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SUPREME AI AGI

Prediction · On the road to AGI

AGI will arrive as a system, not a single model: models, tools, memory, verification and many cooperating agents working together.

Long termMedium confidenceOn track

Why we think so

Every large jump in what AI can do in practice has come from adding something around the model: tools (ReAct), memory and reflection (generative agents), and teams of agents with roles (AutoGen). Each part covers a weakness of the others. We expect general intelligence to come from organising these parts well, the same way an organisation can do more than any single person in it.

What would prove us wrong

A single model, used alone without tools, memory or other agents, matching people across most kinds of work.

Review notes

21 October 2026: On ARC-AGI-3 the same model scored 62.7% with a minimal harness and 99.9% with one that keeps its reasoning state and compacts memory: the system around the model made the difference. Multi-agent systems (Co-Scientist) and propose-test-keep loops (AlphaEvolve) produced the most notable scientific results of 2025.

Horizon
Long term. More than 10 years out, or no clear timeline yet.
Confidence
Medium confidence
Status
On track. New evidence points the way we predicted.
Made
21 October 2026
Last reviewed
21 October 2026

The evidence it rests on

Public results from teams around the world. Read the sources and judge for yourself.

  • · Stanford University and Google researchers (arXiv)

    Generative agents simulate a small society

    Twenty-five agents with memory, reflection and planning lived in a simulated town and showed believable individual and group behaviour.

    What it means for AGI: memory and reflection give agents continuity, and groups of agents produce behaviour no single prompt produces.

    Source: arxiv.org(opens external site)

  • · Microsoft Research and collaborators (arXiv)

    AutoGen: multi-agent conversation for complex tasks

    A framework where several agents with different roles talk to each other, use tools and involve people to solve tasks.

    What it means for AGI: building with groups of agents has become a mainstream engineering pattern.

    Source: arxiv.org(opens external site)

  • · Princeton University and Google researchers (arXiv)

    ReAct: language models that reason and act with tools

    Showed that letting a model alternate between reasoning steps and tool actions improves results on question answering and decision tasks.

    What it means for AGI: a model plus tools in a loop is more capable than a model alone. Most agent systems today build on this idea.

    Source: arxiv.org(opens external site)

  • · ARC Prize Foundation (testing OpenAI's model)

    GPT-6 Astra scores 99.9% on ARC-AGI-3, with a caveat

    ARC Prize verified 62.7% with its standard harness and 99.9% with a harness that keeps the model's reasoning state between steps; in one setting it used fewer actions than the median person on 96% of levels. ARC Prize states it is not claiming this is AGI.

    What it means for AGI: the gap on learning new tasks closed within six months of the test's launch. The jump from 62.7% to 99.9% came from how the system keeps memory, not from a new model, which supports the view that AGI is a system, not one model. The run cost about $18,800, far more than a person would.

    Source: arcprize.org(opens external site)

  • · Google researchers (arXiv)

    Co-Scientist: a multi-agent system that proposes research hypotheses

    Specialised agents generate, critique and rank hypotheses in a tournament. Drug-repurposing candidates it proposed for acute myeloid leukaemia were validated in lab (in vitro) experiments.

    What it means for AGI: a team of agents, not a single model, did useful scientific work that was checked in a real lab.

    Source: arxiv.org(opens external site)

  • · Google DeepMind

    AlphaEvolve: a coding agent that discovers better algorithms

    An evolutionary agent built on Gemini models found a way to multiply 4×4 complex matrices with 48 multiplications, improving on Strassen's 1969 result, and improved on known answers for a share of over 50 open maths problems.

    What it means for AGI: a loop of models proposing, testing and keeping the best ideas can produce new knowledge, an early form of self-improvement.

    Source: deepmind.google(opens external site)

  • · Linux Foundation

    Open standards for AI agents get a neutral home

    The Linux Foundation formed the Agentic AI Foundation around the Model Context Protocol, goose and AGENTS.md, backed by major AI and cloud companies.

    What it means for AGI: agents from different makers are getting shared ways to connect to tools and to each other, a precondition for large agent societies.

    Source: linuxfoundation.org(opens external site)

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