiguo distorsiona, no adapta a mejoras actuales</li>
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</ul>
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<h3 id="-sliding-window-last-n-executions"><a class="header" href="#-sliding-window-last-n-executions">❌ Sliding Window (Last N Executions)</a></h3>
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<ul>
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<li><strong>Pros</strong>: More recent data</li>
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<li><strong>Cons</strong>: Artificial cutoff, perder contexto histórico</li>
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</ul>
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<h3 id="-exponential-recency-bias-chosen"><a class="header" href="#-exponential-recency-bias-chosen">✅ Exponential Recency Bias (CHOSEN)</a></h3>
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<ul>
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<li>Pesa natural según antigüedad, mejor refleja capacidad actual</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>✅ Adapts to agent capability improvements quickly</li>
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<li>✅ Exponential decay is mathematically sound</li>
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<li>✅ 20+ execution confidence threshold prevents overfitting</li>
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<li>✅ Per-task-type specialization</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>⚠️ Cold-start: new agents start with low confidence</li>
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<li>⚠️ Requires 20 executions to reach full confidence</li>
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<li>⚠️ Storage overhead (per agent × per task type)</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>Learning Profile 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-agents/src/learning_profile.rs
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pub struct TaskTypeLearning {
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pub agent_id: String,
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pub task_type: String,
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pub executions_total: u32,
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pub executions_successful: u32,
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pub avg_quality_score: f32,
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pub avg_latency_ms: f32,
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pub last_updated: DateTime<Utc>,
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pub records: Vec<ExecutionRecord>, // Last 100 executions
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}
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impl TaskTypeLearning {
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/// Recency weight formula: 3.0 * e^(-days_ago / 7.0) for recent
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/// Then e^(-days_ago / 7.0) for older
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pub fn compute_recency_weight(days_ago: f64) -> f64 {
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if days_ago <= 7.0 {
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3.0 * (-days_ago / 7.0).exp() // 3× weight for last week
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} else {
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(-days_ago / 7.0).exp() // Exponential decay after
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}
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}
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/// Weighted expertise score (0.0 - 1.0)
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pub fn expertise_score(&self) -> f32 {
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if self.executions_total == 0 {
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return 0.0;
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}
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let now = Utc::now();
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let weighted_sum: f64 = self.records
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.iter()
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.map(|r| {
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let days_ago = (now - r.timestamp).num_days() as f64;
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let weight = Self::compute_recency_weight(days_ago);
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(r.quality_score as f64) * weight
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})
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.sum();
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let weight_sum: f64 = self.records
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.iter()
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.map(|r| {
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let days_ago = (now - r.timestamp).num_days() as f64;
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Self::compute_recency_weight(days_ago)
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})
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.sum();
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(weighted_sum / weight_sum) as f32
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}
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/// Confidence score: min(1.0, executions / 20)
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pub fn confidence(&self) -> f32 {
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std::cmp::min(1.0, (self.executions_total as f32) / 20.0)
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}
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/// Final score combines expertise × confidence
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pub fn score(&self) -> f32 {
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self.expertise_score() * self.confidence()
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}
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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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agent_id: &str,
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task_type: &str,
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success: bool,
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quality: f32,
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) -> Result<()> {
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let record = ExecutionRecord {
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agent_id: agent_id.to_string(),
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task_type: task_type.to_string(),
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success,
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quality_score: quality,
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timestamp: Utc::now(),
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};
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// Store in KG
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db.create("executions").content(&record).await?;
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// Update learning profile
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let profile = db.query(
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"SELECT * FROM task_type_learning \
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WHERE agent_id = $1 AND task_type = $2"
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)
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.bind((agent_id, task_type))
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.await?;
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// Update counters (incremental)
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// If new profile, create with initial values
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Ok(())
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}
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<span class="boring">}</span></code></pre></pre>
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<p><strong>Agent Selection Using Profiles</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 select_agent_for_task(
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db: &Surreal<Ws>,
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task_type: &str,
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) -> Result<AgentId> {
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let profiles = db.query(
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"SELECT agent_id, expertise_score(), confidence(), score() \
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FROM task_type_learning \
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WHERE task_type = $1 \
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ORDER BY score() DESC \
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LIMIT 1"
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)
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.bind(task_type)
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.await?;
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let best_agent = profiles
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.take::<TaskTypeLearning>(0)?
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.ok_or(Error::NoAgentsAvailable)?;
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Ok(best_agent.agent_id)
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}
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<span class="boring">}</span></code></pre></pre>
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<p><strong>Scoring Formula</strong>:</p>
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<pre><code>expertise_score = Σ(quality_score_i × recency_weight_i) / Σ(recency_weight_i)
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recency_weight_i = {
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3.0 × e^(-days_ago / 7.0) if days_ago ≤ 7 days (3× recent bias)
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e^(-days_ago / 7.0) if days_ago > 7 days (exponential decay)
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}
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confidence = min(1.0, total_executions / 20)
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final_score = expertise_score × confidence
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</code></pre>
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<p><strong>Key Files</strong>:</p>
|
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<ul>
|
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<li><code>/crates/vapora-agents/src/learning_profile.rs</code> (profile computation)</li>
|
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<li><code>/crates/vapora-agents/src/scoring.rs</code> (score calculations)</li>
|
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<li><code>/crates/vapora-agents/src/selector.rs</code> (agent selection logic)</li>
|
||
</ul>
|
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<hr />
|
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<h2 id="verification"><a class="header" href="#verification">Verification</a></h2>
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<pre><code class="language-bash"># Test recency weight calculation
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cargo test -p vapora-agents test_recency_weight
|
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|
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# Test expertise score with mixed recent/old executions
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cargo test -p vapora-agents test_expertise_score
|
||
|
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# Test confidence with <20 and >20 executions
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cargo test -p vapora-agents test_confidence_score
|
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|
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# Integration: record executions and verify profile updates
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cargo test -p vapora-agents test_profile_recording
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|
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# Integration: select best agent using profiles
|
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cargo test -p vapora-agents test_agent_selection_by_profile
|
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|
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# Verify cold-start (new agent has low score)
|
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cargo test -p vapora-agents test_cold_start_bias
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||
</code></pre>
|
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<p><strong>Expected Output</strong>:</p>
|
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<ul>
|
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<li>Recent executions (< 7 days) weighted 3× higher</li>
|
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<li>Older executions gradually decay exponentially</li>
|
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<li>New agents (< 20 executions) have lower confidence</li>
|
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<li>Agents with 20+ executions reach full confidence</li>
|
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<li>Best agent selected based on recency-weighted score</li>
|
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<li>Profile updates recorded in KG</li>
|
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</ul>
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<hr />
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<h2 id="consequences"><a class="header" href="#consequences">Consequences</a></h2>
|
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<h3 id="agent-dynamics"><a class="header" href="#agent-dynamics">Agent Dynamics</a></h3>
|
||
<ul>
|
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<li>Agents that improve rapidly rise in selection order</li>
|
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<li>Poor-performing agents decline even with historical success</li>
|
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<li>Learning profiles encourage agent improvement (recent success rewarded)</li>
|
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</ul>
|
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<h3 id="data-management"><a class="header" href="#data-management">Data Management</a></h3>
|
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<ul>
|
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<li>One profile per agent × per task type</li>
|
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<li>Last 100 executions per profile retained (rest in archive)</li>
|
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<li>Storage: ~50KB per profile</li>
|
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</ul>
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<h3 id="monitoring"><a class="header" href="#monitoring">Monitoring</a></h3>
|
||
<ul>
|
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<li>Track which agents are trending up/down</li>
|
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<li>Identify agents with cold-start problem</li>
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<li>Alert if all agents for task type below threshold</li>
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</ul>
|
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<h3 id="user-experience"><a class="header" href="#user-experience">User Experience</a></h3>
|
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<ul>
|
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<li>Best agents selected automatically</li>
|
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<li>Selection adapts to agent improvements</li>
|
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<li>Users see faster task completion over time</li>
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</ul>
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<hr />
|
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<h2 id="references"><a class="header" href="#references">References</a></h2>
|
||
<ul>
|
||
<li><code>/crates/vapora-agents/src/learning_profile.rs</code> (profile implementation)</li>
|
||
<li><code>/crates/vapora-agents/src/scoring.rs</code> (scoring logic)</li>
|
||
<li>ADR-013 (Knowledge Graph Temporal)</li>
|
||
<li>ADR-017 (Confidence Weighting)</li>
|
||
</ul>
|
||
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||
<p><strong>Related ADRs</strong>: ADR-013 (Knowledge Graph), ADR-017 (Confidence), ADR-018 (Load Balancing)</p>
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