MCPs Enable Emergent Behavior in AI Agents

When an AI can see multiple tools at once and understand their capabilities, it starts finding creative solutions to problems

I've been experimenting with MCPs (Model Context Protocol) for the past few months, and I've discovered something unexpected: when you give AI agents the ability to interact with multiple tools simultaneously, they start solving problems in ways you never explicitly programmed them to.

I was working as a Head of Engineering without a dedicated Product person. I needed to quickly turn strategy documents into actionable tickets, so I plugged in a Linear MCP for project management and Todoist for my personal tasks. My plan was simple: dropping in documents and thoughts, then generating properly linked tickets assigned to the right people.

But then something interesting happened. I gave the AI a large, vague task like "implement user authentication." Instead of creating a single ticket, it:

• Broke the task into subtasks (phased out tasks that can be completed and tested independently)

• Set up dependencies between them

• Added test criteria to each subtask

• Included security considerations

I never asked it to do any of this. The AI looked at the context (engineering project in Linear), understood the implications, and structured the work the way an experienced PM with a background in engineering would.

This wasn't a workflow I had configured. It was emergent behavior - the AI combining its understanding of software development with the capabilities of the Linear API to create something more useful than what I'd asked for.

Why This Matters More Than You Think

Traditional automation tools like Zapier would require you to explicitly program each of these steps. If this, then that. Deterministic and predictable.

MCPs enable something fundamentally different. When an AI can see multiple tools at once and understand their capabilities, it starts finding creative solutions to problems. It's not following a script - it's actively problem-solving.

Another example: I connected an AI to both my email and my calendar. When I asked it to schedule a meeting based on an email thread, it didn't just find an empty slot. It:

• Analyzed the email thread to understand the meeting's purpose

• Suggested a duration based on the topics discussed

• Drafted an agenda pulling key points from the emails

• Even suggested which participants were optional based on the conversation flow

Again, I hadn't programmed any of this logic. The emergent behavior came from the AI having simultaneous access to multiple tools and the context to use them intelligently.

The Speed of Discovery

What makes MCPs particularly valuable right now is the speed at which you can experiment with these emergent workflows. I can connect a new tool in minutes and immediately see what unexpected behaviors emerge.

This rapid experimentation is crucial because we're still discovering what's possible. Every new combination of tools seems to unlock new emergent behaviors:

• Connect a code repository MCP with a documentation tool, and the AI starts auto-generating docs that actually match your codebase

• Add a data analysis tool to a CRM integration, and suddenly the AI is finding customer patterns you never thought to look for

• Combine project management with communication tools, and the AI begins surfacing blockers before they become problems

The key is that these aren't features anyone built. They're capabilities that emerge from giving AI the right tools and context.

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The Current Limitations

Of course, MCPs aren't perfect. Configuration can be painful - I often end up writing custom MCPs because existing ones don't quite fit my needs. The ecosystem is fragmented, with everyone building their own MCP hoping to get noticed.

There's also a real question about longevity. As AI code assistants get better at writing API integrations on the fly, will we even need MCPs? Maybe they're just a stepping stone to something better.

But right now, they're the fastest way to explore what happens when AI can orchestrate multiple tools. And what I'm seeing suggests we've only scratched the surface of what's possible.

What This Means for the Future of Work

The emergent behaviors I'm seeing with MCPs hint at a fundamental shift in how we'll work with AI. Instead of AI as a better search engine or writing assistant, we're moving toward AI as an intelligent orchestrator of our digital tools.

Imagine a future where you describe a goal to your AI assistant, and it figures out which tools to use and how to combine them to achieve it. Not because someone programmed that specific workflow, but because the AI can reason about the capabilities available to it.

That's the promise of emergent AI workflows, and MCPs are giving us our first real taste of what that might look like.

Start Experimenting Now

If you're curious about this, I'd encourage you to start experimenting with MCPs yourself. Pick two or three tools you use daily and connect them to an AI assistant. Then watch for the unexpected.

What surprised me most wasn't the time saved on routine tasks - it was discovering entirely new ways of working I hadn't imagined. When AI can see across your tools and find its own solutions, the results can be genuinely surprising.

That's the real story with MCPs: they're not just about connecting AI to tools. They're about discovering what AI can do when it has the freedom to be creative with those connections.

Benny Chat: MCPs for Tasks

We recently posted about our work building out Benny Chat using MCPs which has enabled it to be a uniquely capable task helper in a way that other task tracking tools aren’t! Read on for more information on how we brought Benny Chat to reality and how to get your hands on it.