"Basic chatbots get much of the publicity associated with modern AI platforms, but they have limited use cases, providing a simple natural language interface to search tools. It's certainly useful, provided you implement fine-tuning and grounding in your own data, but it is still best thought of as an extension of existing search tools. AI has other uses: embedding the technology inside enterprise IT stacks, providing advanced filtering and summarization, using it for translation and voice recognition, and simplifying interactions through natural language."
"Agentic AI allows us to treat AI as orchestrated APIs, with data sources provided by Model Context Protocol (MCP) servers and agent-to-agent authentication and authorization via the under-development Agent2Agent (A2A) protocol. It's interesting to see how early adopters have begun to formalize using AI tools in their development tool chains. Last November LinkedIn unveiled its approach to a generative AI application stack, and now the company is building, testing, and monitoring agentic AI applications with a focus on longer interactions and workflows."
AI should be integrated as components within existing software development stacks rather than treated only as chatbots. Chatbots provide useful natural language interfaces but are limited and function best as extensions of search when fine-tuned and grounded in data. Enterprise use cases include embedding AI for filtering, summarization, translation, voice recognition, and simplified interaction. Agentic AI enables orchestration of models as APIs, with data supplied via Model Context Protocol (MCP) servers and agent-to-agent authentication via Agent2Agent (A2A). Early adopters are formalizing AI in development tool chains and applying messaging-based agent architectures for longer workflows.
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