Agentic AI: Context is Key for Production Success

Agentic AI deployment is entering production reality, but success hinges on providing these systems with proper context. The defining challenge isn't building the agents themselves. Instead, even capable AI systems falter without clear strategic grounding and proprietary organizational knowledge. Here's the thing: better context, not just better technology, is the missing ingredient. Vanessa Liu, chair at Appen Ltd., emphasizes that companies need to "give them the business context so that they are going to be able to run well." Data is crucial for companies to leverage AI effectively. What's interesting is that for agentic AI deployment, data acts as a proprietary moat. Frontier AI models are only as effective as the business context they receive. Steve Hasker, president and CEO of Thomson Reuters Corp., notes that companies with a clear customer problem and a defensible competitive moat will stand out. The bottom line: Agents, like their users, are impatient. Ariel Shulman, chief product officer at Bright Data Ltd., states that users' tolerance for delay has significantly collapsed. This means agents need to deliver information very quickly, often under one second, to provide useful answers before users lose patience. This focus on speed and context is vital for successful AI agent implementation.