AGI agents are not chatbots. They are not copilots.

They are revenue-making, P&L-responsible autonomous systems that compound value the way a senior operator would - but at infinite scale and zero marginal cost.

Imagine giving your agent an unlimited corporate credit card. It can buy any API, spin up GPUs, run experiments, hire compute. But it must bring qualified meetings, pipeline, and closed-won revenue. Every dollar it spends must return more dollars than it cost.

Most people talk about AGI in the abstract. I am building it in the concrete - one bounded domain at a time.

The domain I chose is revenue and capital. GTMe (AI GTM Engineer) not because it is easy. Because it has the clearest eval in existence:

Did the meeting happen? Did the deal close? Did money land in the bank?

No ambiguity. No benchmark gaming. No cherry-picked demos. The production system is the laboratory. The ARR is the p-value.

At Meta I shipped ranking and bidding models at billions of daily inferences. At Apple I reduced Face ID false positives by 15% and published the GAN refiner that made synthetic training viable (machinelearning.apple.com/research/gan). At Lotus Interworks I took ARR from $25K to $4M in 9 months, cut the sales cycle from weeks to under 5 days, and lifted close rate from 10% to 34%. At Aonxi I built the agent from scratch. Alone. $0 raised. $199K collected. $8K peak day. $2.9M ARR velocity in 60 days of launch fully autonomous money making agent. Creating AGI agents in education/coaching now.

The combination nobody else runs at scale: Deep ML research + agent systems engineering + production revenue data

I replicate frontier papers the week they drop. I deploy the architecture into the live system. I measure whether the theory holds when money is on the line. If it does not, I fix it or throw it out.

An agent with an unlimited credit card and no constraints is not AGI. It is a liability.

The architecture I engineered to keep it safe, auditable, and production-grade:

Multi-agent swarms with 4-layer quality gates No single agent makes a final decision. Every action passes through intent verification, confidence scoring, compliance checking, and human escalation logic before execution.

Human-in-the-loop (HITL) nodes everywhere Y/N vetoes. Approval gates. Override controls. Rich feedback loops. Every human decision becomes a training signal. Low-confidence cases escalate to humans automatically. Human labels feed continuous iteration. This is RLHF running live in production, every day.

Uncertainty-based routing When the agent is not sure, it asks. When it is sure and wrong, it learns. The confidence threshold for autonomous action rises only when production eval data supports it.

Economic ledger with hard gates Every execution logs cost per task, variance, and outcome delta. Scaling is gated on probabilistic ROI - not task success percentage. No logs, no scale. No proven ROI, no autonomy increase.

Kill switches at every layer Agents are stateless and short-lived. They can be terminated mid-task without corruption. The orchestrator owns workflow state - not the agents. This means I can pull the plug on anything, instantly, at any layer.

Winners auto-replicate and compound. Losers terminate with zero drama. Better data beats better algorithms. I have lived this truth at Meta scale.

Every paper I read gets deployed. Every architecture I replicate gets tested on production revenue data. Here is what I am working from and why each one matters for AGI:

50+ repos. All connected. All learning from each other.

super-brain — The living memory + orchestrator connecting every repo, agent, and engine into one unified intelligence layer. V2 now live.

• Complete registry of all 50+ repos with dependency graph
• Daily health checks, pattern detection, improvement suggestions
• Cross-repo learning: what AROS learns, ARIA inherits immediately
• Hybrid memory: Mem0 cloud + MemoryMesh local SQLite
• Orchestrator scans at 5am daily — reports without human input

The compound loop: Research improves agents → Agents generate production data → Data improves research → Brain detects patterns → Every repo gets smarter → Repeat forever.

Core (shared infrastructure)

Revenue & Capital

GTM Engines

Daily Autonomous Fleet (cron)

Research → Production (NeurIPS 2026 pipeline)

QA & Trust

Products

I want to talk to people working on:

• Long-horizon agent reliability in production
• Memory architectures beyond context windows
• RLHF from live human-in-the-loop systems
• Production evals where real outcomes are the metric
• AGI that operates in bounded real-world domains
• Agents that are P&L accountable, not just task-complete

Sam Anmol · Santa Monica, CA · Shipping every day since age 10