𝗠𝗼𝘀𝘁 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗔𝗿𝗲 𝗝𝘂𝘀𝘁 𝗪𝗵𝗶𝗹𝗲 𝗟𝗼𝗼𝗽𝘀 𝗪𝗶𝘁𝗵 𝗩𝗶𝗯𝗲𝘀

𝗠𝗼𝘀𝘁 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗔𝗿𝗲 𝗝𝘂𝘀𝘁 𝗪𝗵𝗶𝗹𝗲 𝗟𝗼𝗼𝗽𝘀 𝗪𝗶𝘁𝗵 𝗩𝗶𝗯𝗲𝘀

I see new autonomous agent demos every week. They all follow the same pattern. A loop calls an LLM. It fails to check important data. It runs until your API budget runs out.

Most agentic workflows are simple loops. They rely on vibes instead of logic.

You likely see this code in many repositories:

while not done: result = call_llm(context) context = update_context(result) done = result.is_acceptable()

The function "is_acceptable" does too much work. It often relies on a hardcoded iteration limit. Sometimes it asks the LLM if it is finished. This is not an agent. It is a billing reminder with a ticking clock.

Token costs are rising. Many companies spend money on loops with no clear end. These loops retry the same prompt hoping for a better answer.

The hard part of building an agent is not the loop. The hard part is deciding when to stop.

A code agent needs to know what correct means. It needs to see passing tests and clean linting. If your exit strategy relies on an LLM declaring it is finished, you have a problem. You left your system design to a token predictor.

Real agents use verifiable exit conditions. They do not rely on self-assessment.

Look for these traits: • Programmatic validation of tool results. • Measurable external state changes. • Explicit claims instead of feelings.

An agent knows if it succeeded. A loop only knows when it runs out of retries.

Ask yourself these three questions before launching an agent: • Can I write a unit test for my exit condition without mocking the LLM? • Is my exit condition based on data instead of a model opinion? • Will this loop stop if the model gets stuck in a cycle?

If you cannot answer yes, you do not have an agent. You have an expensive loop.

It is easy to show a screen recording of a looping LLM to raise funds. Production is different. In production, your agent runs at 3 AM. It might eat your entire monthly budget because it stays stuck on iteration two.

The best agent code uses conditionals, validators, and state machines. The LLM is a small part inside those structures. The intelligence is in the ability to exit the loop.

What is the most creative exit condition you have built? Tell me how you avoid asking the model if it is done.

Source: https://dev.to/adioof/most-agentic-workflows-are-just-while-loops-with-vibes-155b

Optional learning community: https://t.me/GyaanSetuAi