AI Engineer Conference: AI learns to work, focusing on 'harness' and 'evals'
·2 min read·Intermediate
“
Artificial intelligence is finally stepping down from its research pedestal. Conferences now discuss less about magical models and more about how to make them actually work, in the real world.
In 30 seconds
01The AI Engineer conference in San Francisco highlighted two major trends: 'harness engineering' and 'evals'.
02
→
💡
What this means for you
For us users, all this means AI will become more reliable and less prone to making things up. The apps we use will be more useful, not just talk, because AI will actually learn to work.
Thought slapping 'AI' next to a company name guaranteed its stock would soar? Well, the market had a bitter surprise this year.
·1 min·2·Beginner
'Harness engineering' is the art of connecting AI models to external tools to make them useful and reliable in apps.
03'Evals' are quality tests for AI systems, crucial for measuring performance and safety, especially with RAG and agents.
0101
What is this 'harness engineering' about?
It's how AI models stop being just chatty companions and start doing useful things in the real world. Imagine connecting a digital brain to mechanical hands, making it interact with databases or APIs, without messing things up. It's about making AI a true colleague, not just a theoretical consultant.
The AI Engineer conference in San Francisco saw strong interest in how companies integrate AI with existing external systems. It's no longer enough for a model to generate perfect text; it must also know how to call an API to book a flight or update a customer database. It's the difference between a good speaker and someone who also knows how to use a drill.
This "harness engineering" aims to make AI reliable and safe. If your model needs to make financial decisions, it can't just "guess"; it needs a workflow that minimizes errors and ensures consistency. It's the shift from experiment to product, with all the necessary checks. Who wants an AI that answers, “I don't know, try asking Google”? Exactly, no one.
📬 Enjoying this article?
Get the best AI news every week, straight to your inbox.
0202
Why is everyone talking about 'evals' (evaluations)?
"Evals" are AI quality tests, a bit like the checks before putting a car on the market. They help understand if an AI system actually does what it promises, without making things up or causing harm. It's the moment when hype clashes with reality, and you see who really did their homework.
During the AI Engineer conference, it became clear that evaluations are crucial, especially for complex systems like those using (Retrieval Augmented Generation) or AI agents. These systems must retrieve information from external sources and then use it to respond. "Evals" verify that the AI not only finds the right information but also uses it in a sensible and accurate way.
The challenge is moving from manual, lengthy, and expensive evaluations to automated, robust systems. Imagine having to manually check every single step of an : a nightmare. That's why the industry is pushing for standardized tools and metrics. Want to know if your AI is a genius or a fraud? "Evals" give you the grade, no discounts.
While the tech world was buzzing about OpenAI, Anthropic made its move. They just dropped Opus 5, a model they claim is almost as good as their legendary Fable 5.
NUOVO
