The AI Agent Onboarding Problem (The Real Version)

So you asked about AI agents and onboarding, and honestly? We screwed this up for like three months before we figured it out.

Here’s what was happening. Every time we’d spin up an agent - we were using a mix of Claude with function calling and some custom orchestration - we’d have to stuff the entire context into the system prompt. Our coding standards, the database schema, the API patterns, all of it.

And the context window? It was getting expensive. We’re talking like 50k tokens just for setup on every single task. The math doesn’t work when you’re doing this 20 times a day.

What Actually Broke #

The real problem wasn’t that agents “forgot” - obviously they’re stateless, right? The problem was we had no good way to inject relevant context without… um, without just dumping everything in every time.

We tried a few things that didn’t work. First, we thought we’d just use the conversation history. Terrible idea. You hit token limits fast, and old context pollutes new tasks.

Then we tried maintaining state in Redis between calls. That was better but still required us to manually decide what context each agent needed. Every. Single. Time.

The Thing That Actually Worked #

We ended up building a pretty simple RAG setup. Nothing fancy - just embedded our docs, standards, and past decisions into Pinecone. When an agent spins up, it queries for relevant context based on the task description.

So if the task is “add authentication to the user API,” it pulls our auth patterns, our API structure docs, our error handling examples. Maybe 5-8k tokens of highly relevant stuff instead of 50k tokens of everything.

Cut our setup from like 30 minutes of me writing context to under a minute of automated retrieval. And our token costs dropped by 60% or something.

The Part That’s Still Messy #

The hard part is keeping those embedded docs current. When we change an architectural decision, we need to update the knowledge base. We automated some of it with git hooks, but honestly, it still requires discipline.

And debugging? Man, when an agent does something weird, you have to figure out what context it pulled and whether that was wrong or incomplete. We added logging for every retrieval, which helps, but it’s not perfect.

Also, security was a thing. These agents are pulling from our internal docs. We had to be really careful about what goes in there. No credentials, no customer data, nothing sensitive. Just patterns and standards.

What I’d Do Different #

If I started over, I’d probably use LangChain or LlamaIndex instead of rolling our own. We reinvented some wheels. But honestly, understanding how it works under the hood was valuable.

The other thing - and this is important - start with ONE agent doing ONE thing really well. We tried to solve it for five agents at once. Disaster. Get the onboarding flow right for one use case, then expand.

Oh, and write tests for your agent outputs. Seriously. We were validating manually for way too long.

The Bottom Line #

AI agents are only fast if you’re not bottlenecking them with manual context setup. You need automated, relevant context injection. RAG is probably your best bet unless you’ve got something better.

But yeah, it takes infrastructure work. Boring stuff. Documentation, embedding pipelines, retrieval logic, monitoring.

Most teams skip that and wonder why their agents aren’t delivering.

Anyway, that’s what we learned. Still iterating on it, but it’s way better than where we started.

What are you running into with your agents?