"The promise of agentic AI is unmistakable. What enterprises are struggling with is the move from controlled pilots to real production environments that must operate within the constraints of their current systems. Many proprietary agentic platforms remain optimized for "green field" use cases, making them poorly matched to the complex mix of legacy data assets and workloads that are prevalent in enterprise environments."
"For agentic AI to deliver real enterprise value, it must operate within existing operational, reliability, and performance constraints. For example, an agentic system that can't talk to Java systems - the lingua franca of enterprise computing - is effectively cut off from the most critical operational data, workflows, and decision-making contexts. Forcing enterprises to adopt a parallel, Python-based infrastructure in order to deploy AI systems will delay adoption and significantly increase security, performance, and scalability risk."
As 2026 approaches, open source will be shaped by trends in governance, sustainability, and collaborative innovation. Emerging forces from 2025 are poised to influence the broader technology industry and sectors that depend on open source. Agentic AI deployments are accelerating, and enterprises will increasingly prefer open source AI tooling that integrates agentic workflows with existing applications and data. Proprietary agentic platforms often target greenfield use cases and fail to fit complex mixes of legacy data assets and workloads. Real enterprise value requires agentic systems to operate within operational, reliability, and performance constraints and to interoperate with enterprise languages like Java to avoid security, performance, and scalability risks.
Read at App Developer Magazine
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