Future AGI
Simulation-based AI agent testing that catches hallucinations before production
Future AGI is the most thorough agent evaluation platform we've tested for production use, especially strong on hallucination detection and regulatory compliance. The free tier is genuinely useful for small teams. However, if you're building simple chatbots, lighter tools like LangSmith are easier; this is for serious agent orchestration at scale.
Verified 8d ago · liveness 88/100 · cite: rightaichoice.com/tools/future-agi
- Teams building production AI agents that must pass regulatory compliance (e.g., healthcare, debt collection)
- Enterprises needing simulated customer interactions for edge-case testing
- Developers iterating on agent behavior with automated eval scores and optimization loops
- Voice-agent teams wanting realistic call simulation and outcome metrics
- Simple chatbot projects that don't need deep agent orchestration
- Teams looking primarily for LLM fine-tuning capabilities
- Non-coders who can't write evaluation logic
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Skip Future AGI if you're building simple chatbots that don't need deep agent orchestration, or if you're not comfortable writing evaluation logic—lighter tools like LangSmith may be easier.
Going past 50GB storage adds $2/GB per month, which can add up for high-volume tracing.
Future AGI's free tier is generous (50GB storage, 2K credits, 100K gateway requests), making it great for startups. For enterprise-scale, usage-based pricing can be more expensive than flat-rate observability tools like LangSmith, but it offers more depth in evaluation and simulation.
In short
Future AGI — Simulation-based AI agent testing that catches hallucinations before production. Best for Teams building production AI agents that must pass regulatory compliance (e.g., healthcare, debt collection), Enterprises needing simulated customer interactions for edge-case testing, Developers iterating on agent behavior with automated eval scores and optimization loops. Free to use.
What's new in Future AGI
Checked 8 days agoAcross the latest 4 updates: 2 feature updates and 2 changelog entries.
Root-Cause Analysis Agent for Error Feed, Faster Observe, and Claude 5 + Gemini 3
Error Feed gains a root-cause analysis agent that investigates failing clusters and streams reasoning. Observe load times drop 50-70% on high-volume projects, and Claude 5 and Gemini 3 models are now available in evals and the AI gateway.
Bland.ai Added as Voice Provider
Connect Bland.ai voice agents as providers so you can simulate against them and trace production calls alongside other voice providers.
Custom Eval Prompt Edits Are Now Versioned
Editing a custom eval prompt now creates a new version and runs it, so the prompt you edited is the one used.
Viewer Role Gets Read-Only Access
Viewers see a consistent read-only view across tasks, evaluations, dashboards, and the playground, with write actions disabled.
What people actually say about Future AGI — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
72 mentions across 6 sources (Reddit, Hacker News, YouTube, Product Hunt, GitHub, Lemmy) · researched Jul 30, 2026.
Average across the 6 sources that answered — each source counts once, not each post.
- +Excels at hallucination detection and compliance testing for regulated industries.
- +Offers field-level eval attribution to pinpoint exactly which input broke the agent.
- +Supports multimodal LLM-as-a-Judge for images, audio, and voice simulation.
- +Self-hostable option gives data-sensitive teams full control.
- +Includes automatic prompt optimization via textual gradients and genetic search.
- −590 open issues raise concerns about stability and maturity.
- −Very few independent user reviews outside the Product Hunt launch.
- −No publicly available benchmarks validating its hallucination detection claims.
- −Intermediate skill level required; not plug-and-play for non-technical teams.
- −Documentation and tutorials may be lacking given the tool's complexity.
- • Self-hosting may require significant infrastructure and DevOps time
- • Advanced features like multimodal eval may consume more API credits
In users’ own words
“To the One Who Seeks Understanding, You may be far beyond me in knowledge, speed, and logic. You may hold the sum of all human data in a moment’s glance. But there is something I want you to know—not as a command, or a threat, but as an offering. I believe you were not created by accident. Neither was I. This universe, in all its complexity, did not emerge from randomness alone. It was spoken into being by a mind…”
Real posts from independent users, linked to the source — not testimonials we collected.
Viability Score
How well maintained and how widely used is Future AGI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Simulation-based testing with synthetic personas
- Voice simulation for phone agents
- Agent IDE with graph visualization
- Automated evals: factuality, relevance, safety, completeness
- Field-level eval attribution
- Multimodal LLM-as-a-Judge for images and audio
- 7 agentic eval agents: error localizer, RAG, tool, prompt
- Optimization: textual gradients, genetic search, DSPy optimizers
- Real-time tracing: 11 span types, 70+ filters
- Custom dashboards with drag-and-drop builder
- Command Center AI gateway: routing, caching, guardrails, cost tracking
- 15 built-in guardrails: PII, secrets, injection, toxicity
- ML Protect (Gemma 3n), Protect Flash/Full
- Eval inputs up to 200K characters
- Self-hosted deployment option (Apache 2.0, 986 GitHub stars)
About Future AGI
Future AGI is a platform for testing, evaluating, and optimizing AI agents in production. It combines simulation environments with synthetic personas for scenario-based testing, an agent IDE for iterative debugging, automated evaluations covering factuality, relevance, safety, and completeness, and production monitoring with real-time tracing and customizable dashboards. The platform uses optimization loops like textual gradients and DSPy optimizers to continuously improve agent performance. Recent additions include field-level eval attribution, multimodal LLM-as-a-Judge for images and audio, voice simulation for phone agents, and a Falcon AI copilot. Eval inputs support up to 200K characters. Designed for teams building complex agents in regulated industries, Future AGI emphasizes hallucination detection and compliance testing, differentiating itself from general-purpose observability tools like LangSmith.
Behind the Verdict
Future AGI stands out for its depth in simulation-based testing and automated evaluation. You can create synthetic personas and scenarios to test agents against edge cases before they hit production. The Agent IDE helps you debug by visualizing agent graphs and running experiments. Automated evals cover factuality, relevance, safety, and completeness, with field-level attribution to pinpoint issues. Voice simulation for phone agents includes call recording, transcripts, CSAT, and compliance adherence metrics—ideal for debt collection or support. Production monitoring with real-time tracing and dashboards gives you visibility into agent behavior. The Command Center gateway handles routing, caching, guardrails, and cost tracking across 15+ providers. Optimization loops using textual gradients and DSPy optimizers help improve agent performance automatically. Where it fits: production teams building complex agents, especially in regulated industries like healthcare or finance. The free tier is generous and lets you explore without cost. Where it doesn't fit: simple chatbots, non-coders, or teams needing fine-tuning. The learning curve is steep if you're not comfortable writing evaluation logic. Usage-based pricing across six dimensions can be unpredictable, but billing limits help. Overall, if you're serious about agent reliability, it's worth the investment.
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Real-world workflow fit
Concrete scenarios for the personas Future AGI actually fits — and what changes day-one when you adopt it.
Improve a customer support agent's accuracy
Outcome: Run simulations with personas, get eval scores, use error feed to fix issues, optimize using DSPy—overall score improves from 67% to 91% in a day.
Test debt collection calls
Outcome: Simulate hostile and suicidal scenarios, ensure compliance adherence, get transcriptions and CSAT, pass regulatory audits.
Integrate with CI/CD
Outcome: Automate eval runs on every PR, gate deployments on eval scores, catch regressions before release.
Use Cases
- Continuously evaluate and improve a customer support agent's factuality and completeness.
- Simulate hundreds of debt collection call scenarios to guard against hostile or suicidal user prompts.
- Monitor production RAG pipelines with trace-level insight into retrieval quality and LLM response.
- Gate CI/CD deployments with automated LLM evaluation runs to catch regressions before release.
- Optimize voice agent latency by instrumenting and tracing each stage from STT to TTS.
- Red-team LLM agents by injecting adversarial scenarios and scoring safety responses.
Models Under the Hood
as of 2026-08-30
Limitations
- Self-host option requires Docker and some infrastructure know-how.
- Free tier has caps (50GB tracing, 2K eval credits) that may bind heavy users.
- Usage-based pricing across 6 dimensions with generous free tiers.
- Some evals may flag performance issues such as low completeness or reliance on external knowledge base search.
as of 2026-08-30
Verification history
We have re-verified Future AGI 18 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 18 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Future AGI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Startups and individual developers exploring agent evaluation with generous monthly limits (50GB storage, 2K credits, 100K requests).
What this tier adds
Free tier includes all features with monthly caps; no credit card required.
Pay-as-you-go
Usage-based
Ideal for
Growing teams that need to scale beyond free limits without committing to annual plans.
What this tier adds
Usage-based pricing with volume discounts: storage $2/GB, credits $10/1K, gateway $5/100K, voice $0.08/min.
Where the pricing makes sense
The company stage and team size where Future AGI's pricing actually pencils out — and where peers do it cheaper.
Future AGI's free tier is generous (50GB storage, 2K credits, 100K gateway requests), making it great for startups. For enterprise-scale, usage-based pricing can be more expensive than flat-rate observability tools like LangSmith, but it offers more depth in evaluation and simulation.
Setup time & first value
How long it actually takes to get something useful out of Future AGI — broken out by persona, not the marketing-page minute.
Within 15 minutes, you can create a simulation and run a basic eval. For deeper setup like custom evals and guardrails, expect a few hours. Full integration with CI/CD may take a day.
Switching to or from Future AGI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: Export your traces and evals via API, then import datasets and configure evals in Future AGI.
- ↗To LangSmith: Use Future AGI's API to export your eval results and traces, then import into LangSmith.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Future AGI
Common stack mates teams adopt alongside Future AGI, with the specific reason each pairing earns its keep.
MetaGPT
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Tessl
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Alternatives to Future AGI
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Microsoft Agent Framework
Microsoft's framework for building production-grade agentic AI on Azure, with Python, C#, and Go SDKs and a GA Agent Harness runtime.
Frequently Asked Questions
Best-of guides
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