AutoGPT and BabyAGI: The Birth of Agents

In April 2023, two projects captured everyone’s attention: AutoGPT and BabyAGI. They promised AI that could set goals and work toward them autonomously. The hype was intense—and instructive.

What Are Agents

Traditional LLM usage:

Human: Do X
AI: [Does X]
Human: Now do Y
AI: [Does Y]

Agent pattern:

Human: Achieve goal Z
AI: To achieve Z, I need to:
    1. Do X
    2. Analyze result
    3. Do Y based on analysis
    4. Continue until Z is achieved

The AI decides the steps.

AutoGPT

An “autonomous GPT-4 experiment”:

# Conceptual flow
goal = "Build a website for my business"

while not goal_achieved:
    thoughts = gpt4.think(context, memory, goal)
    action = gpt4.decide(thoughts)
    result = execute(action)  # web browse, write file, run code
    memory.add(result)
    goal_achieved = gpt4.evaluate(result, goal)

Capabilities

• Browse the web
• Write and execute code
• Manage files
• Interact with applications
• Remember context across sessions

Example Session

AutoGPT initialized with goal: "Research competitors for AI writing tools"

Step 1: Searching Google for "AI writing tools 2023"
Step 2: Found 10 results, extracting top 5
Step 3: Visiting jasper.ai to understand features
Step 4: Writing findings to research/jasper-analysis.md
Step 5: Visiting copy.ai...
[continues]
Final: Compiled competitor analysis report

BabyAGI

Simpler but elegant task management:

from collections import deque

task_list = deque()
objective = "Build an MVP of my startup idea"

# Seed task
task_list.append({"task_name": "Define core features"})

while task_list:
    # Pull task
    task = task_list.popleft()
    
    # Execute with GPT
    result = execute_task(task, objective)
    
    # Generate new tasks based on result
    new_tasks = generate_tasks(result, objective, task_list)
    task_list.extend(new_tasks)
    
    # Prioritize
    task_list = prioritize_tasks(task_list, objective)

The key insight: task creation as a tool for the AI itself.

The Architecture

                    ┌─────────────┐
                    │   Memory    │
                    │ (Vector DB) │
                    └──────┬──────┘
                           │
┌──────────┐    ┌─────────┴─────────┐    ┌──────────────┐
│  Tools   │◄───│    Agent Loop     │───►│   Actions    │
│• Browser │    │ 1. Think          │    │• Write file  │
│• Files   │    │ 2. Decide action  │    │• Run code    │
│• Code    │    │ 3. Execute        │    │• API calls   │
│• APIs    │    │ 4. Evaluate       │    │• Web search  │
└──────────┘    └───────────────────┘    └──────────────┘

The Reality Check

What Worked

• Simple, well-defined tasks
• Research and summarization
• Data gathering and compilation
• Proof-of-concept generation

What Didn’t

Goal: "Make me $10,000"
Result: Infinite loop of "researching business ideas"

Goal: "Fix the bug in my codebase"
Result: Made 10 different attempts, none correct, burned $20 in API calls

The Problems

1. Token costs: Each step burns tokens. Loops get expensive.
2. No grounding: Agents hallucinate actions and results.
3. Loop failures: Gets stuck in repetitive patterns.
4. Context limits: Forgets earlier work.
5. Unsafe actions: May take unintended actions.

What They Taught Us

Agents Need Constraints

# Too open
agent.goal = "Be creative"

# Better
agent.goal = "Generate 5 blog post outlines about Python testing"
agent.max_steps = 10
agent.allowed_tools = ["web_search", "write_file"]

Memory Is Critical

Without good memory:

Step 1: Research X
Step 2: Research X again (forgot step 1)
Step 3: Research X again

Vector databases became essential agent infrastructure.

Human-in-the-Loop

while not done:
    action = agent.decide()
    if action.is_dangerous():
        approved = human.review(action)
        if not approved:
            continue
    agent.execute(action)

Autonomy works better with supervision.

Building Agents Today

LangChain Agents

from langchain.agents import create_react_agent
from langchain.tools import Tool

tools = [
    Tool(name="Search", func=search_web, description="Search the internet"),
    Tool(name="Calculator", func=calculate, description="Do math"),
]

agent = create_react_agent(llm, tools, prompt)
result = agent.run("What is the population of France divided by 2?")

OpenAI Assistants

assistant = client.beta.assistants.create(
    name="Research Assistant",
    tools=[{"type": "retrieval"}, {"type": "code_interpreter"}],
    model="gpt-4-turbo"
)

# Create thread and run
thread = client.beta.threads.create()
message = client.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content="Analyze this data and create a visualization"
)
run = client.beta.threads.runs.create(
    thread_id=thread.id,
    assistant_id=assistant.id
)

When Agents Make Sense

Good Use Cases

• Research and summarization pipelines
• Code generation with test verification
• Multi-step data processing
• Workflow automation with checkpoints

Bad Use Cases

• Fully autonomous decision-making
• Unconstrained real-world actions
• Tasks requiring perfect accuracy
• Situations without clear success criteria

The Takeaway

AutoGPT and BabyAGI were important not because they solved the agent problem, but because they demonstrated:

1. LLMs can do multi-step reasoning
2. Tool use is the key unlock
3. Memory and context are hard problems
4. Full autonomy is still far away

The future is agents, but with guardrails.

Agents are the future. Just not the autonomous kind we imagined.