As AI becomes more deeply integrated into software development, one capability is becoming increasingly important: context. Generating code is useful, but understanding the surrounding application, requirements, architecture, and developer intent is what can turn an AI coding tool into something much more valuable.
That's why this perspective from Malcolm Adams, Founder and CEO of Avid Solutions International, stood out to me.
"The challenge wasn’t teaching AI how to reason. It was giving it the context to reason effectively. IBM Bob helped us bring that context into Elmer so intelligence could operate closer to where work actually happens."
That distinction is important. In complex enterprise environments, the model alone isn't the entire solution. AI also needs access to the right project knowledge, tools, constraints, and workflows to produce relevant results.
This is where context engineering becomes particularly interesting. Instead of asking how much intelligence a model has, teams can ask a more practical question: Does the AI have the right information to understand the problem we're actually trying to solve?
As agentic development evolves, providing better context may become just as important as choosing a more capable model.
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