Stop Thinking of AI as a Coworker. It's an Exoskeleton.

We're thinking about AI wrong.

I keep noticing the same pattern: companies that treat AI as an autonomous agent that should "just figure it out" tend to be disappointed. Meanwhile, companies that treat AI as an extension of their existing workforce, an amplifier of human capability rather than a replacement, are seeing genuinely transformative results. Thats not to say that AI can't act automonously with specific tasks (see the rise of OpenClaw as a viral proof of concept), but even that still acts as an extension of human decision making and context.

The framing matters more than we realize. And I think the best mental model for understanding AI isn't a new coworker. It's an exoskeleton.

The Exoskeleton Model

Stay with me here, because this isn't just a metaphor. There are real examples of exoskeletons being deployed right now across manufacturing, logistics, military, and healthcare. The statistics are worth paying attention to.

In Manufacturing:

Ford has deployed EksoVest exoskeletons in 15 plants across 7 countries. The result? An 83% decrease in injuries in units using exoskeletons. Workers still do the overhead lifting (4,600 times per day)but with 5-15 pounds of assistance per arm that makes the work sustainable.
BMW's Spartanburg plant reports 30-40% reduction in worker effort using Levitate Technologies vests.
German Bionic's Cray X provides up to 66 lbs of lift support per movement. German Bionic reports that customers using the Cray X, including BMW and IKEA, have seen a 25% reduction in sick days.

In Military Applications:

The Sarcos Guardian XO Max provides 20:1 strength amplification. 100 lbs feels like 5 lbs. Soldiers can carry up to 200 pounds, not because the suit replaces them, but because it amplifies what they can already do.
The Lockheed Martin HULC enables carrying 200 pounds at sustained speeds of ~7 mph with 10 mph bursts. This matters because musculoskeletal injuries account for over half of all military injuries, with back injuries among the most common. The exoskeleton doesn't fight for the soldier. It keeps them from getting injured while they do their job.

In Medical Rehabilitation:

In a meta-analysis of powered exoskeleton training, 76% of patients with spinal cord injuries were able to walk while wearing the exoskeleton with no additional physical assistance from therapists, many using only crutches or walkers for balance. These are people who were told they would never walk again.

Even in Running:

Stanford's 2020 research showed a 15% reduction in the energy cost of running with their ankle exoskeleton, potentially translating to a 10% boost in running speed.
Harvard's soft exosuit reduced the metabolic cost of running by 5.4%. That means a marathon would feel like running 24.9 miles instead of 26.2.

Notice the pattern here. The exoskeleton doesn't replace the human. It doesn't lift the boxes, run the race, or walk the steps independently. It amplifies human capability. The human is still doing the work, they're just able to do dramatically more of it, more sustainably, with less injury and fatigue.

The Ontological Problem with "AI Agents"

Here's where the AI industry has gone a bit sideways.

There's been this rush toward "agentic AI", or systems that operate autonomously, make their own decisions, and complete entire workflows without human intervention. The dream of having a fully autonomous AI employee is seductive. But I think we've been seduced by the wrong metaphor.

When we think of AI as an autonomous agent as a separate entity with its own judgment and decision-making, we set ourselves up for disappointment. We expect it to understand context it wasn't given. We expect it to make judgment calls it isn't equipped to make. We get frustrated when it "hallucinates" or goes off the rails.

What This Looks Like in Product Development

Instead of building AI that autonomously decides what your product should be, we built a platform that goes incredibly deep on research and analysis — then puts the insights in front of the humans who actually make the calls.

The difference sounds subtle but it's not. Let me give you a concrete example.

Kasava's commit analysis doesn't just count lines of code. It reads every commit across your repositories, categorizes changes by type and impact, identifies patterns in how your codebase is evolving, and surfaces risks you might not have noticed — like a critical module that's been accumulating technical debt for months. But it doesn't decide what to do about it. That's your call. The AI goes deep. The human decides what matters.

Our transcript analysis works the same way. Feed in customer calls, user interviews, or sales conversations, and Kasava extracts themes, sentiment shifts, feature requests, and pain points across hundreds of hours of recordings. It surfaces patterns no human could find manually — not because humans aren't smart enough, but because there's too much data to hold in your head at once. The AI handles the scale. The human interprets the meaning.

This is the exoskeleton model. Each capability in Kasava is like a component of a larger system that, when assembled, gives product teams dramatically deeper insight into their product, their market, and their users — not by replacing their judgment, but by amplifying their capacity to make informed decisions.

Why "Autonomous Agents" Often Fail (And How the Product Graph Fixes It)

Autonomous agents fail because they don't have the context that humans carry around implicitly. They don't know that your enterprise clients care more about reliability than speed. They don't know that your team decided last quarter to deprecate a feature that's still getting usage. They don't know that the reason you price things the way you do is rooted in a competitive dynamic that never got written down anywhere.

This is the fundamental problem with generic AI tools applied to product decisions — they're missing the connective tissue of your product's reality.

Kasava's answer to this is the product graph — a structured, living representation of your product that combines two layers of context most AI tools never have.

The first layer is built automatically. Kasava ingests your codebase, your commit history, your GitHub issues, your PRs, your project management tickets — and from that raw material, it constructs a deep understanding of what your product actually is. Not what your marketing page says it is. What the code says. Which features are actively evolving, which are stagnating, where complexity is concentrating, what your team is actually spending time on versus what the roadmap claims. This is thousands of signals that already exist in your workflow — Kasava just reads them, connects them, and makes them queryable.

The second layer comes from you. When you tell Kasava that a particular feature is strategic, or that a competitor's recent launch changes your priorities, or that certain customer segments matter more than others, those heuristics get woven into the graph alongside the automated context. Your judgment about what matters meets the machine's ability to track everything.

This is what makes the exoskeleton model actually work in practice. The Ford EksoVest provides 15 pounds of lift assistance regardless of context — it's a simple mechanical amplifier. But product decisions aren't simple. They require judgment shaped by history, strategy, and nuance that only your team has. The product graph is how that judgment gets combined with a massive, continuously updated body of evidence from your actual codebase and workflows — so that when Kasava analyzes your commits, tracks your competitors, or synthesizes customer feedback, it's doing so through the lens of both what's really happening in your product and what your team believes should happen next.

It's this symbiosis — automated depth meeting human direction — that makes Kasava an exoskeleton rather than just another AI tool. Neither layer works alone. The machine can't decide what matters. The human can't track everything. Together, they create something neither could achieve independently.

The Micro-Agent Architecture

If you want to build AI that actually works for your team, here's the framework I'd suggest:

1. Decompose jobs into discrete tasks, not entire roles.

Don't ask "can AI do a developer's job?" Ask "what are the 47 things a developer does in a given week, and which of those can be amplified?"

For us, that looks like:

Writing commit messages → AI amplified
Searching the codebase for patterns → AI amplified
Making architectural decisions → Human judgment, AI research
Writing boilerplate code → AI amplified
Reviewing code for security issues → AI amplified
Updating documentation to match product changes → AI amplified
Deciding what features to build → Human judgment
Debugging complex issues → Human leads, AI assists

2. Build micro-agents that do one thing well.

Each component of your AI "exoskeleton" should be focused and reliable. An commit change agent restates problems for clarity, breaks down complex file changes, looks up dependencies, researches existing patterns, and provides a high level summary with oppportunity to dig in further. That's it. But it does that reliably, every time.

3. Keep the human in the decision loop.

This is crucial. The exoskeleton model only works if the human remains in control. The Sarcos Guardian XO provides 20:1 strength amplification, but the human still decides what to lift and where to put it. Similarly, your AI tools should amplify decision execution, not make the decisions themselves.

4. Make the seams visible.

One of the problems with "autonomous agents" is that they obscure the AI's limitations. When something goes wrong, you don't know where in the autonomous workflow it failed. With the micro-agent approach, each component has clear inputs and outputs. When something goes wrong, you know exactly which component failed and can debug accordingly.

The Productivity Numbers

Here's what's interesting: the productivity gains from the exoskeleton approach often exceed what people expect from "full autonomy."

Consider the running exoskeleton research. A 15% reduction in energy cost doesn't mean the runner runs 15% farther. It means they can run faster for longer, with better form, and recover more quickly. The compounding effects matter more than the headline number.

Same with the industrial exoskeletons. A 30% reduction in muscle stress doesn't just mean 30% less fatigue. It means fewer injuries, fewer sick days, longer careers, better quality work, and happier workers who aren't in chronic pain.

In software, there are similar compounding effects. When developers aren't spending mental energy on boilerplate code, commit messages, planning documents, and issue formatting, they have more capacity for the creative work that actually moves products forward. The AI exoskeleton doesn't just save time on specific tasks. It preserves cognitive resources for the tasks that require human judgment.

We went from struggling to maintain documentation to having it auto-updated weekly. From spending 20 minutes per PR on commit messages and descriptions to having them drafted in seconds. From context-switching between tools to having AI agents that plug directly into our workflow. None of these are "autonomous AI." They're amplification tools that compound.

The Future Isn't Autonomous: It's Amplified

If you're trying to figure out how to make AI work for your organization, here's my practical advice:

Stop asking: "How do we deploy AI agents that can handle workflows autonomously?"

Start asking: "What are the most repetitive, error-prone, or fatigue-inducing parts of our workers' jobs, and how can AI reduce the friction there?"

Think like an exoskeleton designer. They don't ask "how do we build a robot that does the factory worker's job?" They ask "where in the body does the worker experience the most strain, and how do we support that specific point of failure?" The exoskeleton market is expected to reach $2 billion by 2030, growing at nearly 20% annually. But notice what that growth is for: it's not for robots that replace workers. It's for devices that make workers stronger, faster, and more resilient.

The same will be true for AI. The enduring value won't come from autonomous systems that work independently of humans. It will come from AI tools that are so well-integrated into human workflows that they feel like natural extensions of human capability.

Want to learn more about how Kasava can be your product development exoskeleton? Building something similar? Experimenting with your own AI exoskeleton? Find me on Twitter or LinkedIn.

Sources

Manufacturing:

Military:

Medical Rehabilitation:

Miller et al. (2016) - Powered exoskeleton-assisted walking for SCI patients, Medical Devices: Evidence and Research

Running Research:

Market Data: