AI Agent Protocols

When building AI Agents, either for personal or enterprise use, there are tons of tools that an AI Agent developer can choose from. The tooling spans from Agent Development Kits, AI Agent SDK's, or just using Application Programming Interface to connect with AI providers. Choosing the right development tool is a task that is largely dependent on your AI Agent flow objectives and your use case. If a particular tool allows you to achieve your objective, then you can objectively select your AI Agent development tool.Your AI Agent workflow objective largely influences the design of your AI agent due to the fact that its capabilities and interaction layer are largely governed by AI Agent protocols, which are defined as a standard way in which AI Agents communicate with external tools, data sources, and each other. They enable AI Agents to work together, be scalable, and enhance their ability to tackle real-life complex problems. The protocols a development tool can help me have access to will be your development tool selection since they will shape how my AI Agent gets to achieve its objective, i.e., having access to the model context protocol, which is assumed to be a protocol used by the majority of AI Agents, will provide my AI agent with access to tools and external data sources.Building Enterprise Grade AI AgentsThe question of an enterprise-grade AI Agent comes when we want to build an Agent that has to accomplish an industry complex task, such as an Agent that performs Inventory Accounting in terms of International Accounting Standards 2. What we need in this case is an Agent that can manage and evaluate inventory. The agent will need to know how an item is brought into the organization (Inventory Acquisition Policies). We have to come to terms with the fact that different organizations have different workflows and policies in which they can source inventory items; others buy from other countries using a different currency and different tax rules applying to the transaction, as opposed to an organization sourcing the inventory locally. Therefore, such a goal reveals to us that there is no blanket approach when building AI Agents; creativity and domain restrictions are important when designing and building AI Agents. An approach that a particular organization may use to build an AI Agent is different from what another organization might do when building its own custom AI Agent. Thus, critical and systems thinking are fundamental when designing custom agents.Another factor that we mustn’t ignore is the fact that AI Agents are more efficient as to the quality of data that they have access to, you need to ensure that your Agent has access to enterprise grade data which will enable the agent with capabilities to make decisions, for instance when creating an Accounting AI agent it needs to have access to your transactions data, customers data, suppliers, inventory, business and accounting policies which can be done using a RAG and other financial items data, the high the quality of the data will aid the AI Agent into making financial accurate accounting decision.Making Use Of Agent ProtocolsWe have non-rebuttable evidence when developing software that using an already built solution can fast-track the development of a new system, which can not go unsaid with the use of AI Agents; different Agent protocols help AI agents to communicate with one another and modify their behaviour, providing them with vast capabilities. The most famous protocol is the Model Context Protocol which standardizes context acquisition and provides the agent with access to external data and tooling capabilities, different organizations and developers have development MCP servers, that can be connected with your AI Agent to provide it with more capabilities, such as an MCP server that can connect to the browser and automate browsing, or one that can connect to slack and manage slack channels. Thus, before you go on to develop an MCP server for your agent, it is advisable to browse around and check the available community servers that can enhance your development. Another benefit is that those servers are maintained by a different team, meaning when there are changes or updates for that particular technology, the developers are going to make that update.Another protocol that an agent developer can utilize is the Agent 2 Agent and Agent Network protocol, which facilitates how agents communicate, collaborate, and discover one another from different providers. Each A2A agent publishes an Agent Card at a well-known URL (/.well-known/agent-card.json) that describes its name, capabilities, and endpoint. Imagine when you are building a multi-agent layered system, you can create a specialized agent that can focus on a particular task, and it can get called whenever the other agent requires its capabilities. Thus, this creates a desire and a need for an individual to break task from tooling task or agent task so that a person might not try to build a tool for a task that needs an agent, for instance, think of tooling as a function that performs one task and performs it good i.e Queries or creates data to the database and does it good.Provided, an Agent task happens when a particular workflow has to be completed first before a certain workflow can be executed, having workflow triggers and event broadcasters can be a good addition to an agentic system, since when a dependent task is finished, the agent needs to know that its now has access to updated information or updated flow that it can execute the work.In the case where the inventory management agent need to discover suppliers and get three different quotations according to the company policy, and placing orders might need an API connection with different vendors, instead of building custom integrations for every vendor, the Universal Commerce Protocol will standardize the shopping life cycle into a modular capabilities through strongly typed request and response schemas across the underlying transport. If we are purchasing from different suppliers its different checkout flows, regardless of the method of connection that has been established.Agent 2 UI, this is basically utilized when you want to use User Interface components within your generative AI. It offers about 18 safe component primitives such as rows, text fields, and columns.The ecosystem of existing protocols is vast, and knowing when and how to use these protocols will help you to build efficient AI Agents that accomplish tasks at hand. There’s also a protocol to handle payments, such as Agent Payment Protocol which adds the typed mandates which provides proof of intent and enforces guardrails for every transation, UCP handles the items ordered and from which vendor where they sourced then AP2 handles the purchase approval and it provides transaction audit trail, usually this two have to be kept together for an extra layer of security.