In this article, we try to explore the collective thinking into a smaller set of practices and explain the reasoning behind each one, rather than asking anyone to memorize a numbered list.
I write about Deep System design,Engineering, tech, how to build things in tech at a deeper level, AI/ML inference, Benchmarks, LLMs and How to Quants, please checkout some of my posts ( more than 300000 reads) -
built a few agents for my digital products business and the loop pattern here is spot on — the ones that survived production were the ones where I kept the model's decision surface as small as possible. every time I let the model decide "what to do next" instead of just "which of these 3 things to do" it broke within a week.
Treating the model as an intermittent decision engine within deterministic state machines is the only pattern that survives production traffic. The real architectural battle isn't expanding agent agency—it’s rigorously bounding execution paths and context windows to maintain predictable latency and eval determinism at scale.
Your deterministic control flow framing lands. The part left open is the gap between the model emitting a tool call and that call actually running. A policy layer outside the model that approves each call before it executes is the thread I would pull.
Personally, these practices highlight an important reality, production AI agents are less about giving models autonomy and more about constraining that autonomy safely, at least in my experience. I found the difficult engineering work is in context management, state, observability, evaluation, and reliable stopping conditions, not in the prompt alone.
Nice one. Great to connect with you.
I write about Deep System design,Engineering, tech, how to build things in tech at a deeper level, AI/ML inference, Benchmarks, LLMs and How to Quants, please checkout some of my posts ( more than 300000 reads) -
https://howtosystemdesigneverything.substack.com/
https://aiinferenceandoptimizations.substack.com/
https://howtobuildtech.substack.com/
[Bookmark: 300000 Reads Top System Design] Weekly Round Up : https://howtosystemdesigneverything.substack.com/p/extended-50-off-discount-top-system?r=14q3sp&utm_campaign=post-expanded-share&utm_medium=web
https://howtosystemdesigneverything.substack.com/p/important-bookmark-shortest-system?r=14q3sp&utm_campaign=post-expanded-share&utm_medium=web
https://howtobuildtech.substack.com/p/how-to-build-tech-2-how-to-actually-c43?r=14q3sp&utm_campaign=post-expanded-share&utm_medium=web
https://aiinferenceandoptimizations.substack.com/p/important-ai-inference-pulse-4-kv?r=14q3sp
built a few agents for my digital products business and the loop pattern here is spot on — the ones that survived production were the ones where I kept the model's decision surface as small as possible. every time I let the model decide "what to do next" instead of just "which of these 3 things to do" it broke within a week.
Treating the model as an intermittent decision engine within deterministic state machines is the only pattern that survives production traffic. The real architectural battle isn't expanding agent agency—it’s rigorously bounding execution paths and context windows to maintain predictable latency and eval determinism at scale.
Your deterministic control flow framing lands. The part left open is the gap between the model emitting a tool call and that call actually running. A policy layer outside the model that approves each call before it executes is the thread I would pull.
Personally, these practices highlight an important reality, production AI agents are less about giving models autonomy and more about constraining that autonomy safely, at least in my experience. I found the difficult engineering work is in context management, state, observability, evaluation, and reliable stopping conditions, not in the prompt alone.