The Agent-Native Company, Part 2: Designing the Agent-Native Organization

In Part 1: Beyond AI-Enhanced, we introduced the agent-native company – a company built around AI agents, not just using AI as a sidekick. Think of it like driver-assist vs. self-driving: in AI-enhanced orgs, humans steer with AI helping on the edges; in agent-native orgs, AI drives while humans guide the direction. Take away the AI, and these companies stop functioning.

It's not about replacing humans with AI, but redefining who does what – agents handle mundane work while people focus on what we do best. The question now is: how do you actually design such an organization?

In Part 2, we'll explore core design concepts of agent-native companies – what "agent-first thinking" means and how it changes business structure. We'll look at designing workflows and teams around AI agents (not just plugging bots into old processes). By the end, you'll start imagining how to build your own team differently with AI agents as collaborators from day one.

Designing with AI at the Core

The defining trait of an agent-native company is a mindset shift – Agent-First Thinking. Instead of asking "Where can we add AI to our current operations?" you ask "If an autonomous agent could handle this, how would we design the process from scratch?"

Agent-first thinking means treating AI agents as first-class actors in every system design decision. For decades, our software and processes assumed a human operator at every step. As our CEO Jeff Haynie said recently, nearly "100% of [past] software was built primarily for a human – interfaces, workflows, tools all assumed a human would be the developer, operator, maintainer." In agent-native orgs, that assumption flips.

We intentionally design products and internal tools for machine-to-machine interaction as much as human interaction. Humans are still involved, especially in "the last mile" of tasks, but starting from this assumption would drastically change almost any process.

Here are some company design concepts we're exploring:

More autonomous workflows

Design business processes so AI agents can execute whole tasks or entire workflows autonomously. Rather than humans orchestrating each step, humans set goals and guidelines, then agents carry them out.

For example, instead of manually compiling a weekly report, an agent-native approach assigns that outcome to an AI agent by default – the agent gathers data, produces the report, and only asks for human input if something falls outside its capability. This is a huge leap from adding an "AI helper" to a human-driven process.

Agents as Team Members (Not Just Tools)

Plan teams and roles that include AI agents. That could mean every employee has an AI "assistant" agent, or certain roles are fully owned by agents. We're discussing having new employees build the agent(s) they need for their job during onboarding.

When charting projects or org charts, you account for non-human contributors. These agents aren't managed like external services; they're "hired," onboarded, and evaluated like employees. Companies like Basis are already posting job listings for Agent Managers – people whose job is overseeing AI agents.

Embracing agents as coworkers demands new leadership skills. When your "team" includes non-human intelligence, you need new roles like prompt engineers and AI workflow designers. Management becomes coaching/oversight – Jensen Huang quipped that tomorrow's managers will be "HR managers for AI," recruiting good models, onboarding them into workflows, monitoring performance, and "firing" or retraining the ones that misbehave.

Agent-first thinking permeates company culture: you expect any new project or department will involve AI agents, and you organize accordingly.

AI fluency, a new human skill set

There's a new term I like: "AI fluency" – think emotional intelligence, but for AI. Knowing how to utilize AI and agents is different from managing humans. I've seen a big difference in work quality when teams are AI fluent.

Companies are formalizing what AI fluency means. Wade Foster, CEO of Zapier, recently shared how they measure AI fluency across four levels:

• Unacceptable: Resistant to AI tools and skeptical of their value
• Capable: Using popular tools with under 3 months of hands-on experience
• Adoptive: Embedding AI in personal workflows, tuning prompts, chaining models, and automating tasks
• Transformative: Using AI not just as a tool, but to rethink strategy and deliver value that wasn't possible before

The difference between someone who occasionally prompts ChatGPT versus someone who chains models together, builds custom workflows, and rethinks how work gets done is massive. In agent-native companies, AI fluency becomes critical – like knowing how to communicate with coworkers.

It's not just about using tools – it's understanding when to delegate to agents, how to design agent-friendly processes, and thinking in terms of human-AI collaboration rather than human-only workflows.

Optimized for a different kind of collaboration

An agent-native company's tech stack looks different. Systems are built to let agents observe, act, and learn with minimal friction. That means instrumenting software for agent access, robust data pipelines, and knowledge bases agents can draw from.

You need new layers of agent orchestration and monitoring. In traditional setups, human managers ensure team coordination; in agent-driven workflows, you might implement an "AI orchestrator" layer to make sure multiple agents work together smoothly. Some teams are exploring multi-agent systems where a "manager" AI agent dynamically delegates subtasks to specialized agents (researchers, coders, testers) and integrates results.

Your infrastructure should track what agents do (for safety, audit, and improvement) and allow them to improve over time. The IT backbone is built for AI autonomy and cooperation – like designing a workplace for effective human collaboration, but including non-human users.

Agentuity calls this "agentic operations" and "agentic learning": creating environments where agents can run things and self-optimize within guardrails.

Start from a blank slate (vs. bolt-on AI)

Don't shoehorn agents into old org designs – start fresh. Simply dropping AI into legacy processes treats it like fancy automation and misses transformative potential.

Revisit fundamental assumptions. In traditional customer service, you might add a chatbot to help reps – but an agent-first approach asks, "What if an AI agent owned tier-1 support entirely?" Suddenly the support team structure changes: you need AI oversight and new escalation procedures.

Many corporations are doing "pilot projects" attaching LLMs to workflows; those are fine incremental steps, but they're like early internet websites that were just brochure-ware. To truly harness AI, you must re-architect.

This might mean reorganizing departments, changing information flows, and adopting tools that make AI a stakeholder in every meeting. Agent-first thinking challenges job descriptions and org charts – but it's key to staying ahead.

As we said in Part 1: designing your organization from first principles with AI in mind is how you avoid being outpaced. Companies that thrive will be those that built their business around AI from day zero, not just added AI to existing business.

Workflows and teams, reimagined

What does this look like in practice? Let's contrast traditional organizations with agent-native ones:

Team Structure and Roles

A conventional startup might have 5 engineers, 2 product managers, 3 customer support reps – each handling defined tasks. An agent-native startup might have 3 engineers plus engineering AI agents that write code or run tests, 1 PM plus an AI project planner agent, and 1 support rep plus an AI agent handling first-line queries.

The org chart mixes humans and AIs. You might have the same headcount but higher output expectations. For example, new hires could be paired with personal AI onboarding agents from day one, which know the company knowledge base and handle tasks from drafting emails to updating documentation.

We've noticed this potential with Cognition's Devin – its wiki of all our repos is fantastic for onboarding engineers. New hires can ask Devin about parts of the system and get walked through how things work.

Workflow design

Consider sales: normally, an SDR manually researches leads, sends outreach emails, follows up, updates the CRM, schedules meetings. Adding AI might give the rep tools to draft emails faster or dashboards with AI insights. That's helpful, but still rep-centric.

An agent-native sales workflow: an AI agent automatically scans inbound leads, qualifies them, drafts personalized outreach, and only involves a human when leads respond wanting calls. The human salesperson handles high-touch conversations and closing. The agent does the heavy lifting by default.

This isn't a temporary experiment – it's permanent design. Support workflows might be built around AI triage agents integrated with knowledge bases and ticketing systems, with human experts as escalation points. These agents aren't just chatbots answering FAQs – they can take actions (refund orders, escalate to engineering) within guardrails.

Designing workflows around agents means automating hand-offs: the support agent creates bug reports for engineers when needed, or the sales agent schedules meetings automatically. Every hand-off that used to require one human emailing another can potentially be handled agent-to-agent.

Scaling control

One striking difference: how quickly you can scale functions without linear headcount growth. AI agents can be replicated at low cost, so small human teams can supervise large arrays of automated processes. One human manager might oversee ten AI agents each handling different market research projects.

Organizations stay lean because adding agents is faster than hiring and training employees. We experienced this firsthand – with just 6 people and a suite of AI agents (like Devin), we achieved in 8 weeks what felt like 14 months of product development work.

Agent-native structures are lean and loose by design. Less siloed because agents connect across functions, empowering individual employees with far more leverage through AI counterparts.

Culture

A subtle but powerful aspect: culture. When everyone – humans and AI agents – is considered part of "the team," it fosters collaboration across human-machine lines. Employees see AI agents as partners, not threats.

Daily stand-ups might review what AI agents did overnight. Company OKRs might include metrics owned by agents. There's an ethos of continuous learning where both humans and agents are constantly upgraded.

Because agents work 24/7 and handle rote tasks, human work culture can shift to prioritize creative work and problem-solving instead of firefighting mundane issues. Agent-native thinking can make companies more inclusive in decision-making – AI agents can crunch data or simulate scenarios to give voice to ideas that loud humans might overlook.

The organization becomes human-AI symbiosis. You still need strong human vision and leadership (AI won't set your mission or values), but much execution and ideation happens in partnership with machine intelligence.

This can be exhilarating – imagine brainstorming sessions where AI agents generate 50 design variations overnight, or product planning meetings where agents bring real-time user data analysis. When designed right, agent-native culture prizes experimentation and speed (since agents can iterate rapidly) and encourages people to "coach" their AI counterparts to better performance.

It's humans + machines vs. the problem, rather than humans vs. machines.

A question to leave you with

If you were starting your company today with unlimited access to AI agents, what is a role or process you'd design around an agent, first? What's stopping you from making that change right now?

Up next

Up Next – Part 3 Preview: So far, we've discussed the "what" and "why" of agent-native structures. In Part 3: Work and Roles in the Agent-Native Era, we'll dive into the human side of this paradigm shift. How do job roles evolve when AI agents take on so much work? What does your team look like when hierarchies flatten and "middle management" work is automated?

We'll explore agent orchestration as a key skill – essentially, how tomorrow's leaders will excel at directing swarms of AI collaborators. Get ready to rethink job titles, career paths, and the very notion of a "team" in the age of AI.