Your agents make the same mistakes every run and never learns from them. Kayba analyzes your agent's past execution traces, finds what's failing, and extracts actionable insights. Point your coding agent (Claude Code, Codex) at the results and it implements and deploys the fixes directly to your code. Run again, feed new traces, repeat. Every cycle your agent gets more reliable. We measured 2x improvement in agent consistency on real-world enterprise tasks.

We launched "Agent Prompt Optimizer" on PH three months ago. Since then we talked to hundreds of agent builders and realized prompt optimization alone isn't enough. The real pain is that your whole agent needs to improve: the prompts, the code, the routing logic, the tool usage. So we rebuilt everything around one loop: Kayba analyzes traces and finds failures. Your coding agent reads the results and implements the fixes. You review, run again, feed new traces back. Every cycle your agent gets better. Research labs charge $100K+ for this kind of recursive agent improvement. We wanted to make it accessible to every developer. Would love to hear: - What agent failures cost you the most time? - What matters most to you: speed, cost, or accuracy?

Wow, interesting! Could Kayba also reduce wasted tokens from repeated failures?

Kayba

Make your agents self‑improve from experience

Make your agents self‑improve from experience