What AI agents actually do: the Claude Agent SDK and MCP in plain English

An AI agent is software that uses a language model to work towards a goal by taking real actions, not just producing text. Give it a goal and it decides the steps, uses your tools, and checks its own work. The Claude Agent SDK is Anthropic's toolkit for building these agents. MCP, the Model Context Protocol, is the standard that connects them to your systems. A chatbot answers a question; an agent does the task.

What is an AI agent, in plain English?

Picture the difference between an assistant who answers your questions and one who handles the job. Ask the first where a delivery is and they tell you. Ask the second and they check the courier, email the customer, update the order and let you know it is done. An AI agent is the second kind.

Under the surface, an agent is a language model given three things it does not have on its own: a goal that persists across many steps, tools it can use to affect the real world, and the ability to check what happened and try again. The model supplies the reasoning. The agent structure supplies the memory, the hands and the feedback loop. That combination is what people mean by agentic AI, and it is why the category is growing so fast. Gartner predicted in 2024 that 33 percent of enterprise software applications will include agentic AI by 2028, up from less than 1 percent in 2024.

The plain test is this. If the software only produces words for you to act on, it is a chatbot or a copilot. If it takes the action, within limits you set, it is an agent.

What is the Claude Agent SDK?

The Claude Agent SDK is a toolkit from Anthropic that developers use to build agents on top of Claude. An SDK, a software development kit, is a set of ready made building blocks so you are not writing the hard parts from scratch.

Building a reliable agent means solving the same problems every time: keeping a goal in mind across dozens of steps, deciding which tool to use and when, remembering what has already happened, and stopping cleanly when a task is done or something looks wrong. The SDK handles those parts. It also carries the controls that matter for a business: permissions that limit what the agent can touch, and logging so every action leaves a record.

That last point is why we build on the SDK rather than stitching together a loose script. A script that quietly does things to your CRM is a liability. An agent built on a proper framework does the same work but behaves predictably and can be audited afterwards. When Teylu takes on custom AI agent development, this is the foundation the work sits on.

What is MCP, the Model Context Protocol?

An agent is only useful if it can reach your tools and data. MCP is how it does that.

MCP, the Model Context Protocol, is an open standard introduced by Anthropic in November 2024. It gives any AI model a common way to connect to external systems: a CRM, an ERP, a database, a Google Drive, a payments platform. Think of it as a universal plug. Before it existed, connecting a model to five systems meant building and maintaining five bespoke integrations. With MCP, you connect each system once and any compatible agent can read records and take permitted actions through that link.

The standard has spread quickly. OpenAI and Google DeepMind both adopted it, and in December 2025 Anthropic donated MCP to the Agentic AI Foundation, a fund under the Linux Foundation, so it is now stewarded as shared industry infrastructure rather than one company's project. For a business, the practical effect is that the connections you build are not tied to a single vendor. That is the same standard doing the heavy lifting behind AI integration with CRM, ERP and ecommerce, where the model reads and acts on the records already sitting in your systems.

How do agents, the SDK and MCP fit together?

Three plain roles, one system. The model does the thinking. The Claude Agent SDK gives that thinking structure, memory and controls, turning a clever responder into something that can hold a task. MCP gives it reach, the standard connections into the tools where your work actually lives.

A short worked example. You want an agent that keeps your sales pipeline honest. The SDK holds the goal, review every open deal and flag the ones going cold. MCP connects it to the CRM so it can read each deal's history. The model judges which look at risk and drafts a nudge for each. Permissions mean it can queue those drafts but not send them without a human tap. Logging means you can see exactly what it looked at and why. Take any one piece away and it breaks: no SDK and it forgets the goal halfway, no MCP and it cannot see the deals, no model and there is no judgement at all.

What can an AI agent actually do for a B2B business?

Useful agents tend to be narrow and unglamorous, which is the point. A few we build and see working:

A pipeline agent reviews every open opportunity overnight, flags the ones that have gone quiet against their usual rhythm, and drafts a tailored follow up for each, ready for the owner to approve in the morning. A research agent takes a target account, pulls what is public, checks it against your CRM history, and writes a one page brief before a call. A reporting agent assembles the weekly numbers from several systems, writes the plain English summary of what moved and why, and drops it where the team will read it.

None of these replace a person. Each removes the twenty to forty minutes of assembly that comes before the actual judgement: gathering context, drafting the boring first version, chasing the numbers across tools. That is also where an agent becomes the engine behind AI workflow automation for marketing operations, and where it earns back attention that was being spent on preparation rather than decisions.

Where do AI agents go wrong?

The honest answer matters more than the sales pitch. Gartner predicted in June 2025 that over 40 percent of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value and weak risk controls. The technology is real; most of the failures are not technical.

Three patterns cause most of the trouble. Aiming too wide, where an agent is asked to run a whole function instead of one well defined job, so it is impossible to trust or measure. No human checkpoint, where the agent acts without approval on things that carry real consequences, and one bad call erodes confidence in the whole idea. And weak grounding, where the agent reasons over messy or out of date data and produces confident nonsense. That last one is why a generative AI knowledge base and brand RAG setup often sits alongside agent work: it gives the agent a clean, current source to draw on rather than guessing.

The human layer is what makes the automation safe to run and trusted enough to keep. Designing it out is how these projects join the 40 percent that get cancelled.

How should a B2B business start with AI agents?

Start with one task, not a strategy. Pick a job that is repetitive, rules based, drags on your best people, and where a human can review the output before it counts. The overnight pipeline review is a good first candidate. So is call preparation, or the weekly report.

Then build it properly: on the Claude Agent SDK so it is controlled and auditable, connected through MCP so it reaches the right tools, with a human approving anything that carries weight. Prove it earns its place on that one job, measure the time it gives back, then widen. Our practical 2026 framework for implementing AI in a B2B business sets out the order to do this in, from readiness and governance through to the first agents in production. Done this way, an AI agent stops being a demo that impresses once and becomes a quiet piece of infrastructure that does a real job every day.

FAQ

What is an AI agent?
An AI agent is software that uses a language model to work towards a goal by taking real actions, not just producing text. Given an objective, it decides the steps, uses tools such as a CRM, a spreadsheet or a search, checks the result and adjusts. A chatbot answers a question. An agent does the task: it reads the records, drafts the follow up, updates the deal and reports back.

What is the Claude Agent SDK?
The Claude Agent SDK is a toolkit from Anthropic that developers use to build agents on top of Claude. It handles the parts every agent needs: holding a goal across many steps, calling tools, managing memory and context, and running safely with permissions and logging. It is the framework Teylu builds on so an agent behaves predictably and can be audited, rather than a one off script.

What is MCP, the Model Context Protocol?
MCP, the Model Context Protocol, is an open standard introduced by Anthropic in November 2024 that gives an AI model a common way to connect to external tools and data, such as a CRM, an ERP, a database or a Google Drive. Instead of a bespoke integration for every tool, you connect each system once through MCP and any compatible agent can then read and act through that link. In December 2025 Anthropic donated MCP to the Agentic AI Foundation, a Linux Foundation fund, so it is now stewarded as a shared industry standard.

How is an AI agent different from a chatbot?
A chatbot responds. You ask, it answers, and acting on the answer is your job. An agent is given a goal and carries out the steps to reach it, using tools and checking its own work along the way. The chatbot tells you which three accounts look at risk. The agent reviews every account, drafts a tailored follow up for each, and queues them for your approval.

Are AI agents safe to use with company data?
They can be, with the setup handled properly. Access runs through permissions, so an agent reads and changes only the fields you allow, and every action can be logged for audit. Processing can run in a UK region such as AWS Bedrock UK South to keep data in the country under the ICO. The EU AI Act adds governance duties, with full high-risk enforcement from 2 August 2026 and penalties up to 35 million euro or 7 percent of global turnover, so an AI inventory, a named owner and documentation matter from day one.