Future AGI

This year, we’re thankful for… failed experiments. 🦃 Because every time an agent: > hallucinated in simulation instead of with a real customer, > broke inside a sandbox instead of a support ticket, > or got caught by a guardrail instead of Legal, …someone on the other side slept a little better. At Future AGI, we don’t just cheer for “AI that works. We’re weirdly grateful for: > the red flags in eval dashboards, > the edge cases that refuse to be ignored, > and the teams who keep asking, “But what happens in the worst case?” They’re the ones turning AI from “demo magic” into systems you can actually trust on a Monday morning. So this Thanksgiving, here’s to: > builders who simulate before they ship, > PMs who insist on real evaluation, > and everyone quietly making AI a little less chaotic. Happy Thanksgiving from Future AGI. Grateful for the work you do that nobody sees… because when it’s done right, nothing breaks. 🧡

Debugging transcripts while users debug their patience” Transcripts don’t capture the 2-second pause that made someone think the call dropped. They don’t reveal which part of your pipeline - STT, LLM, or TTS turned “I need to update my billing” into confusion. Voice AI needs testing that runs deeper: → Simulate 1000 accents and noise conditions before users hit them → Isolate whether your STT, reasoning, or speech synthesis caused the drop → Catch regressions in tone and timing, not just accuracy → Compare providers to know if you’re using the right stack That’s where audio-native evaluation changes everything. This edition analyzes the differences between audio and transcript evaluations for Voice AI - what each does well, where they differ and where you need which approach. Read the full comparison here. 👇

10k+ downloads later, our “Agent Evaluation” ebook has basically turned into a quiet standard for a lot of teams shipping agents. The most common messages we got from AI leaders, Engineers and PMs: >> Got over ‘vibes-based’ evals and moved to a real performance scorecard aligned with business >> The failure-modes checklist caught issues/hallucinations before our launch Inside the ebook, you’ll find things teams keep stealing and pasting into their own docs: ✅ A step-by-step eval pipeline for RAG + voice + multimodal agents ✅ A Failure Modes Taxonomy you can turn into Jira tickets or eval tags ✅ A Production Readiness Checklist for agents that your PM / Eng / Data can all agree on It is written by folks who’ve shipped eval stacks at scale (ex-Microsoft, DRDO-backed projects, and fast-moving AI startups)...not just theorists. If you’re responsible for reliability on RAG, copilots, or multimodal agents, this will save you weeks of trial-and-error. 📘 Download the free ebook → https://lnkd.in/dhQwmrnR

🚀 Bring enterprise prompt infrastructure to your n8n powered-workflows. Introducing Future AGI × n8n integration: Two powerful platforms, one game-changing workflow. Our official community node on n8n brings enterprise prompt management directly into your favorite workflow automation platform. Instead of managing prompts manually in each n8n workflow, Future AGI automates everything: 1. Centralized Prompt Management Create, test, and version prompts in Future AGI → Pull them into n8n by name. 2. Smart Template Variables Map workflow data directly into prompt variables (customer name, context, history whatever you need). 3. Built-in Observability & Evals Every prompt execution automatically logged. Run evals. Track performance. All native. 4. Version Control That Works Switch between production, staging, or any version instantly. No workflow edits required. In your n8n workflow: Add Future AGI node -> Pull prompt by name ("support-agent-v3") -> Map workflow data to variables = DONE. Everything else is AUTOMATED. No manual versioning. No copy-paste. No tracking spreadsheets. 80% less time managing prompts. 100% focused on building agents. Install: Search "Future AGI" in n8n community nodes. <More in comments>

“Chat box = agent UX” is the biggest lie we’ve all been telling ourselves. Last week, we hosted a deep-dive on Agentic UX with Tyler Slaton (Founding Engineer @ CopilotKit) and the recording is now live on our website. 🎥 Watch the replay: https://lnkd.in/db9M2A2i In ~1 hour, we covered: - Why long-running, event-driven agents break classic request-response UI - How AG-UI standardizes agent → user events across LangGraph AI, Mastra, Microsoft, Google, etc. - Practical patterns for streaming, tool-call surfacing, and HITL approvals so apps don’t feel “stuck” for 30s+ - Where this is going next: voice-native agents and self-improving UX via user feedback If you’re still shipping agents behind a single text box, this will give you a new mental model for how the UI should look.

Last week we perfected our SIMULATE platform for voice agent builders. Production calls are where your agent’s assumptions die. Your dashboards see “calls.” Your users bring context, emotion, and chaos.  and SIMULATE lets you rehearse that chaos in a controlled environment before it hits production. 🧬 Pre-Built & Custom Personas Simulate real humans, not ideal ones. Configure tone, emotion, background, and behavior or choose from a library of personas like frustrated customers, drunk callers, or confused users. Same agent. Different humans. Completely different outcomes. 📡 Voice Observability Support Turn every Retell AI/Vapi call into a trace you can inspect. Get call-level visibility, real-time debugging, and audio interaction insights in a single workspace - from simulation to production. 🧪 End-to-End Voice Stack Experimentation A/B test full voice stacks from STT → LLM → TTS using your own customer scripts. Compare providers like OpenAI, ElevenLabs, Deepgram, etc. on accuracy, latency, and audio quality to find the stack that actually works for your users. 📜 Richer Transcripts & Realistic Speech Get transcripts with persona cues, per-turn timing, and evals across the entire conversation. Test with more natural and multilingual accents to see how agents behave in real-world conditions, not lab-perfect audio. We're proud to say SIMULATE is now the most advanced end-to-end platform for Voice AI testing, evaluation, and observability. Discover the full power of Future AGI’s SIMULATE platform. <Docs link in comments> 👇

One thing that I love in leading the industry towards building self healing AI agents is that competitors looking in to our platforms, logging in, checking us, checking our positioning, changing their positioning, changing even their titles. We have open to use platform, doing research with world class team, breaking things, making things. All are welcome to try our platform and get inspired here: https://app.futureagi.com let me know if you need some free credits to start with and get wowed... AI Hallucinates, and we are fixing it...

In most boardrooms, AI has already moved from “innovation experiment” to “budget line item.” The real signal of maturity isn’t how much you’re spending on LLMs, but whether you can explain what that spend is buying in efficiency, revenue, and resilience. Cost optimization here isn’t a cost-cutting exercise, it’s governance. Dive into the full breakdown: 👇

1 Day to Go! The best AI products don't feel like AI products. They feel like collaboration. That's an interface problem, not a model problem. Tomorrow: Tyler walks through how to build for this- designing systems where agents and humans work together, not in sequence. Last chance to register if you're shipping agents in production. RSVP- https://luma.com/cgvudw30 See you tomorrow!

Day 1 at function1 didn’t slow down for a second. Nikhil Pareek and Vrinda Damani met some incredible folks who care about the same thing we do: moving AI from cool prototypes to reliable systems that hold up in production. If this was the warm-up, Day 2 is going to be fun. Dubai, keep the momentum coming.