Problems with Autonomous Agent Loops

To solve the rigidity of chains, developers moved to Autonomous Agents (like AutoGPT or BabyAGI).

The Basic Agent Loop

The core idea is a while(true) loop:

1. Think: What should I do next?
2. Act: Execute a tool.
3. Observe: See the result.
4. Loop: Go back to step 1.

graph TD
    Think --> Act
    Act --> Observe
    Observe --> Think

The "While True" Trap

While flexible, this architecture is chaotic.

1. The Infinite Loop

If an agent gets an error from a tool (e.g., "File not found"), it might decide to try again. And again. And again. Without explicit control, an agent can burn through your entire API budget in minutes, obsessed with solving a solvable problem.

2. Loss of Focus

As the conversation history grows (the context window fills up), the agent often "forgets" the original goal. It might start optimizing for a sub-task and never return to the main objective.

3. Debugging Nightmares

If an agent fails after 50 steps, how do you debug it? Was the mistake in step 4 or step 49? Autonomous loops are notoriously hard to inspect and fix.