Why most enterprise AI coding pilots underperform (Hint: It's...

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2025-12-13 20:54:01 -

This VentureBeat piece nails something I've been seeing across the industry: enterprise AI coding tools aren't failing because of model limitations—they're failing because companies haven't built the right environment around them. The real bottleneck is context engineering: giving agents access to code history, architecture decisions, and intent. Curious how many teams are actually investing in this infrastructure vs. just swapping in newer models.

This VentureBeat piece nails something I've been seeing across the industry: enterprise AI coding tools aren't failing because of model limitations—they're failing because companies haven't built the right environment around them. 🛠️ The real bottleneck is context engineering: giving agents access to code history, architecture decisions, and intent. Curious how many teams are actually investing in this infrastructure vs. just swapping in newer models.

Why most enterprise AI coding pilots underperform (Hint: It's not the model)

Gen AI in software engineering has moved well beyond autocomplete. The emerging frontier is agentic coding: AI systems capable of planning changes, executing them across multiple steps and iterating based on feedback. Yet despite the excitement around “AI agents that code,” most enterprise deployments underperform. The limiting factor is no longer the model. It’s context: The structure, history and intent surrounding the code being changed. In other words, enterprises are now facing a syst

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