# Future AGI Evaluation, simulation, and monitoring tools for teams building AI agents. - Findings24: - Website: - Pricing: Freemium - Categories: AI Observability, LLM Evaluation ## What is Future AGI? Future AGI is a development and operations platform for AI agents. It combines evaluation, simulated conversations, runtime guardrails, and production tracing to help teams identify failures and investigate their causes. Dataset management, prompt versioning, experiments, and an agent playground support testing changes before release. Teams can work through the dashboard, APIs, and SDKs, using evaluations during development or against ongoing and historical activity. The platform also includes an AI gateway for model routing, caching, and cost tracking. ## What can Future AGI do? ### Multiple evaluation methods Combine heuristic checks, code evaluations, LLM-as-judge scoring, and agentic evaluation agents for RAG, tools, prompts, and error localization. Run evaluations inline, continuously, on historical data, or in CI/CD. ### Text and voice simulation Test agents with predefined or custom personas and script, dataset, or graph scenarios. Inspect transcripts and compare sessions to examine how conversations unfold. ### Configurable runtime guardrails Apply input and output checks for PII, prompt injection, toxicity, secrets, and hallucinations. Configure each rule to enforce, monitor, or log, with score thresholds and PII remediation. ### Production observability Inspect traces, spans, sessions, and agent graphs. Search with filters, build custom dashboards, and send alerts through Slack or email to track operational issues. ### Datasets and agent experiments Manage datasets, compare models in experiments, and version prompt templates. The visual agent playground supports tool calls and format validation; AI features can generate synthetic data or improve prompts. ### Model gateway controls Route requests across supported LLM providers with load balancing, automatic failover and retries. Exact and semantic caching, budgets, rate limits and virtual keys support usage management. ## Use Cases ### Validate a support agent before release Use knowledge-base examples to evaluate factuality, relevance, and completeness. Compare prompt or retrieval changes in experiments before selecting a version for deployment. ### Rehearse difficult voice conversations Simulate callers with different personas and situations to test escalation, interruptions, and sensitive conversational branches. Review transcripts and session comparisons to identify weak responses. ### Investigate production failures Follow a problematic session through its agent graph and traces, then use error analysis and evaluation explanations to locate issues in retrieval, tool usage, or prompts. ### Review examples with human annotators Assign traces, spans, or dataset items to annotation queues. Collect numeric, categorical, text, star, or thumbs labels to support team review and scoring. ## Frequently Asked Questions ### What does the free tier include? No credit card is required. Monthly allowances include 50 GB of storage, 2,000 shared AI credits, 100,000 gateway requests, 1 million text simulation tokens, and 60 voice simulation minutes. Usage beyond the allowances is billed by consumption. ### Can I use my own model API keys? Yes. BYOK LLM-as-judge evaluations and model calls in experiments and playgrounds carry no platform charge. Model-provider fees, if any, remain separate. Heuristic evaluations are always free; managed model and code evaluations use AI credits. ### How are team access and retention handled? The public plans include unlimited team members and projects. The Free offer lists 30-day data retention and separately lists persistent datasets and annotations. Storage remains part of usage accounting. Paid add-ons extend retention and annotation queue limits. ### Do evaluations and guardrails guarantee safe, correct output? Use scores and guardrails as configured checks, then validate them against your own examples and human annotations. Review false positives and missed errors, and monitor rules before enforcing them. Simulation and scoring do not replace review of a real deployment. ## Quick Facts - **Category**: AI agent testing and observability - **Access**: Web dashboard, API and SDKs - **Primary users**: Teams developing and operating AI agents - **Billing**: Monthly free allowances with usage-based overages - **Website**: https://futureagi.com