Future AGI
Agent evaluation platform that simulates voice and chat agents, traces production, and scores hallucinations before customers hit them
If your agent speaks on a phone line in a regulated industry, this is one of the few platforms that simulates the call, scores it for compliance and CSAT, and traces the production version in one place. The free tier is real — 50 GB tracing, 2K eval credits and 60 voice minutes reset monthly, and the vendor says most teams pay nothing. Budget for setup time and for someone who can actually write evaluation logic, because that skill is the price of admission here.
Verified 9h ago · liveness 88/100 · cite: rightaichoice.com/tools/future-agi
- Teams building multi-step agents in regulated verticals like healthcare, debt collection or financial services, where agent statements carry legal risk
- Voice agent teams wanting realistic call simulation with CSAT, latency and compliance-adherence scoring instead of manual transcript review
- Developers who need eval scores tied to specific failure modes plus optimization loops to fix them
- Platform teams consolidating agent tracing, evals, guardrails and gateway routing under one usage-based bill
- Simple RAG chatbots with no tool calls, no voice and no compliance surface — LangSmith is lighter to adopt
- Teams without anyone who can write evaluation logic; code evals and custom judge prompts assume that skill
- Projects expecting zero setup — scenario design and guardrail tuning take real time before signal appears
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Skip Future AGI if you only need request tracing on a single-turn chatbot with no tool calls, no voice and no compliance surface — LangSmith is lighter to adopt and you will not use the simulation or optimization stages.
Only 50 GB of tracing is free; past that storage bills at $2/GB down to $1/GB above 2 TB, so a high-volume voice project with recordings can move out of the free tier quickly.
The free tier (50 GB tracing, 2K AI credits, 100K gateway requests, 1M simulation tokens, 60 voice minutes, resetting monthly) fits a small team or a pilot. Pay-as-you-go unit pricing in the same range as LangSmith and Braintrust, and cheaper than full APM suites, suits Series A to mid-market platform teams. Enterprise buys on Google Cloud Marketplace, Scale or Enterprise plans where cloud commitment spend applies. Heuristic evals and 15 guardrails are free forever, so budget is driven by judge
In short
Future AGI — Agent evaluation platform that simulates voice and chat agents, traces production, and scores hallucinations before customers hit them. Best for Teams building multi-step agents in regulated verticals like healthcare, debt collection or financial services, where agent statements carry legal risk, Voice agent teams wanting realistic call simulation with CSAT, latency and compliance-adherence scoring instead of manual transcript review, Developers who need eval scores tied to specific failure modes plus optimization loops to fix them. Free to use.
No head-to-head comparison for Future AGI yet — the alternatives below are the closest matches we list.
What's new in Future AGI
Checked 3 days agoAcross the latest 5 updates: 4 feature updates and 1 launch.
Simulate voice and chat agents in a rich environment
Point Future AGI at your agent's code — a GitHub repo or an uploaded codebase — and it builds a full simulation environment with real tool calls, deterministic evals and an agent-as-judge.
Buy Future AGI on Google Cloud Marketplace
Subscribe from the Google Cloud console with Google billing directly, so spend runs through your Google Cloud account and can count toward your commitment. Available on Pay-as-you-go, Scale and Enterprise plans.
Simulate outbound Retell voice agents and enable observability for them
Outbound Retell voice agents can be simulated through the Agent Learning Kit with observability enabled in the same flow; transcripts, recordings and evals land like a native simulation.
Root-cause analysis agent ships in Error Feed
The Error Feed agent investigates a failing cluster on its own, streams its reasoning in the Fix tab, and caches the likely root cause for reuse.
Bland.ai voice support and Viewer role
Bland.ai was added as a voice provider across Simulate and Observe, and a Viewer role now grants read-only access. Claude 5 and Gemini 3 families also went live across evals and the gateway.
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: October 2026
How we score →Key Features
- Simulation-based agent testing with synthetic personas and multi-turn scenarios
- Voice agent simulation with per-call CSAT, latency, WPM, stop latency and compliance adherence
- Simulation environments auto-built from an agent's GitHub repo or uploaded codebase
- Outbound Retell voice agent simulation via the Agent Learning Kit with observability
- Bland.ai voice provider support across Simulate and Observe
- Text and voice agent simulation with deterministic evals and agent-as-judge
- Synthetic data generation for eval dataset creation
- Agent IDE with dataset, experiment and agent graph visualization
- Prompt versioning and labels with an agent playground
- Heuristic evals free and unlimited (BLEU, ROUGE, regex)
- Code evals and LLM-as-judge (Turing) charged per run via AI credits
- Seven agentic eval agents including error localizer, RAG, tool and prompt
- Multimodal LLM-as-judge for images and audio
- Root-cause analysis agent in Error Feed that investigates failing clusters and streams reasoning
- Real-time tracing with 11 span types and 70+ filters
About Future AGI
Future AGI is an agent evaluation, simulation and observability platform for teams whose AI agents talk to customers. The pipeline runs in five stages: Simulations spin up scenarios, synthetic data and voice or chat agent testing against synthetic personas; Agent IDE holds datasets, experiments and agent graph visualization; Evaluate mixes heuristic evals, code evals, LLM-as-judge and seven agentic eval agents (error localizer, RAG, tool and prompt among them); Optimize applies textual gradients, genetic search and DSPy optimizers; and Monitor handles tracing plus the Command Center AI gateway for routing, caching, guardrails and cost tracking. Point it at an agent's GitHub repo or an uploaded codebase and it builds a simulation environment with real tool calls, deterministic evals and agent-as-judge scoring. Per-call voice analytics are the differentiator we keep coming back to. Simulated runs return CSAT, agent latency, words per minute, stop latency, promise-to-pay conversion, multilingual switch rate and compliance adherence — not just a transcript. Voice providers Bland.ai and Retell are wired in across Simulate and Observe, and an outbound Retell agent can be simulated through the Agent Learning Kit with observability enabled in the same flow. Claude 5 and Gemini 3 are available as model families in evals and the gateway. The free tier resets monthly and is not a demo: 50 GB tracing storage, 2K eval credits, 100K gateway requests plus 100K cache hits, 15 built-in guardrails, 1M text simulation tokens, 60 voice simulation minutes and unlimited datasets and prompts. Past that, billing is per unit — storage from $2/GB, AI credits from $10/1K, gateway from $5/100K requests, voice simulation from $0.08/minute — with billing limits you can set. Heuristic evals are always free, and BYOK LLM-as-judge costs nothing on the platform side. Subscriptions also run through Google Cloud Marketplace so spend can count toward a cloud commitment. Against LangSmith, Future
Behind the Verdict
We'd reach for Future AGI the moment an agent's wording creates legal exposure. Debt collection is the clearest example in the vendor's own demo: a simulated Riley persona routes hostile callers to a human, hangs up on unproductive conversations and surfaces a suicide-threat handling path. That is not a generic eval suite — it is compliance scoring with a phone attached. The per-call metrics (CSAT, latency, WPM, promise-to-pay conversion, compliance adherence) are the reason voice teams pick this over a transcript-review workflow. The second reason is the loop. Simulations generate the scenarios, evals produce scores, the Error Feed root-cause agent investigates a failing cluster and streams its reasoning, and Optimize applies textual gradients and DSPy optimizers to close the gap. Fewer tools means fewer handoffs, and the same usage-based bill covers tracing, evals, guardrails and gateway routing. Where it bites: you need someone who can write evaluation logic. Code evals and custom judge prompts assume that skill, and scenario design plus guardrail tuning take real time before you see signal. A simple RAG chatbot with no tool calls, no voice and no compliance surface will find LangSmith lighter and faster to adopt. Cost modeling matters at volume. Tracing, LLM-as-judge runs, gateway requests and voice minutes bill separately once you clear the free tier, so high-volume operations should set billing limits before launch rather than after. The unit prices are published and volume discounts kick in at scale, which makes the math doable — just do it. One more caveat on data: the free plan carries 30-day retention and US (Ohio) or EU (Frankfurt) region choice. If your compliance team needs longer retention, that is a paid-tier conversation, not a free-tier one. The
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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.
Points Future AGI at the agent's GitHub repo; it generates varied scenarios that exercise real tool calls, adds deterministic evals automatically and applies an agent-as-judge, then traces the production agent with 11 span types to confirm the same failures occur live.
Outcome: A pre-release eval suite plus production traces on one bill, with failing clusters routed into Error Feed for the root-cause agent to investigate.
Builds a simulation around the collection agent with global prompts for hostile-caller, suicide-threat and request-human branches, then runs outbound Retell calls through the Agent Learning Kit so each simulated call scores CSAT, latency and compliance adherence.
Outcome: Per-call compliance and CSAT scores instead of manual transcript review, and the same calls land in Observe without a separate setup.
Routes traffic through the Command Center gateway across 15+ providers with semantic caching, then layers 15 built-in guardrails plus ML Protect for PII and toxicity, watching cost per virtual key in the dashboard.
Outcome: One routing and safety layer in front of every provider with cost tracking and automatic failover, billed at $5/100K requests after the free 100K.
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 2026-10-16
Limitations
- Usage is metered across six dimensions separately, so projects running heavy LLM-as-judge evals or long voice simulations can accumulate spend past the generous free tiers (e.g.
- 2K eval credits, 1M simulation tokens, 50 GB tracing per month).
- Authoring effective tests assumes some technical capability: code evals, guardrail thresholds, and scenario prompts are user-authored, and simulation environments can be built from an agent's GitHub repo or uploaded codebase.
- Model support is exercised through the platform's own gateway and judges rather than being a generic model host, and underlying model availability is limited to those named on the site.
as of 2026-09-30
Verification history
We have re-verified Future AGI 22 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-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — 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 22 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
Solo developers and small platform teams piloting agent evaluation — a support or voice agent at low volume, or anyone validating the platform before committing spend.
What this tier adds
Starting tier: $0 with no credit card, resetting monthly — 50 GB tracing, 2K AI credits, 100K gateway requests plus 100K cache hits, 1M simulation tokens, 60 voice minutes, 15 guardrails, 3 alert monitors.
Pay-as-you-go
Usage-based
Ideal for
Series A to mid-market teams running agents in production where trace volume, judge runs or voice minutes exceed the monthly free allowances and a predictable unit price beats a seat license.
What this tier adds
Adds metered usage on top of the free tier: storage from $2/GB, AI credits from $10/1K, gateway from $5/100K requests, voice simulation from $0.08/minute, BYOK judge runs at $0 platform cost, plus volume discounts and billing limits to cap spend.
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.
The free tier (50 GB tracing, 2K AI credits, 100K gateway requests, 1M simulation tokens, 60 voice minutes, resetting monthly) fits a small team or a pilot. Pay-as-you-go unit pricing in the same range as LangSmith and Braintrust, and cheaper than full APM suites, suits Series A to mid-market platform teams. Enterprise buys on Google Cloud Marketplace, Scale or Enterprise plans where cloud commitment spend applies. Heuristic evals and 15 guardrails are free forever, so budget is driven by judge
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.
Platform engineer: first traces land in under an hour once you install a traceAI SDK (30+ libraries) and set an API key. Evaluation lead: budget a day or two — scenario design, eval prompts and guardrail thresholds are authored, not auto-generated, though pointing the platform at an agent's codebase auto-builds a simulation environment with scenarios, deterministic evals and an agent-as-judge.
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: install a traceAI SDK (30+ supported libraries), re-point traces, then rebuild your evaluators as heuristic, code or LLM-as-judge evals using BYOK keys at $0 platform cost.
- →From a self-built eval harness: import existing datasets (uploads up to 25 MB), map them into the Agent IDE, and replace hand-rolled scoring with the seven agentic eval agents.
- →From manual voice QA: connect Bland.ai or Retell as a voice provider, replay scenarios against the agent, and let per-call CSAT, latency and compliance metrics replace transcript review.
- →From a standalone LLM gateway: move routing to Command Center, which covers 15+ providers with 9 load-balancing strategies, auto-failover and exact plus semantic caching across 6 backends.
- ↗To LangSmith: export datasets and eval results from the Agent IDE, then re-instrument traces with LangSmith's SDKs — you keep heuristic scoring but lose the agentic eval agents and voice simulation.
- ↗To a self-hosted OSS stack: Future AGI's open-source components are Apache 2.0, so you can self-host via Docker and keep traceAI instrumentation while managing scaling yourself.
- ↗To separate point tools: route traffic through your own gateway and keep guardrails in a dedicated provider such as Lakera or Llama Guard, which Future AGI already lists as external integrations.
Integrations
Resources & Guides
Tutorials & Learning
FutureAGI MCP: Run Evals, Traces & Simulations From Your Editor
Future AGI
Building AI Agents That Self-Improve from Production Data
Future AGI
Engineering Coding Agent Harnesses for Data Workflows | Snowflake × Future AGI
Future AGI
YouTube returned 6 videos for “Future AGI”, and we withheld 3: 3 did not mention Future AGI. Showing the 3 we can prove are about Future AGI.
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.
Arena AI
Arena AI ranks chat models, coding agents and fullstack builds on live head-to-head human votes.
LangSmith
LangSmith is agent and LLM observability: trace, monitor, and evaluate AI agents in cloud, BYOC, or self-hosted.
Zhipu GLM
Zhipu GLM is a full-stack large-model platform built on open GLM weights, with MaaS APIs, ZCode and shipped computer-use agents.
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