Future AGI vs Openlayer

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.

Dozens of pre-built evaluations cover quality, safety, security, and performance, with LLM-as-a-judge scorers, session-level metrics, and support for writing your own custom tests against your own data.

Import a repository and run evaluation suites automatically on every change, benchmark each iteration, and block releases when a metric regresses beyond the threshold you define.

Trace every request through multi-step agent workflows, including tool calls and retriever steps, with token counts, latency, cost per request, alerting integrations, and subpopulation analysis.

Register each AI system with an owner, purpose, data sensitivity, and risk tier, score inherent risk across multiple dimensions, and move systems through staged approval gates before they reach production.