The AI Tool You Pick Doesn't Matter

There's a weird thing happening in the AI tools space right now. Cursor just added agents. Claude Code added memory. OpenClaw can read files. Windsurf launched with similar architecture. Every tool is becoming every other tool.

Nufar Gaspar, speaking on The AI Daily Brief's latest episode, argues this convergence means we've been optimizing for the wrong thing. The tool choice? Increasingly irrelevant. What actually matters is what you build underneath.

She calls it your "agentic operating system"—and it's probably the most overlooked concept in practical AI right now.

The Portability Problem (That's Actually Good News)

Here's the part that makes this interesting: almost everything these agentic tools do under the hood is reading text files. Files that define who you are, what you know, what you can do, what you remember.

"The work you do to build your system is portable," Gaspar explains. "While the tool owners might not want you to think so, that's the reality of every tool becoming every tool. When you switch tools or add a new one, all you have to do is point the tool to the same folder and it reads the same files. No migration, no rebuild."

This is the opposite of how most people approach AI tools—they pick a platform, learn its quirks, invest in its ecosystem, then feel locked in. But if the underlying system is just human-readable text files? Your investment travels with you.

The catch is you actually have to build that system. And most people haven't.

Seven Layers You Didn't Know You Needed

Gaspar's framework has seven layers, each solving a different problem in how AI understands and serves you. The running example she uses is a "chief of staff" agent—something that reviews your inbox, preps you for meetings, tracks commitments, flags blind spots.

It's a practical choice. Whether you're entry-level or executive, everyone benefits from an AI that knows your working style, your priorities, your people.

Layer one is identity. Who are you, and what rules do you want enforced every time? In OpenClaw it's called "soul." In Cursor it's "agents.mmd." Different names, same concept: a text file that tells the tool who it's working for.

The smartest part of Gaspar's approach? Don't write this file yourself from scratch. You'll hate it and quit. Instead, brain dump to an AI, let it interview you with 15 questions about how you work, then edit the draft it produces. Ship a version that's 70% right, then patch it over three weeks as you notice gaps.

Layer two is context—what you know. This is where the generic "AI advice" problem gets solved. Generic advice is a Google search away. What you can't get from the public internet is your roadmap, your org chart, your customer segments, your actual situation.

"What you know is the single biggest predictor of whether AI gives you generic output or something genuinely useful for your actual situation," Gaspar says. And here's the key insight: "No model improvements will ever know what you're shipping next quarter or who your key stakeholders unless you tell it."

The trap is trying to create one massive context document. That becomes a novel that goes stale. What works is three to five focused files, each a single page, each covering one thing: my team, my product, my customers, my quarter. Dated. Fresh. Updated when things change.

Gaspar calls it "context curation"—not a project, but a practice. Every time you catch yourself re-explaining something about your situation to AI, that thing should have been in a context file.

Layer three is skills—reusable instruction sets for workflows you do repeatedly. Weekly status updates. Meeting prep. Stakeholder emails. Each one written as: when I say [trigger], do [process], using [sources], produce output in [format].

Without this, you're re-explaining the format every time, pasting the same sources every time, complaining the output doesn't match your voice—but never teaching the AI your voice. A skill fixes that. Write it once, it fires forever.

Again, the methodology is MVP first: ship a version that's 70% right, use it for a week, notice what's off, patch it. A few weeks in, it's writing better first drafts than starting from scratch ever could.

Layer four is memory—where every tool company is investing heavily right now. Claude Code added auto-memory. Cursor has project-level memory. Things are changing daily because memory is clearly one of the biggest unlocks.

Gaspar's advice here is surprisingly practical: understand how your specific tool's memory works. Ask it directly. "Explain how your memory system works. What do you remember between sessions? What do you forget?" Most tools still have gaps in cross-session memory, in what they retain versus discard.

For advanced users, she suggests adding specialized memory for work context: decision logs (what was decided, why, what alternatives were considered), learning logs for ongoing process improvement, relationship context for how conversations with specific stakeholders went.

Layer five is connections—how your agent reaches real systems. Email, calendar, Slack, Jira, Salesforce. This is where Model Context Protocols (MCP), CLI tools, and direct APIs come in. The good news is tools are making this progressively easier out of the box.

One critical note: "Start as much as possible with read-only access. Before you let your agents write back into systems, let them only read your calendar or only read your inbox."

This is the point where the AI goes from "smart assistant" to "thing that takes actions in the real world." Starting with read-only is just smart risk management.

The Knowledge Work Angle

What's interesting about Gaspar's framing is the explicit focus on knowledge work, not coding. Most of the discourse around agentic tools centers on development—which makes sense given where the tools came from. But she's right that most professionals aren't developers.

"Whether it's strategy, communication, operations, decision-making, research, management—anything the knowledge worker does, that is where most professionals live and that is where the agent OS makes the biggest difference," she explains.

The system she's describing isn't about automating code reviews. It's about automating the recurring cognitive work that fills knowledge workers' days: synthesizing information, maintaining context across conversations, keeping track of who needs what, preparing for the next thing.

For people in those roles, the question isn't "which AI tool should I use?" It's "what knowledge do I have that isn't written down anywhere?"

That shift—from tool selection to system building—is where the actual productivity gains live. The tools will keep converging. The features will keep copying each other's breakthroughs. What won't commoditize is your specific context, your workflow patterns, your way of working.

That's what an agentic operating system captures. And if it's built right, it goes with you wherever the tools go next.

—Tyler Nakamura