agentdb-feedback

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.