When I started integrating AI into my workflows, I was seduced by the promise of “one tool to rule them all.” One login. One workflow. One platform that would manage research, writing, operations and communications — all in one neat package. In theory, it was elegant. But what I found, in the end, was a trap.

What broke first were nonnegotiables: depth, nuance and reliability. The moment I tried to force a single AI platform to do everything — from deep research to outreach copy to automation orchestration — I hit an invisible wall. Research became shallow, writing homogenized and operational workflows became brittle. That’s when I realized that no single AI tool could do everything I needed.

What followed was a shift in mindset: I swapped “one-platform thinking” for “stack thinking.” I started curating a bench of specialized tools — each assigned to a distinct job — and built workflows that were resilient, adaptable and far more effective in the real world.

How I fell into the one-platform trap

At first, using a single AI platform felt efficient. Everything was under one roof. No juggling accounts, no format drift. Just “AI, here I come.” It was neat. It felt modern.

But the cracks started to show quickly. The first to crumble was research depth. I’d task the platform with what I thought was “deep research” — reading about an executive’s background, pulling themes, summarizing context. Then I’d ask the same system to convert that into outreach copy, a positioning doc or automation steps.

On the surface, the output looked fine. But I slowly recognized a pattern:

• The research was broad, but it missed edge-case nuance.
• The writing felt safe, generic, unbranded and homogenized.
• Operations workflows ran, but felt brittle or manual when stretched.

And — and this was the real problem — I was spending more time debugging the platform’s limits than I was accomplishing meaningful work.

The turning point came when I tried to run an “agentic” workload for my Chief of Staff agent — which I named Isla. The plan was simple: have one AI run end-to-end. It would read my email threads, parse context, draft replies and convert follow-ups into actionable tasks. It was a heavy load. What could possibly go wrong?

When I took a close look at how Isla was performing, the details were a mess. Context accuracy had collapsed. The AI mis-threaded conversations; summaries lost nuance; and follow-ups failed to capture essential subtleties. I tried patching it — full-thread retrieval, name-matching, confidence-scoring — but no matter how clever the prompts got, the central limitation remained. As it turned out, the single platform could not mimic the complex pipeline of human judgment, context and layered logic.

That’s when I stopped asking “How do I make one tool do everything?” and started asking, “What tool is built for the various jobs involved here?”

Stack thinking and the blind spot revelation

Once I gave myself permission to stop chasing “one tool to fit all,” I embraced stack thinking: letting a curated set of specialized tools work together and do what they do best.

The real revelation? The moment I added a dedicated research engine into the mix, it exposed gaps I never saw coming. Suddenly:

• I uncovered contradictions between what an executive said last year versus last month.
• I discovered niche angles hidden in small-circuit podcasts, obscure interviews and domain writings.
• I detected unspoken strategic tensions that never surfaced in mainstream bios.

I didn’t just improve research. I changed the reality I was able to see. What I had thought were prompt errors turned out to be problems caused by using a single tool too broadly. After noticing that, I was sold on building a stack.

The discipline of curation — not accumulation

Stack thinking isn’t about collecting every shiny new tool. It’s about curation. Today I treat tools like hires: they need to specialize in a job and they must bring value.

Here are the questions I ask before giving a tool a seat on my bench:

• What job is it uniquely better at? If it’s only “slightly better,” it doesn’t earn a slot.
• Does it create compounding time savings? Weekly multipliers beat one-off wins.
• Can it integrate without breaking my workflow rhythm? If adopting it means rewiring habits, I need a 10× payoff.

Most tools fail at the first question. They’re generalists pretending to be specialists. I don’t need another “pretty good at everything” model. I need a killer in one slot. Now the rule is blunt: if I can’t describe the tool’s unique role in one sentence, it doesn’t make the cut.

Managing the integration tax: Making many tools work as one

A multi-tool stack is elegant in theory, but messy in practice. Multiple tools bring context switching, format drift and data-handoff friction. That overhead is real and dangerous.

I’ve found that the only way to manage it is through rigid discipline and structured orchestration that follows this structure:

• Define fixed input and output schemas between tools.
• Use a small number of orchestrator prompts to translate between systems.
• Avoid freeform tool-to-tool conversations. Everything passes through a framework — predictable, testable, swap-able.

For example, when I built a large site on one platform (800+ MB on Replit) for speed and momentum, it got me moving fast — but it wasn’t the right environment for final hosting. I needed a different stack to handle production-ready architecture. Because I had built with a bench mentality, not a platform addiction, I was able to rip, transplant and rebuild.

I define freedom like this: vendor independence, portability and reliability. My workflow — and my business — runs on my terms, not a single tool’s roadmap.

Mapping specialization to function: What goes where

If you’re building your first AI toolbench, don’t start with tools. Start with functions. Map them carefully by:

• Research and sensing: breadth, retrieval, verification.
• Synthesis and reasoning: ambiguity tolerance and multi-step logic.
• Production: tone, format, media output.
• Operations and automation: routing, triggers, task persistence.

Common mismatches happen when people expect a research engine to write like a marketing copywriter, or a writing engine to manage workflows, or automation tools to reason like humans. That’s how you get universal mediocrity — the “jack of all trades, master of none” curse.

In our own processes, we don’t rely on a single agent. We split functions across distinct agents.

Evolution over revolution: Versioning your stack the right way

Because AI is evolving rapidly, it’s tempting to chase every new launch or next-generation platform. But I don’t. I treat my toolbench like a product roadmap: methodical, practical and diverse.

Here’s how I approach new tools:

• Identify friction or a ceiling in the current stack.
• Test new tools in sandbox workflows — limited, controlled and isolated.
• Measure real before-and-afters performance based on leverage — not hype.
• If a new tool overlaps heavily with an existing one but doesn’t beat it decisively, I pass.

This disciplined, iterative approach ensures that your architecture remains resilient and free from the inherent brittleness of a single, all-in-one system.

Build a bench, not a castle

If you’re building with AI today — for business operations, content, automation or long-term strategy — don’t fall for the one-platform myth. It’s seductive. It’s simple. But simplicity can be the mask of fragility.

Instead, adopt stack thinking. Be deliberate about which tool does which job. Prune the bench. Define your schemas. Standardize handoffs. Insist on workflow fit, not hype.

Your AI doesn’t need to be a monolith. It needs to be resilient, adaptable and designed for real-world friction. If you build a bench instead of a castle, you’ll find that what you gain isn’t just efficiency or output. It’s clarity, quality and freedom. And in a world of constant change, that’s a far more powerful advantage than any single tool ever will be.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?