Future AGI vs Oberhahn

Generate personas, scenarios and synthetic datasets to stress-test text and voice agents across many conversation branches before launch.

Combine always-free heuristic metrics, code evaluators and LLM-as-judge scoring, including agentic evaluators that localise errors in RAG and tool calls.

Route traffic across 15+ LLM providers with load balancing, failover and caching, plus 15 built-in rule-based guardrails and ML content moderation.

End-to-end traces, sessions, agent graphs, custom dashboards and alert monitors give real-time visibility into production behaviour.

Connect coding assistants, agents, trackers and orchestration layers once, then watch every usage event flow into a single normalized ledger with no manual reconciliation.

See projected month-end spend, cost per session, spend per user, context-window use and an efficiency score, broken out by the source driving each dollar.

Every session links to real Linear, Jira or GitHub issues, so you can read cost per estimate point and see what shipped versus what sank.

Oberhahn flags oversized models, cache misses, runaway sessions and duplicate workflows, each ranked by the spend it affects so you know what to investigate first.