The hardest part of AI right now? Making the promise possible.
Walmart’s move toward super agents is one of the clearest examples of where this space is heading. Agents that don’t just answer questions, but take action. These aren’t JUST chatbots. They’re orchestrators: agents that talk to other agents, trigger workflows, and pull the right data at the right time to get real work done.
But you’ll notice something missing, details on how they’re actually doing it.
Everyone’s using the buzzwords, super agents, orchestration, real-time, action layers, but the tooling to make it all work takes work to build. Its not a data lake, and its definitely not plug-and-play.
In The AI Evolution, I point to data lakes as a foundational layer, and they are. But they’re built for reporting, not action. What agentic AI needs is a layer that’s both readable and executable, with access to real-time context and permissions.
If you’re taking with companies that aren’t saying this you’re building a huge data swamp, that won’t unlock the things that Walmart ays they have. The reality is that most teams are duct-taping workflows together with brittle APIs or pushing dashboards behind a chat interface and calling it an agent.
That’s the space I’m often finding our work at PerryLabs. Not just demoing agents, but building the underlying layers to actually deploy them, and for lots of companies the scaffolding just is not there yet.
