| name | agentdb-feedback |
| description | Close the learning loop — record reward signal for an action AgentDB suggested. Use after using anything from agentdb_pattern_search / reflexion_recall / skill_search / learning_route. The bandit needs the signal to improve. |
Feedback
Close the loop on a memory or routing decision so AgentDB's bandit learns.
When to use
- Always after using a recall result, a routed action, or a skill suggestion. The most-undervalued part of the loop.
- After a task ends — record episode-level reward.
- When intentionally ignoring a suggestion — record negative reward so the bandit notices.
API
agentdb_record_feedback(
id: <pattern/skill/episode/decision id>
reward: -1..1
context?: { task, outcome, latency, ... }
)
agentdb_bandit_update(
arm: <bandit arm name>
reward: -1..1
)
Reward conventions
| Outcome | Reward |
|---|
| Used the suggestion, task succeeded | +1.0 |
| Used the suggestion, task partial success | +0.5 |
| Used the suggestion, didn't help | 0.0 |
| Used the suggestion, made things worse | -0.5 |
| Ignored the suggestion (other reason) | -0.1 (mild downweight) |
| Rejected as wrong / harmful | -1.0 |
Pattern: bracket every recall with feedback
const hits = await agentdb_pattern_search(query)
for (const h of useful(hits)) {
use(h)
await agentdb_record_feedback(h.id, +1)
}
for (const h of skipped(hits)) {
await agentdb_record_feedback(h.id, -0.1)
}
Without negative feedback, the bandit only sees "winners" and exploration starves.
Don't
- Don't aggregate. Per-id feedback is what the bandit consumes; a single "task succeeded" reward attached to the task itself doesn't update individual memory weights.
- Don't fabricate reward. Honest 0.0 ("retrieved but didn't help") teaches more than dishonest +1.
- Don't only reward — especially record negative reward. It's the higher-information signal.
