u/ContextualNina

I’m hosting my first Reddit AMA soon with Max Marcon, Director of Product at MongoDB, along with Mikiko Bazeley, Staff Developer Advocate, and Yang Li, Senior SA. The AMA will focus on context engineering, RAG, agents, and what it takes to build production AI apps.

Disclosure: I also work at MongoDB. I’m posting because I want to bring useful, practitioner-level questions from this community into the AMA, since I’ve seen some related topics discussed here.

For people designing prompts and model workflows: how much of context engineering still involves the prompt, rather than shifting focus to retrieval, context compression, tool use, memory management, and other parts of the system around the model?

When you’re trying to improve model behavior, how do you decide whether the answer is to write a better prompt versus change what context the model receives, how that context is selected, or how the surrounding app/agent workflow is structured?

Would love to collect the sharpest questions and bring them into the AMA.

I’m hosting my first Reddit AMA soon with Max Marcon, Director of Product at MongoDB, along with Mikiko Bazeley, Staff Developer Advocate, and Yang Li, Senior SA. The AMA will focus on context engineering, RAG, agents, and what it takes to build production AI apps.

Disclosure: I work at MongoDB. I’m posting because I want to bring useful, practitioner-level questions from this community into the AMA, since I’ve seen a lot of related topics discussed here.

For people building RAG systems: what would you actually want answered?

Some areas I’m especially curious about:

• What context belongs in retrieval vs prompts vs tool calls vs memory?
• How are teams evaluating whether retrieved context is actually helping?
• How do you handle freshness, permissions, and metadata filtering?
• When does a general-purpose database/vector search setup work, and when do you need something more specialized?
• What breaks first when agents use RAG as a tool and move from prototype to production?

Would love to collect the sharpest questions and bring them into the AMA.

Hi r/ContextEngineering!

I’m Nina (u/ContextualNina), your friendly AMA moderator for next week, the inaugural AMA for this subreddit! I’m excited to introduce the three people who will be taking all of your questions for our upcoming AMA: Max Marcon (u/mmarcon), Mikiko Bazeley (u/mmbaze), and Yang Li (u/Ok-Amphibian6116). Between the three of them, they spend a lot of time working with teams building AI agent systems that need to hold up in production.

Ask them anything during a live AMA right here on Wednesday, July 8 from 12-1 PM ET (9-10 AM PT). The real tradeoffs, the messy parts, AI hype vs. reality - whatever you’ve got.

I invited this group because they work directly on the data layer for production AI agents, which gives them a pretty grounded view of where things get hard: context design, retrieval quality, memory, state, multi-step workflows, and the parts of agent systems that tend to fail outside of demos.

We’ll be answering questions about:

• Where context engineering ends and memory engineering begins
• What “context rot” looks like as context gets longer
• How to think about memory in multi-agent systems
• When RAG beats long context, and when it doesn’t
• The context mistakes that can quietly sink agent systems in production

You can start dropping in questions now ahead of time (they’ll answer them during the live window), or ask them live next Wednesday!

Full disclosure: I’m the founding mod of this subreddit, and I recently started at MongoDB. I thought this subreddit could benefit from chatting with some of my new colleagues.

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Surge AI recently released the Handbook benchmark, sharing here in case folks want to check it out. Sharing the links and the context they shared around it, and adding it to my weekend reading list :)

"We drop an agent into a live company environment with files (PDFs, Excel, Word Docs, ...), tools (email, Slack, Jira, calendar, ...), and a dense corporate handbook (up to 124 pages), across 5 enterprise domains.

The agent is given one instruction: follow the company rules.

HANDBOOK models the way enterprise employees have to adhere to company handbooks in their everday work, and every frontier model fails >75% of the time.

They fire employees without authorization.
They clear self-"approved" expenses.
They submit expired records to insurers.
...and then they report full compliance"

Initial thoughts: it's always exciting to see a new benchmark with so much room for agents to improve. However I've noticed that new benchmarks are saturated so quickly these days. I am betting on an >80% score within 2 months, let's round to September 1. Accepting (gentleman's) bets below.

Surge AI recently released the Handbook benchmark, sharing here in case folks want to check it out. Sharing the links and the context they shared around it, and adding it to my weekend reading list :)

"We drop an agent into a live company environment with files (PDFs, Excel, Word Docs, ...), tools (email, Slack, Jira, calendar, ...), and a dense corporate handbook (up to 124 pages), across 5 enterprise domains.

The agent is given one instruction: follow the company rules.

HANDBOOK models the way enterprise employees have to adhere to company handbooks in their everday work, and every frontier model fails >75% of the time.

They fire employees without authorization.
They clear self-"approved" expenses.
They submit expired records to insurers.
...and then they report full compliance"

Initial thoughts: it's always exciting to see a new benchmark with so much room for agents to improve. However I've noticed that new benchmarks are saturated so quickly these days. I am betting on an >80% score within 2 months, let's round to September 1. Accepting (gentleman's) bets below.

Hey everyone! I'm u/ContextualNina, a founding moderator of r/ContextEngineering.

This is our new home for all things related to context engineering. We're excited to have you join us!

What to Post
Post anything that you think the community would find interesting, helpful, or inspiring. Feel free to share your thoughts, photos, or questions about topics like optimization, architecture, memory, RAG, subagents, etc., or cool demos you've built.

Community Vibe
We're all about being friendly, constructive, and inclusive. Let's build a space where everyone feels comfortable sharing and connecting.

How to Get Started

1. Introduce yourself in the comments below.
2. Post something today! Even a simple question can spark a great conversation.
3. If you know someone who would love this community, invite them to join.

Thanks for being part of the very first wave. Together, let's make r/ContextEngineering amazing.