Articles Posted in Agentic AI

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As a stakeholder considering the implementation of an MCP connector, it may be difficult to glean from product documentation or marketing materials alone how the tool is functioning, and what must be done to manage its implementation.

MCP connectors provide a standardized way to connect AI models to external systems, allowing for easier proliferation of agentic AI. However, these connections also introduce new risks, including data access and privacy liability; unauthorized or erroneous actions; security vulnerabilities; accountability and governance; and third-party mismanagement.

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Before reading the first three installments of Pillsbury’s MCP connector series, you may have thought MCP-connected agentic architecture was too complicated to understand. But now that you have wrapped your mind around what MCP connectors are, what legal and operational risks they pose, and how to practically mitigate those risks, you may feel ready to deploy them in your organization. But not so fast…

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Providers have recently moved towards enabling AI agents to maintain persistent context and memory across interactions rather than treating each request as an isolated event. The environment makes it easier for enterprise AI systems to be designed to remember data and materials input and output from the tool.

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We all remember the first time we beheld the majestic power of generative AI. It plans vacations! It drafts my emails! It writes my essays! … then you accidentally include “Would you like me to soften the breakup message I drafted for you to be less confrontational?” in the text you send to your now ex- and highly offended partner, and you realize quickly the glaring limitation that a large language model (LLM) has on making you more productive. The model could give you the words, but it couldn’t act on them to fix your problems. And so, agents came along, which we thought would fix the inefficiency of copying and pasting a text response. But technically, these tools were hard to scale because every connection was custom-built, one at a time. Want Claude to talk to Slack? Build a custom bridge. Want ChatGPT to talk to Google Drive? Build another custom bridge. In reality, these tools weren’t scaling in the way we thought would drive efficiency. Your dreams of building an autonomous breakup robot were just not coming to fruition.

That is until Anthropic came up with a solution. Enter the Model Context Protocol (MCP), a standardized language that allows integration of LLMs into existing data source and application structures.

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A recent code leak indicated that OpenAI is set to release its first true AI Agent. An AI agent is a system designed to perceive its environment, process information, and autonomously take actions to achieve specific goals. Unlike traditional software that operates based on direct input and predefined instructions, AI agents can analyze situations, make decisions, and sometimes learn or adapt over time to better achieve its goals.

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