| name | agentdb-route |
| description | Ask the AgentDB bandit which RL algorithm / skill / pattern fits the current task best. Use at task start when there are multiple plausible approaches and you want the data-driven pick. |
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:
- Pattern ranking — which historical pattern matches this query best?
- Algorithm selection — which RL algo trains best on this task?
- Compression tier — full / PQ8 / PQ4 / binary?
- 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.
