Future AGI

Future AGI is an open-source platform for testing, guarding and monitoring AI agents, combining simulations, evaluations, guardrails, tracing and error tracking so teams can catch hallucinations and fix them faster.

Future AGI is an open-source platform for teams that build AI agents and need to know when those agents go wrong. It covers the full loop of testing, guarding, monitoring and improving: simulated conversations surface failures before launch, evaluations score the outputs, guardrails block bad responses in real time, and tracing shows what happened inside a live run. The core idea is that hallucinations and broken behavior should be caught, explained and fixed in one place rather than across several disconnected tools. In practice, a team can design or import an agent, then run it against generated scenarios. The simulation tools let users define branching conversation flows with personas and situations, and the examples on the homepage show voice-agent tests such as a debt collection caller handling hostile or distressed customers. Evaluations apply more than 20 metrics, such as context adherence, factuality and safety, to traces or datasets. An error feed groups recurring problems in the style of Sentry, with trend counts, affected users, a suggested root cause and an immediate fix. Dashboards, alerting and tracing cover production monitoring, and an optimization layer uses reinforcement learning to improve agents between versions. A built-in assistant called Falcon answers questions about error patterns. The audience is mainly engineering and product teams shipping customer-facing LLM agents, including chat and voice support bots, RAG systems and regulated workflows where unsafe answers carry real cost. Instrumentation is handled through traceAI integrations for providers such as OpenAI, Anthropic, Vertex AI, Bedrock, Mistral, Groq, Together AI and Ollama. The codebase is released under Apache 2.0, and documentation describes self-hosting with Docker Compose, which suits organizations with data residency needs. A hosted version is also available, along with enterprise and startup programs. Among alternatives, it sits alongside LLM observability and evaluation products, but it differs by bundling simulation, guardrails and optimization with tracing instead of focusing on logs alone. Teams that only need lightweight prompt logging may find the scope broader than necessary.