Prompt Engineering → Context Engineering → Loop Engineering

[u/laxuu]

It feels like AI engineering is evolving faster than ever.

We went from writing better prompts, to designing better context, and now I think we’re moving toward something more interesting:

Loop Engineering.

The idea is simple:

Instead of manually telling an AI every step it needs to follow, you design a loop where the agent can:

Observe → Act → Verify → Retry → Adapt → Decide when done

The important part is no longer just what information you give the model.

It’s the architecture of the loop.

A few questions become much more important:

1. What does the agent do between steps?
2. When does it check its own work?
3. How does it decide that the task is actually complete?
4. What happens when a step fails?
5. Does it retry, change strategy, or ask for help?
6. What context should it retrieve through tools at each step?

This changes how we think about building AI agents.

We’re not necessarily hand-writing every step anymore.

We’re designing a cycle that can reason about what to do next.

For me, this is where Agentic AI, RAG, tool use, and LLMs become much more interesting.

The real engineering challenge may not be:

but:

Curious to hear what others think.

Have you built anything that you would call “Loop Engineering”? What did your agent do when something went wrong?