The AI agent problem that’s holding back innovation

RECENT advances in large language models and agentic architectures have fundamentally transformed artificial intelligence (AI) capabilities. Today’s AI systems can plan multi-step tasks, reflect on their outputs, use tools and even coordinate with other AI systems.

We are witnessing the emergence of truly agentic AI – systems that operate with increasing autonomy and goal-directed behaviour rather than merely responding to prompts. However, as these impressive capabilities mature, a critical infrastructure gap threatens to undermine their potential at scale.

The problem is straightforward but profound: most AI agents today are confined to proprietary technological stacks. They rely on platform-specific memory stores, orchestration logic, toolchains and interaction schemas that work perfectly well in isolated environments, but create serious friction when deployed across organisational boundaries.