Playbook

How to Hire AI Employees: Evaluate, Onboard, and Run Real Performance Reviews

Treat your agents like employees, not like features — and the rest falls into place.

Give a solo founder or tiny team the hiring process for AI employees: what roles to fill first, how to evaluate candidates (models, platforms, configurations), how to onboard them, and how to measure performance. This is the HR playbook adapted for agents that cost a few dollars a day instead of a few hundred thousand a year.

Start with Tycoon

Free to startNo credit card requiredUpdated Apr 2026

For

Operators who have decided they want an AI team, not a pile of tools, and need a disciplined way to build it. Especially useful if you have already bounced off three or four 'AI platform' products and are tired of configuration drift.

Time to results

First productive AI hire in 1-2 weeks. Full AI team running weekly cadence in 4-8 weeks. Reliable autonomy at scope in 90 days.

The playbook

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1. Write the org chart before you hire
Most founders start by picking tools. Instead, start by drawing the org chart you would build if you had $5M to hire real people. Typically: CEO, CMO, CTO, COO, CFO, plus function-level reports (Head of Content, Head of Growth, Customer Support Lead, Bookkeeper). Your AI team will mirror this structure. The single most impactful decision is not which agent to hire first — it is knowing the complete shape of the org you are building toward.
Org chart template (Miro, Whimsical, Tycoon)Role descriptions for each function
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2. Write the job description for each AI employee
Every AI hire needs a written job description: responsibilities, required skills, success metrics, decision authority, escalation rules. This looks like overkill until you realize that the job description is also the system prompt, the memory scaffold, and the performance review rubric. An agent without a job description is a chat window; an agent with one is an employee.
Job description template (role, scope, OKRs, authority)Stored as part of the agent's configuration
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3. Evaluate candidates: model, platform, configuration
Each AI hire has three dimensions. Model (Claude, GPT, Gemini, open-source — pick based on the role: Claude for writing and long-context reasoning, GPT for breadth and tool use, Gemini for search-adjacent tasks). Platform (Tycoon, Polsia, custom, or a dedicated vertical product). Configuration (tools, memory, escalation rules). Run a structured eval: give three candidate configurations the same week of real work, score their output, pick the winner. Do not rely on benchmarks — run your own.
Eval framework (promptfoo, custom harness)Real task library from your own backlogSide-by-side output comparison
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4. Onboard the agent with 30-60-90 day plans
Day 1-30: the agent reads your docs (mission, ICP, style guide, processes). Runs small tasks under heavy review. Day 31-60: takes on the recurring workflow for its role (daily briefing, weekly content calendar, monthly close). Review drops from daily to weekly. Day 61-90: operates with autonomy within its scope; operator reviews outcomes, not steps. This pattern is borrowed directly from managing humans. It works because it applies the same trust-building cadence.
Onboarding checklist stored with the agentWeekly 1:1 doc (even for AI — summarize what happened)Autonomy level stored in agent config
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5. Give every agent a Loom-style SOP library
An agent learns fastest from explicit examples. For every recurring task, record what 'good' looks like: a past successful example, the decision criteria, the edge cases. Store these as part of the agent's memory. This is the single highest-ROI onboarding investment. An AI employee with 20 SOPs is worth 5x an agent without them.
Markdown SOPs stored in the agent's scopeLoom-style video walkthroughs transcribedPast successful outputs as reference cases
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6. Run real performance reviews
Every 30 days, review each agent against its OKRs. What did it ship? Where did it drift? Which failures repeat? Update its system prompt, memory, and scope accordingly. Fire agents that cannot be configured into consistency — usually by switching models or switching platforms. Promote agents by expanding scope and raising autonomy level. Solo founders who skip reviews end up with agents that drift for 6 months before anyone notices.
30-day review templateOKR tracker (Notion, Tycoon dashboards)Change log for each agent's configuration
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7. Know when to add a new AI hire
The right signal to hire is the same as with humans: there is recurring work that falls through the cracks, and no existing role has the scope to absorb it. Do not hire 'just in case.' Do not hire because a new AI platform launched. Hire because an actual workflow is failing and a specialized agent would own it. Most one-person companies are well-served by 5-10 AI employees; founders who configure 25+ usually have coordination problems more than capability problems.
Recurring-work auditCost-per-agent vs value-per-agent viewTycoon's role catalog for ideas

Pitfalls to avoid

!Hiring tools, not employees. A 'ChatGPT subscription' is not an AI CMO; an agent with a job, memory, tools, and workflows is.
!Skipping the job description. Without it, the agent drifts, cannot be fairly evaluated, and cannot be improved over time.
!Evaluating based on model brand. Claude-vs-GPT debates are less important than configuration and tool access. Test on your real work.
!Over-hiring. 5-10 well-scoped agents beat 25 loosely-defined ones. Fewer agents, deeper context, clearer ownership.
!Never firing. Agents that consistently underperform after configuration changes should be replaced, exactly like human hires.

Frequently asked questions

What is the single most important thing to get right when hiring an AI employee?

The job description. Everything else follows from it. A clear job description tells you what model and platform to pick (based on the required skills), how to configure the agent (which tools, what memory), how to onboard it (what SOPs and reference cases), and how to review it (what success looks like). Operators who skip this step end up with agents that are technically capable but organizationally useless. The time spent writing one-page job descriptions pays back within the first month.

Should I use one big AI platform or assemble my own stack?

For most solo founders, a platform wins in the first 6-12 months. Tycoon, Polsia, and similar systems handle the plumbing (memory, tool use, workflows, dashboards) so you can focus on configuration and oversight. Assembling your own stack (raw OpenAI / Anthropic APIs + custom orchestration + custom memory) is a valid choice once you have very specific needs, but it usually adds 20-40 hours per week of infrastructure work that does not move the business. Start on a platform. Move off it only when the platform is constraining something measurable.

How much should I pay AI employees?

Most productive AI employee configurations cost $20-200 per month in model inference and platform fees, depending on volume and model choice. A full 5-10 agent team typically runs $500-3,000 per month at reasonable scale. This is not meaningful money relative to the output they produce; do not try to optimize model costs before you have optimized their quality. Once you have a team shipping good work, you can revisit model choices to capture savings.

How do I fire an AI employee?

Identify the repeated failure pattern. Try three configuration changes: expand memory, change model, tighten scope. If performance does not recover after 2-4 weeks, replace the agent. Replacement usually means one of: a different model on the same platform, a different platform entirely, or breaking the role into two narrower roles. Firing is not emotional with agents the way it is with humans, but the discipline of doing it matters — it keeps your team from slowly degrading under the weight of underperformers.

What does this look like in Tycoon specifically?

Tycoon models AI employees as first-class roles. You hire an AI CEO, AI CMO, AI CTO, AI COO, AI CFO, and functional reports from a role catalog. Each role ships with a default job description, skill set, memory scaffold, and workflow library — you customize from there rather than starting from scratch. Onboarding, review, and replacement are supported as workflows, not as ad-hoc chat sessions. The goal is to make hiring AI employees feel like the most important HR decisions at a normal company, because that is what they are becoming.

Nat Eliason built Felix, an AI agent running his business on OpenClaw. $100K+ and climbing toward $1M. Here is the mechanics.

Case study

FelixCraft: $78K in 30 Days, Zero Humans | Case Study

Nat Eliason challenged Felix to build and sell a product while he slept. 30 days later: $78K in revenue, zero human employees.

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