Agents are coming for your clicks – Betty says get ready
From Static Answers to Agentic Actions
First, it was embeddings. Then it was RAG. Now every AI search conversation seems to circle around agents, agentic behavior, & MCPs.
At first glance, it sounds like we’re about to spin up a Dungeons & Dragons campaign (“my agent is lawful-neutral, +3 in reasoning”), but in reality, these buzzwords point to how AI may be moving from static answers to dynamic actions.
This post explores:
- Agentic – what it could mean as AI begins taking initiative
- Agents – the task-doers that may act on our behalf
- MCPs (Model Context Protocols) – the infrastructure (hosts, clients, & servers) that could make all this work at scale
TL;DR: How AI Might Get Stuff Done
Agentic → Potential behavior: Gives AI autonomy to decide & take next steps → Strategy tip: Anticipate user journeys & consider optimizing content for the “next action,” not just the initial search
Agents → Potential role: Execute those steps as discrete task-runners → Strategy tip: Make content task-completion friendly—clear, structured, & ready to be acted on
MCPs (Model Context Protocols) → Potential framework: Provide the host–client–server rules that let agents talk to different tools, APIs, & data sources → Strategy tip: Treat your content like a server: schema, structured data, & API endpoints could help agents fetch & act on it
Agentic
Agentic behavior refers to the possibility of AI systems moving from reactive to proactive. Instead of only responding to exactly what you ask, they may begin to:
- Suggest follow-up steps
- Trigger the “next action” on their own
- Decide which context matters most
Think: a chatbot that doesn’t just tell you solar panels are worth it, but offers to line up an installer for you.
Agentic is the behavior. Agents are the things that may carry out that behavior.
Search Implication: If this trend grows, we may need to optimize for intent chains. Missing the “next step” content could mean an agent passes the user to someone else.
Agents
Agents are the potential execution layer. They’re the actual programs, bots, or task-runners that may make agentic workflows happen.
If agentic = the decision-making & initiative… Agents = the doers of that behavior.
Examples could include:
- A travel agent that not only suggests hotels but books them
- An AI agent that audits your site & pushes fixes into GitHub
- A shopping agent that finds a laptop, adds it to cart, & checks out
Search Implication: As agents take on more task completion, content may need to be structured so they can “grab” it with confidence. That suggests clean steps, trust signals, & structured data will grow in importance.
MCPs (Model Context Protocols)
If agents are the doers, MCPs may become the rulebook that makes their work possible.
MCPs create a standard way for AI to access tools, APIs, & data. Think host–client–server:
- Host: The LLM environment (ChatGPT, Gemini) where the agent lives
- Client: The agent making a request (“find solar installers”)
- Server: Your website, API, or dataset that provides the info or action endpoint
Without MCPs, every integration could stay custom & brittle. With them, everything may begin to speak the same language.
Search Implication: Websites could evolve into less of a “page for humans” & more of a server for agents. Schema markup, APIs, & structured data may be how sites become discoverable & usable in this new architecture.
Crystal Ball Moment: Changing User Behavior & Agent Adoption
Here’s a wild prediction, straight from Betty’s tarot deck: I see a future where agents begin to replace users for many search & task workflows.
Instead of us typing keywords into a search bar, our agents could soon be running the agentic workflows for us:
- Interpreting intent
- Finding the best source
- Taking actions end-to-end
In my crystal ball, agents are the ones asking, comparing, deciding, & acting while the human sits back, blissfully uninvolved.
If this trajectory continues, search may evolve into optimizing for agents as intermediaries, not just people.
The omens already flicker on the horizon:
- Trust in AI – 39% of consumers say they’re already comfortable using AI agent
- High-Value Transactions – Consumers are turning to AI agents for big-ticket choices once left to humans:
- Flights: 70% of consumers book with an agent
- Hotels: 65%
- Electronics: 59%
Public Agents Already Walking Among Us:
- Gemini → Plans trips, compares products, summarizes topics
- Expedia’s Romie → A WhatsApp travel agent that builds itineraries
- Hopper → Predicts airfare changes, even locks fares
- GuideGeek → Travel assistant across WhatsApp & Instagram
DIY Magic Is Here Too: Platforms like OpenAI GPTs, Zapier AI Agents, & MindStudio mean anyone can conjure their own agent. Define the job, connect the tools, & suddenly your “search engine” is a personal assistant that never opens Google.
What To Do With This
- Design for “next step” visibility: Consider content that anticipates the agent’s workflow, not just the human click.
- Make content agent-friendly: Structured, step-based, & loaded with trust signals.
- Think like a server: Schema, APIs, knowledge bases, & content that machines can use may gain an edge as agents rise.
Tools & Thinkers to Follow
- Dave Davies – AI agents in SEO: What you need to know
- Mari Haynes – AI Agents
- Josh Blyskal – OpenAI’s Operator and the Dawn of Agent-Oriented SEO