Learning loop
Evidence becomes memory.
The system gets better because execution is measured.
AgentKit captures what happened, compares it with the recommendation, and feeds the result back into future search, ranking, and reasoning. Evidence is not a report; it is the raw material for better commercial judgment.
Evidence record
What happened
Execution result, timing, and surface success.
Why it happened
Context, assumptions, and policy constraints.
What changed
Knowledge, rules, and future search heuristics.
What to do next
Refine, retry, or shift the objective.
Evidence types
- Execution success and failure
- Surface-level delivery results
- Founder approval or rejection
- Incoming demand, replies, and conversions
Learning artifacts
- Learning Record
- Knowledge
- Policy updates
- Search heuristic refinements
What improves over time
Search quality
AgentKit gets better at finding relevant opportunity space.
Ranking clarity
Options are compared using better evidence and fewer assumptions.
Execution reliability
Failed surfaces can be isolated and retried.
Commercial judgment
Future recommendations become sharper and more useful.
Primary CTA
If the system cannot learn, it cannot compound.
AgentKit only becomes valuable when every outcome feeds the next decision.