future-agi
Future AGI is an enterprise platform for building, testing, evaluating, guarding, optimizing, and monitoring AI agents to catch hallucinations, enforce guardrails, and continuously improve agent performance across production workloads.
future-agi is ai agents software teams evaluate for business operations. Use this page to review pricing, integration signals, and the best alternatives before you commit.
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Quick Overview
Best for: Business Operations
What it does
AI Agents software for decision-makers comparing workflow fit and alternatives.
Best fit
Business Operations
Pricing snapshot
Free
Next step
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future-agi
Future AGI is a platform designed to help teams build, test, evaluate, protect, and improve AI agents in production. The product offers end-to-end tooling—simulations to stress-test agents at scale, an Agent IDE for iterating and running experiments, an evaluation suite to catch factuality and safety issues, optimization tooling to apply learning back to models, and observability (tracing, dashboards, alerting, and a command center) to monitor agents in real time. It is positioned for enterprise and startup customers that need guardrails, compliance, and continuous improvement across agent deployments.
AI evaluation and optimization platform for automated quality assessment and performance enhancement.
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Claim this listing for $29Key Features
Simulations & Scenarios
Run large-scale, multi-turn conversational simulations, adversarial inputs, and persona-driven scenarios to surface edge cases before deployment.
Agent IDE & Experiments
Purpose-built environment to develop, iterate, and run structured experiments across models and parameters (Agent IDE, experiment runs, A/B-style trials).
Evaluation Suite
Create and run evaluation datasets against prompts and responses to measure hallucination, factual grounding, relevance, safety, and compliance across contexts.
Guardrails & Protect
Real-time guardrails that intercept harmful outputs, block prompt injections and PII exposures, and enforce compliance rules with an audit log of blocked actions.
Error Feed & Root Cause
Centralized error feed to surface, triage, and investigate failures (hallucinations, tool errors, PII leaks, policy violations) with traces and recommended fixes.
Tracing, Observability & Command Center
End-to-end tracing of requests, detailed spans and trace trees, dashboards, alerting rules, and a command center to monitor agent performance and incidents in real time.
AI Optimization & Continuous Improvement
Apply evaluation feedback and optimization runs (including RL-style retraining/auto-tuning) to iteratively improve agents and reduce error rates.
SDKs, API & Sandbox
Developer-facing SDKs and API references, plus a sandbox environment for hands-on evaluation and onboarding (including CLI example: pip install futureagi).
Integrations with LLM Providers
Supports multiple model providers and models (examples in UI include gpt-4o, gpt-4o-mini, claude-3.5, gemini-2) and provider management in the gateway/requests logs.
Pricing
Free sandbox access / "Get Started - Free" and sandbox/demo request available (no detailed pricing tiers shown on page).
Use Cases
Customer Support & Voice Agents
Simulate and evaluate support workflows, STT/TTS, and multi-turn voice calls to catch hallucinations and compliance issues before calls go live.
Internal Copilots & Enterprise Tools
Test role-based scenarios, enforce access boundaries, and audit agent actions to prevent data leakage and unauthorized access in internal copilots.
RAG & Document Search
Stress-test retrieval, verify citations against sources, remove unsupported claims, and optimize chunking and retrieval strategies for grounded answers.
Autonomous & Multi-step Agents
Simulate workflows, detect loops and unexpected actions, enforce boundaries and gates, and trace step-level decisions for multi-step autonomous agents.
UI Automation (Computer-Use Agents)
Simulate UI workflows, evaluate click/form accuracy, block destructive actions, and replay sessions to ensure safe UI automation.
Coding Agents & PR Review
Test code-generation agents across languages and frameworks, evaluate code quality and security, block dangerous operations, and trace file changes.
Integrations
OpenAI (gpt models)
Evidence of provider usage in request logs and model selection (gpt-4o, gpt-4o-mini).
Anthropic (Claude)
UI and logs reference Claude models (e.g., claude-3.5, claude-sonnet) as selectable LLMs.
Google (Gemini)
Model usage examples include gemini-2 / gemini-pro in experiments and request logs.
Benefits
Limitations
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Frequently Asked Questions
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Getting Started
- 1 Step 1: Request sandbox access via the "Request Sandbox Access" form on the site (you'll receive a personalized sandbox link).
- 2 Step 2: Read the documentation and API/SDK references available from the site to understand integrations and evaluation tooling.
- 3 Step 3: Try the CLI example showcased on the site: install the package and initialize the sandbox (shown: "$ pip install futureagi" and "$ futureagi init --sandbox"), then open the dashboard to simulate and run experiments.
Support
docs
Documentation, API Reference, and SDK Reference links are listed on the site for developer onboarding and integration.
sandbox_request
Personalized sandbox access via a request form (site states: "We'll send your personalized sandbox link within 24 hours").
github
Repository and open-source presence: site links to GitHub ("Star on GitHub").
API
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