Future AGI

Future AGI is an AI lifecycle platform that helps teams build self-improving agents by detecting what broke, learning why, and feeding fixes back so every version ships smarter. It combines rapid prototyping, rigorous evaluation, continuous observability, and reliable deployment to support enterprises throughout their AI journey. The platform covers the full loop from simulation and evaluation to real-time monitoring and reinforcement learning optimization, all accessible via a web UI, Python/TypeScript/Java SDKs, and a REST API.

• Simulations — Simulate thousands of multi-turn text and voice conversations against branching scenarios and AI-generated personas before deploying to production.
• Agent IDE — Build and test multi-step AI agent workflows visually on a drag-and-drop canvas with no code required.
• Evaluate — Run 76+ local heuristic metrics, LLM-as-Judge, or proprietary Turing cloud models across datasets, simulations, and CI/CD pipelines.
• Error Feeds — Sentry-style error tracking that automatically detects, clusters, and surfaces agent failures with root-cause recommendations.
• Guard / Protect — Block AI hallucinations and enforce safety policies in real-time with 15+ built-in guardrails covering PII, prompt injection, toxicity, and bias.
• Prism AI Gateway — A unified API gateway for 100+ LLM providers with intelligent routing, semantic caching, cost tracking, rate limiting, and built-in guardrails.
• Tracing & Observability — End-to-end OpenTelemetry-based tracing with auto-instrumentation for 45+ frameworks including LangChain, LlamaIndex, CrewAI, OpenAI, Anthropic, and more.
• Prompt Workbench — Create, version, label, and optimize prompts using 6 SOTA algorithms (ProTeGi, GEPA, PromptWizard, Bayesian, Meta-Prompt, Random Search).
• Datasets & Synthetic Data — Manage versioned evaluation datasets, generate synthetic data from schemas, and import from HuggingFace or CSV.
• Annotations — Human-in-the-loop annotation queues with 5 label types, multi-annotator support, review workflows, and inter-annotator agreement metrics.
• RL Optimization — Continuous improvement via reinforcement learning feedback loops applied to agent prompts and configurations.
• MCP Server — Interact with the platform via natural language from Claude, Cursor, or VS Code using the Model Context Protocol.