When I talk about scale here, I’m not talking about handling more traffic. I’m talking about letting AI do the work for you without a human eye on every single output.
Let’s join in.
Start with the problem, not the technology
You have some form of throughput problem. Maybe you’re in construction, and you need to process hundreds of requests for proposals every day. Maybe you’re a creator, and you need to send more work to your clients. Maybe you’re a consultant trying to produce what your superiors know.
Whatever it is, AI enables the work, not the work itself.
If you don’t have a problem to solve, of course, it’s fun to talk about. You won’t get to the point where you’re actually deploying AI in production unless there’s a real problem running it.
The test to see if you are ready is very simple. Does adding AI help your organization operate with a greater level of trust without wasting your team trying to do the work as you already do running your business?
- If you’re putting extra burden on your team just to turn on the AI, that’s a problem.
- If your team doesn’t know how to work with it, that’s a problem.
- And if you don’t trust the output the same way you do other parts of your organization, that’s a big problem.
Why do AI agents keep breaking down in production?
78% of enterprises have AI agent pilots running. Only 14% have successfully made an ascent. The difference is not a model problem. It’s engineering (and it’s hiding in plain sight….)
The Adoption Journey (And the Trap Waiting at the End)
You have a throughput problem. You’ve decided that your data needs to stay nearby. You are now on the journey to AI adoption.
It usually starts with initial excitement because AI is really powerful. It’s amazing what you can achieve with a few API calls in LLM. Faster prototyping, mostly correct answers, demonstrably correct output. You look at it and think, “That’s too close.”
Then you start shuffling. The answer is not absolutely correct. It’s adding things to the reactions that you didn’t want. There are things missing from specific documents. so you come to the prompt Engineering. You write thousands of different signs.
That’s why you need a more systematic approach. Now your engineering team is throwing new terms at you. We need a vector database. What is vector? I thought we just threw everything away llm. Well, no, you have to vectorize the stuff.
Now you need GraphRAG, or a citation graph, or a new set of tools to understand the semantic relationships in your documents.
And this is where the trap closes.
You saw the potential. You wanted production-grade AI. And now your team is spending most of its time here creation of ai Infrastructure When your business isn’t about building AI infrastructure. Your business is about solving that basic throughput problem.
You really need three things: context, control, and confidence.
When you’re trying to achieve production-grade AI, three things matter almost more than anything else:
Context
💡
The context is the data you are feeding into the system. How do you understand what data you are connecting to? How is that data being applied to AI? In your output, is your data actually driving them? And can you change the data to change the output?
There’s a practice that goes hand in hand with accelerated engineering called context engineering, which is preparing your data so that it’s ready for AI. You probably have different types of documents in different stores. Relational databases, unstructured documents, CSV. They all need to be looked at differently.
It’s not just about vector databases, or just about GraphRAG, or just about one approach.
You have to think carefully about your data because if you try to do all this at runtime, you’re asking technology To do a lot of work very, very fast. It’s going to miss things. You need to guide it.
Control
You need an orchestration layer. In this current moment, everyone is talking about agent-to-agent, agent-to-agent. I will tell you one thing from my experience. a lot that is being said in full agentic Actually not. AI components are doing very specific things with the pipeline.
Think about it in legacy infrastructure terms. I came up with racking and stacking servers, and we cared about uptime. Four nines, five nines, whatever. If I have a server with four nines uptime and a network with four nines uptime, do I have four nines in total? No, the probability moves downwards.
the same logic applies agent. If you have one agent who is 95% confident and assigns the job to another agent who is 95% confident, you don’t get a 95% confident answer. You end up somewhat worse off.
So when you hear people talking about linking agents together in real-world production, you probably have AI doing a specific task very well as part of a larger workflow. The rest of the workflow is fine, being traditional code. Not all of these need to be AI-ified yet.
You also need to define:
- How your data gets into the workflow
- How labeling controls what AI sees on certain requests
- How rules and policies control flows
The simplest way to do this is through data labeling. If you do not want some information to appear in the output, do not present it in the LLM.
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Self-confidence
Confidence means measuring accuracy rather than assuming. Scoring outputs before expanding automation to everyone.
Confidence also has different meanings for different people. we talk nightmare Like they’re always bad. For Toffler, hallucinations are not bad. They’re red teaming. They want the AI to come up with wild, unpredictable scenarios to stress-test ideas.
