Promptic - Trace, evaluate, and optimize AI agents and LLM prompts

I’ll benchmark the router-based, tool-first, and single-agent variants on your evaluation dataset and compare the tradeoffs before suggesting an implementation change.

Three architectures are registered against the same dataset and KPIs. Now I can benchmark them head to head.

Running all three variants on the same 248 cases.

No single variant wins every metric — quality, cost, and latency each point at a different architecture. Let me weigh them against your priorities: quality first, then cost, then latency.

Router-based gives the best quality at acceptable cost, and its only weakness — latency — stays inside your budget. That’s the one to implement.

Before I implement the winner, let me read the traces where router-based still fails — so the fix targets the real problems instead of guesses.

The traces make the fix obvious: give billing its own route and scope each route’s tools so the wrong one stops getting retried — and the extra hop disappears once routing is explicit.

I’ll implement the router-based architecture with the two fixes the traces surfaced.

The router-based variant is implemented and ready to validate against the same dataset.

Re-running the evaluation on the implemented variant to confirm the benchmark holds.

Validated and shipped. The change is backed by the full comparison, so the next iteration starts from evidence, not guesswork.