Agents, MCP and everything in between: a guide for marketers

You use ChatGPT daily. You’re refining your prompting techniques. Maybe you’ve even created a few custom GPTs. As marketers, the mandate (and desire) to use AI is exploding. From writing content to generating new brand imagery to running ad campaigns and everything in between, AI can supposedly do it all and is becoming more central to how we work.

Now, a new wave of AI innovation has entered the picture: agents. These tools don’t just respond. They reason, act, and work across systems. If you feel like you’ve been hearing the term everywhere, you’re not alone. But AI is changing fast, and keeping up with the most up-to-date knowledge can be challenging. No one wants to admit they don’t know what an agent really is, or what MCP stands for, or what RAG means, or how it is at all relevant. 

Plus, your job as a marketer isn’t to keep up with every AI breakthrough. But you are expected to figure out how AI can make your team more productive. That starts with understanding the current landscape. This guide breaks down concepts like AI agents, MCP, and related terms in practical terms, then explains why they matter.

What is an AI agent?

Most people think of AI as chatbots that respond to natural language. You ask a question, it gives an answer. Agents go a step further. They don’t just reply; they decide, reason, and act.

What makes agents different from traditional automation is their ability to make decisions in dynamic, context-rich environments. Automations are built to follow rigid rules: if X happens, do Y. They’re great for predictable, repetitive tasks. But they fall apart when the path isn’t clear or when flexibility is needed.

Agents, on the other hand, are designed to handle ambiguity. They interpret natural language inputs to understand what you’re asking, figure out the steps needed to get the job done, and then take those steps using connected tools and services. They don’t just do what they’re told — they figure out how to do it.

There are three core levels of understanding that an AI agent leverages:

• Understanding: The agent interprets natural language and grasps what you’re asking for — even if it’s vague, layered, or complex. Unlike automation, which relies on structured inputs, agents can navigate human nuance.
  
• Making decisions: Once the agent understands the task, it evaluates the steps required, selects the right tools, and plans how to complete the task. Traditional automation doesn’t decide — it simply follows preset rules.
  
• Taking action: Agents execute tasks across systems and tools — searching documents, sending messages, summarizing calls, or generating content — all based on their own plan. This is what enables agents to act like a true teammate, not just a trigger-response machine.
  

For example, suppose you want to re-engage old leads. Traditional automation might send a monthly email based on static rules. An agent, however, could review past call transcripts, tailor messaging to each lead’s concerns, and pull in relevant content from your site. It’s not following rules. It’s thinking through the task.

For agents to work with this kind of intelligence they require access to tools and data and the appropriate context. That’s where MCP (Model Context Protocol) comes in.

What is MCP?

MCP stands for Model Context Protocol. It’s a new standard that lets AI models connect to external tools and services — like Slack, your CMS, the internet for search, or a CRM — in a consistent and structured way.

Without tool calling, even the most advanced language model is limited to what it already knows. For example, if you asked a basic LLM what the weather is like in San Francisco today, it would have to guess — because it can’t access real-time data. But with tool calling, the agent understands that it needs external information, invokes a weather API (or web search), retrieves the current forecast, and uses that to answer your question. This ability to recognize gaps in its own knowledge and fill them by calling tools is what transforms a static model into a dynamic, capable agent.

MCP is a standardized way of doing this tool calling across models. Think of MCP like the HTTP of the agent world. Just as HTTP made the modern web possible by creating a shared way for browsers and websites to talk to each other, MCP does the same for agents and tools. It allows your agent to take action on your behalf without needing custom integrations every time.

So when an agent pulls a document from your CMS, searches the web, summarizes a sales call, or sends a message in Slack — that’s possible because of MCP. It provides the protocol that standardizes how AI models interact with external systems.

Before MCP, connecting AI to tools was messy: each model vendor (like OpenAI, Anthropic, Google) had different formats, different expectations, and required custom integrations. Developers had to rebuild functionality every time a new model came out.

With MCP, there’s one shared protocol. That means tool builders can create a connector once and have it work across models. And AI builders can focus on reasoning, orchestration, and user experience instead of integration glue.

For marketers, this changes the game: you can now plug AI into your existing stack (calls, content, CRM) without technical overhead — and get real results, fast.

Other AI terms you should know

MCP might be the linchpin protocol that powers agent intelligence, but it operates alongside several other key concepts that give agents their power. Understanding these terms helps demystify how agents work — and why they’re fundamentally different from simple automations.

Tool / Tool Calling

A tool is any external capability the agent can use — like a search engine, your company’s CMS, Slack, or a call summarizer. Tool calling is the process by which an agent invokes these capabilities in real time to complete tasks. Without tool calling, an agent is just a very articulate chatbot.

RAG (Retrieval-Augmented Generation)

RAG allows an agent to retrieve information — from documents, call transcripts, or the web — before answering a question. It grounds the agent’s output in real data instead of relying solely on its pre-trained knowledge. That means you can get answers based on your actual customer conversations, not just internet averages.

Context Window

This is the model’s working memory — the amount of information it can hold at once. The bigger the window, the more it can “see,” but it still needs to be selective. That’s why smart retrieval and tool use matters: the agent can pull in just the right context to complete the task effectively.

Hallucination

When a model makes something up, that’s a hallucination. Agents avoid this by using tools and RAG to ground outputs in verifiable sources. If your AI is confidently incorrect, it’s probably missing access to the right context.

Orchestration

This is the agent’s ability to not only choose the right tool, but to sequence actions and combine steps to complete more complex workflows. It’s what lets the agent behave more like a teammate than a script.

Together, these concepts give agents the ability to reason, plan, and act. Agents are the brain of the system, interpreting requests, reasoning what needs to happen and deciding how to accomplish a task. They use tool calling and RAG to interact with external knowledge and fetch real, up-to-date context. MCP is the connective tissue that enables those interactions, acting as the standard that lets agents plug into different tools and systems without custom work. Once those connections are in place, orchestration kicks in — the agent can combine tools, plan multi-step workflows, and adapt based on what it finds. And all of this is grounded in the realities of AI’s limitations: the context window determines how much the model can see at once, and the risk of hallucination reminds us why accuracy and grounding are so important. Taken together, these elements make agents not just possible — but trustworthy, flexible, and ready to support real work.

How MCP and agents change the game for marketers

AI agents mean a real shift for marketers because they enable context-aware automation, along with intelligent decision-making and collaboration between multiple systems and sources. What this means for the AI-enabled marketer is a shift from smart workflows and rigid systems to a world where you have a dynamic team of “workers” who can do heavy lifting for you.

Research at scale

Summarize what our top three competitors have changed on their websites in the past month. Compare it to our positioning. Then review how those competitors show up in customer calls. Recommend positioning tweaks, battlecard updates, and blog edits. Send me a report each week.”

Now possible because:

• Agent can access live web, calls, and positioning content docs all through different tools
• Agent is dynamic – it can find different calls to reference, identify different content to update, and make changes based on what it learned
• Agent can actually take action, evaluating websites, drafting updates, and emailing recommendations on a weekly basis
  

Unified customer understanding

“Every Monday, summarize activity across our top 50 accounts: sales calls, support issues, product use, open deals. Flag risks and upsell opportunities. Share in Slack.”

Now possible because:

• Agent can access multiple systems: CRM, call transcripts, product analytics, and support platforms
• Agent can identify relevant patterns and connect dots across tools
• Agent can proactively package and share insights in the channel where your team works

Voice of customer, on demand

“Every Friday, summarize what customers are saying about our onboarding experience — from sales calls, support tickets, and CS notes. Highlight top praises, biggest frustrations, and changes week-over-week.”

Now possible because:

• Agent can pull from live customer interactions across different systems (Gong, Intercom, Zendesk, etc.)
• Agent can distinguish between topics (onboarding vs. implementation vs. support) and surface trends
• Agent delivers insights on a regular cadence, without you having to ask twice

Dynamic Content Creation

“Create a refreshed sales one-pager for our analytics dashboard. Use recent call quotes from buyers who loved it, include competitive differentiators based on recent customer objections, and align the messaging to our updated positioning doc.”

Now possible because:

• Agent understands what content exists, what messaging is current, and what customers are saying
• Agent can pull in real call quotes and tailor messaging to target personas or objections
• Agent generates on-brand, up-to-date content that’s useful immediately — no waiting for creative briefs

Cross-Channel Workflow Automation

“When a new case study goes live, draft a LinkedIn post for the PMM, a sales blurb for the battlecard, and an email for our top 100 prospects — personalized based on industry. Then share them with the right owners for review.”

Now possible because:

• Agent knows what content is new, what channels need updates, and who needs to approve
• Agent uses CRM data to personalize outreach based on industry, persona, or funnel stage
• Agent connects outputs to distribution workflows — so content actually gets used
  

The bottom line

Agents, powered by MCP and supported by tools like RAG and orchestration, shift how marketers operate. They turn disconnected data into insight, repetitive work into action, and complexity into clarity.

This isn’t about replacing marketers. It’s about removing the manual steps that slow them down. Instead of gathering information or chasing handoffs, marketers can focus on what they do best: crafting the message, setting the strategy, and driving impact.

AI agents make the rest of it easier. And they’re only just getting started.