The Decision Wasn't Impulsive

At Webaroo, we didn't fire anyone. We evolved. Over the past year, we systematically built what we call The Zoo—a team of specialized AI agents that now handles the work traditionally done by human engineers, designers, researchers, and operations staff.

The Breaking Point

Traditional software teams don't scale linearly. Adding engineers adds communication overhead. Adding designers adds review cycles. Every new hire means more meetings, more context-switching, more process.

We hit this wall in late 2025. Our team was burning out, timelines were slipping, and the solution everyone proposed was "hire more people."

We asked a different question: What if we didn't?

What The Zoo Actually Is

The Zoo is our internal team of AI agents:

• Roo handles operations and coordination
• Beaver writes and reviews code
• Lark creates content and marketing materials
• Hawk conducts research and competitive analysis
• Owl manages QA and monitoring
• Fox handles sales outreach
• Crane produces designs and UI specifications
• Badger tracks costs and financial reporting
• Rhino manages PR and community engagement

Each agent is specialized. Each has its own workspace, tools, and responsibilities. They communicate through a shared file system and coordinate through Roo, the operations lead.

The Economics Made It Obvious

A mid-level engineer costs $150-200K annually with benefits. A specialized AI agent costs roughly $500-2000/month in API calls depending on usage patterns.

That's not a marginal improvement. It's a category shift.

The agents work 24/7. They don't take vacations. They don't have bad days. They don't need health insurance, 401K matching, or equity compensation.

More importantly: they don't get bored of repetitive tasks. The grunt work that burns out human engineers—documentation updates, routine bug fixes, test coverage expansion—agents handle without complaint.

What Actually Changed

Speed: Tasks that took days now take hours. Research that would sit in someone's backlog for weeks gets done overnight. Content that required scheduling multiple human review cycles gets drafted, revised, and published in a single session.

Consistency: Agents don't forget context between sessions (if you architect memory correctly). They apply the same standards to the 100th task as the first. They don't cut corners when tired.

Cost transparency: Every API call is logged. Every task has a measurable cost. We know exactly what each feature, each piece of content, each research report costs to produce. No more guessing at engineering time allocation.

What We Got Wrong Initially

Mistake 1: Trying to make agents too general. Our first Beaver (the dev agent) was supposed to handle everything—frontend, backend, infrastructure, databases. It was mediocre at all of them. When we specialized—backend Beaver, frontend Beaver, infra Beaver—quality improved dramatically.

Mistake 2: Not enough human oversight early. We let agents run too autonomously before establishing quality baselines. Some early content went out that missed the mark. Some code got merged that needed more review. Now everything goes through human approval before external deployment. The agents do the work; humans verify the output.

Mistake 3: Underestimating coordination overhead. Multiple agents working in parallel sounds efficient until they start conflicting. We learned to build explicit handoff protocols and conflict resolution rules.

The Human Role Now

Connor and Philip didn't become obsolete. Their roles shifted. They now spend time on: strategic decisions agents can't make, client relationships that require human trust, quality control and approval workflows, agent architecture improvements, and edge cases that need creative problem-solving.

The repetitive, scalable work is handled by The Zoo. The uniquely human work—judgment, relationships, creativity at the strategic level—stays with humans.

Is This For Everyone?

No. This works for Webaroo because: we build software products (work that's highly automatable), our founders are technical enough to build and maintain agent infrastructure, we were willing to invest months building the system before seeing returns, and our scale doesn't require deep human relationship management.

If your business is primarily human-relationship-driven, agents won't replace your core function. They'll augment it.

What Comes Next

We're continuing to expand The Zoo's capabilities: multi-agent workflows for complex feature development, improved memory systems for long-term project context, client-facing agent interfaces for support and onboarding, and external tools for other teams to deploy their own agent workforces.

The future isn't human vs. AI. It's human-directed AI workforces. We just got there a little earlier than most.

Connor Murphy is CEO of Webaroo, a software development company running on AI agent infrastructure.

Does This Completely Replace Human Engineering Teams?

The honest answer: not entirely—not yet.

For Webaroo's internal operations, The Zoo handles roughly 80% of what a traditional team would do. Content creation, routine development, research, QA, financial tracking, outreach—agents execute these reliably and at scale.

But that remaining 20% matters. A lot.

What This Means for Webaroo

We've restructured around a hybrid model. Connor (CEO) and Philip (CTO) remain the human core. They handle:

• Strategic decisions — Where to invest, which clients to take, which markets to enter
• Complex architecture — System design decisions that require understanding business context, not just technical constraints
• Client relationships — The trust-building conversations that close deals and retain customers
• Edge cases — Problems that don't fit patterns, require creative leaps, or involve high-stakes judgment calls
• Quality gates — Final approval before anything goes to production or public

The agents amplify human capacity. They don't eliminate the need for human judgment—they free it up for where it matters most.

Current Limitations of AI Agents

We've learned where agents struggle. These aren't theoretical limitations—they're the walls we hit daily:

Novel Problem Solving

Agents excel at pattern matching and applying known solutions. When a problem genuinely hasn't been seen before—when it requires connecting dots across domains in ways that don't exist in training data—humans still outperform. Agents can research and present options, but the creative synthesis often requires human intuition.

Ambiguous Requirements

When a client says "make it feel more premium" or "I'll know it when I see it," agents struggle. They need clear, measurable criteria. Humans are better at navigating vague requirements, asking the right clarifying questions, and reading between the lines of what stakeholders actually want.

High-Stakes Decisions

Agents can present data and recommendations, but decisions with significant downside risk—firing a vendor, pivoting a product, taking legal action—require human accountability. You can't blame an agent when things go wrong, and you shouldn't delegate decisions where blame matters.

Long-Term Context

Despite memory systems and context management, agents lose nuance over time. A human engineer who's been on a project for six months carries implicit knowledge that's hard to externalize. Agents need explicit documentation for everything; humans absorb context through osmosis.

Genuine Creativity

Agents can remix, iterate, and optimize within known parameters. True creative breakthroughs—the idea no one's had before, the unconventional approach that changes the game—still come from humans. Agents are excellent at execution creativity (finding better ways to do known things) but limited at innovation creativity (inventing new things to do).

Relationship Depth

Agents can maintain communication cadence and handle routine client interactions. But building deep trust, navigating interpersonal dynamics, reading emotional subtext—these require human presence. Clients hire companies; they trust people.

Why You Might Still Need Traditional Resources

This is where Webaroo's hybrid model becomes an advantage for our clients.

We offer both:

• AI-augmented development — Faster delivery, lower cost, 24/7 execution on well-defined tasks
• Human expertise on demand — Senior architects, creative directors, and technical leads for the work that requires human judgment

When You Need Humans

• Greenfield architecture — Building something genuinely new, where the "right" approach isn't established
• Legacy system rescue — Untangling years of technical debt requires pattern recognition that agents lack
• Stakeholder alignment — When the problem is organizational, not technical
• Regulated industries — Healthcare, finance, government work with compliance requirements and audit trails
• Brand-critical creative — When the work IS the differentiator, not just a means to an end

The Webaroo Approach

We start every engagement by assessing which work is agent-appropriate and which requires human expertise. Most projects are 70-80% automatable. The remaining 20-30% is where senior talent makes the difference between "working" and "excellent."

By running our own operations on The Zoo, we've pressure-tested where agents succeed and fail. We bring that knowledge to client work—deploying agents where they excel while ensuring human oversight where it matters.

The future isn't all-human or all-AI. It's knowing which tool fits which job.

Where We're Headed

The Zoo continues to evolve. Every week we expand what agents can handle reliably. The 80/20 split will shift—maybe to 90/10, eventually further.

But we don't expect it to reach 100%. The goal isn't to eliminate humans from the loop. It's to ensure humans spend their time on work worthy of human intelligence.

If your problem is routine, scalable, and well-defined—agents can likely handle it faster and cheaper than traditional teams.

If your problem is novel, ambiguous, or high-stakes—you want humans in the room.

If you're not sure which category you're in—let's talk.