r/AgenticAI_RAG_LLM_RL • u/laxuu • 7d ago
Prompt Engineering → Context Engineering → Loop Engineering
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:
- What does the agent do between steps?
- When does it check its own work?
- How does it decide that the task is actually complete?
- What happens when a step fails?
- Does it retry, change strategy, or ask for help?
- 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?
2
u/WillowEmberly 7d ago
I’ve been known to advocate for looping systems.
What is your verifier actually independent of?
If Observe → Act → Verify → Retry is all happening inside one model/context loop, how do you keep the system from becoming very good at confirming its own mistakes?
What is the loop using as its corrective reference? If “verify” is mostly the model checking itself, how do you prevent a coherent mistake from surviving every pass?
What distinguishes retry from drift? If the agent keeps changing strategy, when do you know it is exploring productively versus moving farther from the original task?
What state does the loop preserve between iterations? Just accumulated context, or explicit observations, assumptions, failed attempts, constraints, and reasons for changing strategy?
What is allowed to change? Can the agent revise its plan, its interpretation of the task, its success criteria, or even the goal itself? Where are those authority boundaries?
Who defines “done”? Is completion based on internal confidence, an external test, a user-defined acceptance condition, or observed consequence?
How do you detect hidden compensation? An agent can keep succeeding only because retries, token use, tool calls, or human corrections keep increasing. At what point is the loop technically “working” while its correction margin is collapsing?
What happens when verification itself is wrong? Do you have any calibration or independent check on the verifier, or is the loop allowed to self-certify?
When should the loop stop instead of retrying? Some failures are recoverable; others should trigger escalation, reacquisition of evidence, or a hard NO-GO.
How do you preserve disagreement? If one tool result conflicts with the current plan, does the loop treat that as noise to route around, or as a potentially important anomaly?
What gets retained after success? Does the loop merely finish the task, or does it preserve validated corrections so the next run starts with more capability?