The Agentic Loop: A Practical Field Guide
Stop prompting agents. Start designing loops.
Most people use AI agents as chatbots. They type a prompt and wait for a result. That is a one-time task. It is not an agent.
An agentic loop is different. It is a system that does work, checks the result, and decides whether to continue.
A task without a check is just hope.
The craft of loop engineering moves your effort from typing prompts to designing systems. Instead of manual input, you build a cycle that runs on a schedule or against a goal.
How a loop works:
- Observe: The agent reads files or runs a test.
- Act: The agent makes one small, reversible change.
- Check: The agent runs a fixed test to see if it worked.
- Decide: The agent chooses to continue, stop because it succeeded, or stop because it is blocked.
The most important rule: Use a separate model to check the work. The model that writes the code is too generous when grading itself. You need a maker-checker split. One agent acts, and a different agent verifies.
Build your loops with these five blocks:
- Trigger: When does the loop start?
- Inputs: What fresh state does the agent inspect?
- Action: What single, bounded change will it make?
- Check: What fixed test decides success?
- Stop: When does it stop for success, budget, or failure?
If you miss a block, your loop will drift or run forever.
To keep loops safe, you must set three hard stops:
- Max iteration count: A ceiling on the number of turns.
- No-progress detection: If the agent makes no measurable change, stop it.
- Budget ceiling: A hard limit on tokens or dollars.
The bottleneck in software is no longer writing code. It is specifying the goal and the check clearly enough that an agent can run without you.
Build the loop. But stay the engineer.
Source: https://dev.to/truongpx396/the-agentic-loop-a-practical-field-guide-mnc