u/Cold-Cranberry4280

I’ve been living with an Always-on AI Agent for several months now, and for anyone about to build one - whether you’re a company or a builder - I thought I’d share a few non-obvious things (at least in my opinion) that I’ve learned (and am still learning) along the way.

Let’s start with what an Always-on AI Agent actually means:
An AI that doesn’t wait for prompts or commands - it runs continuously and makes decisions on its own (within the boundaries you’ve set). It “sniffs” what’s happening across the different things you’ve connected it to, alerts you or gathers data when needed, reaches out when it thinks it should, and can even respond on your behalf if you allow it. It’s your always-on partner.

Here are 10 things worth planning properly when building an AAA (Always-on AI Agent):

1. Memory is not a single system. The conversation you’re having right now or had yesterday, versus what the agent has learned about you and your domain over months - these are completely different types of data. They require different tagging, storage, decay, search, and retrieval strategies. Many systems don’t account for this and mix them together, which leads to agents that “forget.”
2. The context window is sensitive - even if it’s huge. Think of it as a budget that needs to be allocated wisely (how much goes to identity, relevant memory, current user state, attached documents, user request, etc.). Proper allocation (and not using 100% of it!) leads to a big jump in quality.
3. LLMs have attention issues - like my kids. They need structure. Think of it like moving apartments and loading a truck: the order and placement of things matter so everything fits, arrives, and unloads properly. There are tons of articles on context engineering, “lost in the middle,” etc.—read them and implement them. It will literally save you money and frustration.
4. Memory alone isn’t enough - you need Awareness. A 24/7 agent needs to know things the user never explicitly told it. A meeting got rescheduled, a deal got stuck, an urgent email hasn’t been answered for two days. And when building Awareness, do it efficiently—detection, retrieval, analysis, storage, and usage—otherwise you’ll start bleeding money and wake up to hundreds of dollars in charges after a few hours (ask me how I know).
5. Not all information in memory or Awareness is equal. A calendar is dynamic on an hourly (or faster) basis. Your business value proposition changes maybe every few weeks. Your kids’ names will never change. There’s zero reason to check everything at the same cadence - and when you do check, you want it to be efficient, not starting from scratch.
6. Your agent already has access to a lot of the people you communicate with - make sure to extract and use that, preferably without LLM calls when possible (it gets expensive).
7. The agent should know how to use the right model for the right task - not run everything on the same model. Structured background tasks can often run on weaker/cheaper models. I’ll share real numbers in a separate post.
8. An agent can work autonomously on a single goal over days, efficiently, without draining your wallet and without compromising on model quality - but first, you need to build solid infrastructure.
9. The hardest part of a proactive agent isn’t triggers or scheduling - it’s teaching it when to stay silent. The decision engine is 10x harder than the messaging logic itself.
10. “20 different agents, or one that truly knows me?” - I get asked this a lot. I have my own answer, but you should think carefully about what fits your use case before defaulting to what’s popular.

In the coming weeks, I’ll try to share more about some of these - some of them took me months to fully understand.