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Agents: Shifting from How to What

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Sometimes, the greatest insights come from the least expected places.

I recently attended an AI event that, yes, had a bit of a quirky, superhero theme. It wasn't my usual style, but the content was a game-changer. It gave me a deep, practical understanding of what AI agents can truly do.

This event didn't just teach us about a new tool; it showed us a fundamentally new way of thinking.

What does the agent need?

For years, when building software, our main focus was the how. We spent all our energy defining the precise steps: how to write the workflow, how to program the logic, how to follow the process.

With agents, that question flips completely. Our focus shifts to: What data do we need to provide so that the agent can do it?

This is a massive change and similar to how we manage teams. We stop obsessing over the exact instructions and start obsessing over the quality of the context. We stop being process engineers and start being data engineers. We trust the agent to figure out the how, provided we give it the right what.

Socrates Advice: a data-centric startup

At Socrates Advice, we're a startup focused on helping teams master continuous improvement. We’re currently building our platform, and this new agent perspective is now at the heart of our architecture.

Our job is no longer to build a giant, complicated rule book. Our job is to build a platform that gathers the most valuable input possible, allowing the agent to perform the highest-level thinking.

We are implementing an agent layer that will gather a richer, multi-prong view of team performance:

  1. Behaviours: The things teams do
  2. Beliefs: The reasons why teams expose these behaviours
  3. Results: The final outcomes

This integrated view lets the agent connect the dots. It can see that a specific belief - like a fear of admitting mistakes - is causing a specific behaviour - like excessive rework - which directly hurts the results.

Better advice, built on context

This is the power of the shift. Instead of generic advice, the agent provides hyper-specific, contextual guidance. We move from telling a team to "be more organized" to the agent suggesting a precise experiment based on the data it's analyzing.

Our work has become simpler and more profound at the same time. We are focusing our energy on defining the right inputs and ensuring that the teams we serve get the best possible guidance. We’re excited to be shifting our focus to this new, data-centric model, empowering teams to truly master their journey to excellence.

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