r/AISystemsEngineering

I'm a senior PM (6+ years, with an earlier developer background) and I have a case study interview next week for an AI Platform PM role on a shared AI infrastructure team (model serving, inference, GPU capacity). The case is about prioritizing under a tight engineering capacity constraint, and outlining my reasoning and trade-offs.

I've written up my answer (about 3 pages). Has anyone here worked as a Platform PM on AI inference or GPU platforms? A DevOps or MLOps background would be even better.

I'm not looking for a rewrite. I'd like someone with hands-on experience to read it and tell me where my reasoning has gaps, or where an infrastructure engineer would push back.

It should take 20 to 30 minutes. If you're open to it, comment or DM me and I'll send the case and my write-up.

Hello everyone i hope you’re doing great.
So basically i am interested in becoming an ai system engineer… i have a totally different background i am in the medical field and i have two businesses not related. Could anyone please guide me or walk me through a small brief of what should i do or the steps that i should be taking to become an ai system engineer. I know it’s the future. Like should i start by learning programming ? Languages( ex python ..) ?

I've been working on my own approach to this, and something keeps bothering me. Why are we still so constrained by context windows when we have all these other ways to store, retrieve, and manage information? I'm trying to build a system that adapts its working memory to what a task actually needs, moving relevant information in and out of context as needed instead of treating the context limit as the thing everything has to work around. I know RAG, summarization, compaction, and external memory exist. I'm not saying nobody is working on this. I'm wondering why there don't seem to be more mature systems that tie all of this together, especially for agents working on long-running coding tasks. Am I missing some obvious projects? Is this just a much harder problem than it looks, or are there other reasons this hasn't progressed further? I'm already building my own version, so I'd genuinely like to know what's out there before I reinvent something that's already been done. Also interested in hearing from people who've actually tried building this stuff, because I'm sure there are problems I haven't run into yet.

I’m a software engineer currently working on production AI agent/workflow systems. I already have a strong software/system-design background, so I’m not looking for general system-design books, basic ML textbooks, or broad introductions to LLMs.

I want to go deep specifically on agentic AI architecture, especially:

• Agent planning and task decomposition
• ReAct / reflection / replanning loops
• Tool/function calling and tool selection
• Designing good tools/interfaces for agents
• Planner–executor architectures
• Agent loops: plan → act → observe → replan
• Workflow vs autonomous-agent design
• Orchestration and routing
• Supervisor/worker and coordinator/delegator patterns
• Multi-agent communication and delegation
• State, memory and context in agent workflows
• MCP / A2A where relevant
• Evaluation and debugging of multi-step agent behavior
• Failure recovery, stopping conditions and guardrails

I care much more about depth and syllabus relevance than popularity or Amazon ratings. A newer or less-known book is completely fine if it genuinely covers these topics well.

Some books I’ve come across:

• Agentic Design Patterns — Antonio Gulli
• AI Agents in Action, 2nd Edition — Micheal Lanham
• AI Agents: The Definitive Guide — Nicole Koenigstein
• Building the Agentic Enterprise — Jaroslav Pantsjoha
• Building Agentic AI Systems — Anjanava Biswas & Wrick Talukdar
• Systems Thinking for Agentic AI

Has anyone actually read any of these?

If you wanted to become really strong at designing production agent planning/tool-use/orchestration systems, which 1–3 books would you choose?

Also very interested in books I haven't listed. Papers/blogs/courses are welcome too if you think books haven't caught up with the field yet.