If We Wanted to Build an AGI, What Would We Need to Put Inside It? - Ali Zamani

# A problem a big model alone won't solve

Today's general-purpose AI models do very diverse work — writing, text analysis, image and video generation, coding, and even some computer operations. But the picture we should NOT have in mind is:

wrong_model.txt

# This is NOT how AGI emerges:
 

              small model
              →
              bigger model
              →
              much bigger model
              →
              AGI ✗
            
 
# Bigger models unlock many capabilities, but current limits
# are not defined by model size alone.

Recent research shows progress increasingly comes from post-training and more compute at use time (like longer reasoning chains), while some problems — reliably executing long tasks and trustworthy interaction with the physical world — remain challenging. So AGI may be more like a complete system than a single model.

# The big picture: a system, not a lone brain

If we sketch an engineering picture of general intelligence, we probably end up with a system of several major subsystems connected in a loop:

agi_architecture.txt

This map is not an official AGI architecture — no such agreed architecture exists. It's a conceptual model to see which parts we would probably need if we designed a general, autonomous system. Let's walk through them one by one.

# 1. Perception: AGI must be able to see the world

A general intelligence can't only receive text. To work across diverse environments, it must handle many kinds of input: text, images, audio, video, files, the web, screens, sensors, and databases. When a user says "why isn't this device working?", the system may need the device's photo, its sound, the error code, the manual, and repair history. And it's not just about "seeing" — the system must convert this information into a representation usable by the reasoning stage:

perception_flow.txt

Today's multimodal models have covered much of this path, but making such capabilities work across all environments with high reliability is a separate problem.

Not all information matters equally

Suppose a robot's camera sees a table, chair, cup, window, door, person, laptop, and lamp — but its goal is only "pick up the laptop from the table." A general system must find what's relevant amid the flood of information:

relevance.txt — goal: pick up the laptop

# Not all information deserves equal attention:

Laptop critical

Table important

Cup minor

Window ignore

This filtering ability matters more than "more data": if the whole world enters the reasoning process with equal weight, compute costs explode and the chance of getting lost in irrelevant details grows.

# 2. Memory: AGI must not start from zero every time

Suppose you tell a system today: "This project is built with Laravel, our database is PostgreSQL, and auth uses OTP" — and tomorrow you have to explain it all again. That's closer to a stateless model than a general agent. But memory isn't just a big context window; at least three distinct concepts are worth separating:

memory_types.txt

# A conceptual model (not a formal AGI architecture):
 
Working Memory   # current-problem information
    Current Task: Fix authentication bug
 
Episodic Memory  # events and experiences
    Last deployment failed — migration X was missing
 
Semantic Memory  # structural knowledge
    PostgreSQL → Relational DB → SQL → Transactions

Memory without retrieval is just a big warehouse

Now suppose the system has stored millions of events over two years, and the user asks: "Why did we change this API last week?" The system must find the relevant part within that mass:

retrieval_flow.txt

Here memory becomes more than storage — it becomes a retrieval system for experience and knowledge. Even a huge memory helps little without a good way to find what's relevant.

# 3. Reasoning and planning: an answer is not a job done

Having information differs from using it. A general system must derive conclusions from facts, rules, goals, and constraints. For a software problem, for instance:

reasoning.py

# Facts:
⚠ Database is slow
⚠ Queries are expensive
✗ Index is missing
⚠ Traffic has increased

▶ hypothesis (to be tested, not assumed):

▶ Missing / Inefficient Index ──→ Likely Cause ✓

But planning is harder than single-step reasoning. Tell an AI "launch a profitable online business in a new market" — we don't know which market fits, what product to sell, or which hypothesis is worth testing. The system must first decompose the problem itself:

decompose.txt

Goal
├── Research Market
├── Find Customer Need
├── Generate Hypotheses
├── Evaluate Opportunities
├── Build Prototype
├── Test With Users
├── Measure Results
└── Iterate ↺

This is no longer question answering — it's problem solving. And a fixed plan isn't enough: after a failure, the system shouldn't say "I made my plan, so I continue." A good planner interacts with the environment:

adaptive_loop.txt

# 4. Tools and environment state

Intelligence without access to the world is limited. A purely language model may have stale information, but an agent can search with a browser, compute with Python, and access databases, terminals, and APIs. The model no longer just produces answers — it acts.

Tool calling alone is not AGI: Being able to call get_weather() isn't general intelligence. The harder questions are: Do I even need a tool? Which one? How do I interpret the result? What if the tool fails? What matters is the Goal → Reasoning → Tool Selection → Observation → Reasoning Again cycle — not the function itself.

Understanding the current state

Suppose an agent works on a computer where the browser is open, the terminal is ready, the server is offline, the database is connected, and a file is missing. It performs an action (Start Server) and the state changes:

state.txt

              State₀
              ── Action ──→
              State₁
              ── Action ──→
              State₂
              ── Action ──→
              State₃
            
 
# The system must answer:
# "Where am I now?" and "What happens if I do this?"

Without such state representation, long-horizon planning becomes very hard. One step beyond it sits the concept of a world model: an internal representation that lets the system think about the probable consequences of actions — "if I move the hand slowly, I likely grasp the glass; if I push hard, it may fall."

ⓘ

World model and memory together: Memory looks at the past ("what happened before?"), the world model looks at probable futures ("what happens if I do this?"), and reasoning decides between them. Exactly what kind of world model an AGI needs is still an open research question — but for a system that must act in an environment, modeling state and consequences makes a lot of sense.

# 5. Continual learning and generalization

A typical model has one training phase: Data → Training → Model → Deployment. But a hypothetical general intelligence must keep learning through interaction with the world: try an approach, analyze the failure, learn, try again. One problem, though:

Catastrophic forgetting: If a model is repeatedly trained on new information, it may lose older skills. Continual learning is not just "train again and again" — it also involves preserving old knowledge, adding new knowledge, and managing conflicts between them.

Generalization: maybe the most important word in AGI

If the model only memorized, a new problem catches it off guard. But if it can transfer knowledge — realizing that city routing and network routing are both, at their core, graph search — it's closer to general intelligence:

transfer.txt

City Routing      ↕
Network Routing   ↕
Graph Search      ← same underlying structure

It should even learn genuinely new things from the environment: tomorrow a new API ships that the model never trained on, but the documentation exists — Documentation → Understand → Create Tool Interface → Test → Use. That's very different from retraining the model on a new dataset from scratch.

# 6. Verification: the system must not always believe itself

In the article on AI hallucinations we saw that a language model can produce a fluent, confident answer and still be wrong. For an agent this matters even more, because the error can move from the text level to the action level. The sentence "migration succeeded" proves nothing by itself:

verify_flow.txt

And perhaps as important as reasoning: the system must be able to say "I don't know." A trustworthy agent distinguishes between these states:

epistemic_states.txt

I know.            # high confidence, strong evidence
I can infer.        # reasonable inference, worth verifying
I need to verify.   # stop → search → ask → verify
I don't know.       # honest uncertainty beats confident nonsense

# 7. More autonomy, more risk

One system just answers; another can run Analyze → Plan → Use Tools → Execute → Verify → Correct → Finish on its own. These differ not only in capability but in autonomy. Yet if an agent has access to email, databases, terminals, cloud, and payments, one small error can have large consequences. A real AGI acting in the world needs safety layers:

permissions.txt

# Layered safety for an acting agent:
 
Read File        →  Allowed
Run Test        →  Allowed
Deploy Staging  →  Rate-limited
Delete Database  →  High Risk → Human Approval
 
# + Sandbox · Audit Log · Rollback · Tool Restrictions

Safety research likewise draws a sharp risk distinction between systems that merely answer and agentic systems that execute chains of actions in an environment.

Does AGI need a body?

Put AGI in a robot and entirely different problems appear: sensor noise, latency, friction, uncertainty, and mechanical limits. This is exactly the issue we examined in why robot hands are harder to build than robot brains: making the decision can be far easier than turning it into precise, reliable motion in the physical world. Embodied intelligence is an important research direction, but whether having a body is a necessary condition for AGI remains scientifically unsettled.

# 8. The full component list

Now we can gather everything we discussed into one table — along with each part's status today:

agi_components.txt

# Component              │ Status today
# ─────────────────────────────────────────────
Perception / Multimodal   │ ✓ exists
Working Memory            │ ✓ exists (context window)
Long-Term Memory          │ ~ partial (RAG, vector stores)
Reasoning                   │ ✓ exists (imperfect)
Planning                    │ ~ partial
World Model                 │ ~ partial (open research)
Tool Use / Action          │ ✓ exists
Continual Learning         │ ✗ hard (catastrophic forgetting)
Verification                │ ~ partial
Generalization / Transfer  │ ~ partial
Safety / Permissions       │ ~ active research

But more important than having these boxes is the relationship between them. AGI isn't supposed to have these capabilities separately — it must wire them together in a single loop. Interestingly, this view mirrors what we saw in city simulation: we don't simulate "a city"; we model roads, lights, vehicles, and drivers, and the city's behavior emerges from their interaction. AGI may have the same nature.

# 9. A small taste of the idea

We can build a very simple Python agent that demonstrates a few concepts: Memory + Planning + Tool + Execution. This is not AGI — just a small educational model of the architecture:

mini_agent.py — python3

from dataclasses import dataclass, field
from typing import Callable
 
@dataclass
class Memory:
    facts: list[str] = field(default_factory=list)
 
    def remember(self, fact: str) -> None:
        self.facts.append(fact)
 
    def recall(self, keyword: str) -> list[str]:
        return [f for f in self.facts if keyword.lower() in f.lower()]
 
@dataclass
class Tool:
    name: str
    function: Callable[[str], str]
 
class Agent:
    def __init__(self):
        self.memory = Memory()
        self.tools: dict[str, Tool] = {}
 
    def plan(self, goal: str) -> list[str]:
        # a real planner would reason, not keyword-match
        if "calculate" in goal.lower():
            return ["calculator"]
        return ["reason"]

Security warning: The classic version of this pattern uses eval() for the simple calculator. Never run eval() on untrusted input — even with character filtering, it can be bypassed. In real code, use ast.literal_eval or a safe math parser.

This little system combines memory, planning, tools, and execution — but it's still very far from AGI: no general reasoning, no world model, no robust learning, no multimodal understanding, no long-horizon planning, no self-correction.

# 10. The "jagged intelligence" problem

One of the most interesting traits of current systems: they may brilliantly solve a super-hard problem yet fail at a simple multi-step one. Recent research calls this jagged intelligence — a very uneven capability profile:

jagged_profile.txt

Chart values are illustrative: These bars show the general shape of "jaggedness," not real measurements of any specific system. The conceptual point: Very Capable AI ≠ Automatically AGI — high capability in a few domains doesn't by itself mean generality.

So part of today's disagreement about AGI isn't about capability at all — it's about definition. With a hard definition (learn any new problem, adapt to unfamiliar environments, run long tasks independently, act reliably in the physical world) we're not there yet. With a simpler one (high performance across a very wide range of cognitive tasks) some current systems are much closer. Two researchers can look at the exact same system, and one says AGI while the other says not — because one weighs Performance, another Generality, a third Autonomy.

# 11. Maybe this loop is the heart of AGI

To compress the whole article into one cycle: a typical model is Input → Output; a more general agent runs this loop:

agi_loop.txt

And perhaps what we call "generality" shows up more in this loop than in model size. The road ahead may not be just 7B → 70B → 700B, but: from building bigger models to building better systems — better memory, better planning, better tools, better verification, better learning.

# 12. Summary: maybe AGI isn't a brain — it's a system

We opened by asking: if we wanted to build AGI, what would go inside it? The answer is no longer just "a very large model." In this conceptual picture: the model is the brain; memory is experience; perception is the senses; tools are the hands; the planner picks the route; the world model thinks about probable futures; verification blocks some mistakes; and safety defines how far the system may act. (These are metaphors — not a claim that human minds and AI systems share an architecture.)

Have we already built AGI? There's no definitive answer. Many of the parts are real and progress is fast, but significant limits remain in long tasks, novel environments, hallucination, and physical interaction. We may not discover AGI in one moment; it may be a spectrum we gradually move along. And the harder question than "can we build intelligent machines?" might be: how do we turn these parts into a general mind?

takeaway.txt

              AGI may not be a bigger model — it may be a system.

              We already have many of the parts —

              but having parts ≠ having general intelligence.

              The real challenge is building a system that wires all of them

              into a single loop.