agentdb-route

# Route Ask the Thompson Sampling bandit which approach to use for the current task. ## When to use - Task start with multiple plausible skills / algorithms - Branching decision — A/B between approaches - Cold start on a new task type — let the bandit explore ## API ``` agentdb_learning_route( task: <description> candidates?: [<skill_id> | <algo>, ...] // omit to consider everything context?: { stack, project, ... } ) Returns: { picked, expectedReward, confidence, alternatives: [...] } ``` ## How it picks Thompson Sampling: each candidate has a Beta(α, β) posterior over reward. The bandit samples once from each, picks the highest sample. Exploration emerges naturally — uncertain candidates get tried until their posterior tightens. Four bandit decision points across AgentDB: 1. **Pattern ranking** — which historical pattern matches this query best? 2. **Algorithm selection** — which RL algo trains best on this task? 3. **Compression tier** — full / PQ8 / PQ4 / binary? 4. **Skill composition** — chain A→B→C or A→D→E? The router unifies them: it returns the picked candidate AND a `decisionTrace` showing which decision points fired. ## Use the result, then close the loop ``` const { picked } = await agentdb_learning_route(...) const result = await runWith(picked) agentdb_bandit_update(arm: picked, reward: result.reward) ``` The `agentdb-feedback` skill (this plugin) wraps the close-loop step. ## Don't - Don't second-guess the bandit on early calls — exploration is by design. - Don't refuse the bandit's pick without recording negative reward. If you ignored a suggestion and used a different one, log that — otherwise the bandit thinks its pick "worked" because no negative signal arrived.