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In computer science, most problems eventually collapse into two primitives: searching and sorting. After years of building GTM systems, I’ve come to realize the same primitives drive how revenue teams discover opportunity and prioritize action. The mechanics haven’t changed — the environment has.

The next generation of AI agents doesn’t just query data - it reasons with it. Yet, most of today’s data layers are built for a world of filters, not meaning. While AI models have evolved to understand context, intent, and nuance, the underlying data systems still speak SQL - a language of rigid filters and static schemas. It’s like training a philosopher to think only in spreadsheets.

The dream of AI has always been simple: one-shot intelligence. You ask a question in natural language and instantly get meaningful insights from across the internet. No setup, no prompts, no friction. That sounds perfect in theory. But in practice, this is where most AI products fail. The problem is not the model. It is the translation gap between what users mean and what large language models understand.

Every product team faces the same tension: When do you double down on what works, and when do you bet on what’s next? In search algorithms, this tradeoff is called exploration vs. exploitation. At OpenFunnel, we use the same principle to guide our roadmap and balance focus with innovation.

Switching to usage-based pricing didn’t just change how customers pay. It changed how we build. Our customers don’t pay for API calls or execution time. They pay for results - which means every credit has to deliver measurable value. This shift forced us to rethink reliability, precision, and transparency across our entire stack.

AI agents aren’t just consuming data. They’re evaluating vendors. When agents compose workflows, they rely on objective performance metrics - latency, accuracy, and cost per outcome — to decide which tools to use. This marks a major shift from demo-driven selling to data-driven evaluation.

As large language models (LLMs) become increasingly sophisticated, a new discipline is emerging that goes far beyond traditional prompt engineering: context engineering. This evolving practice represents the art of providing comprehensive, multisourced context to enable AI systems to solve complex business problems with unprecedented accuracy and relevance. Context engineering embodies the maturation of AI from a tool that responds to instructions into a strategic partner that understands, reasons and acts within the full complexity of business environments through multiple input data sources, both external and internal.