Working With AI Employees

Who this course is for

Founders, operators, marketers, and anyone new to AI. Beginner friendly · no code.

Course format

9 modules, about 60 minutes of self-paced learning, and free access.

Course curriculum

• Module 01: Meet Your AI Employee — What an AI employee actually is, in plain English, and the words everyone uses: agent, model, LLM. 6 min read.
• Module 02: Delegation: What to Hand Off — The first habit of working with AI is deciding what to delegate: which tasks are ready, and treating it like a new hire not a search box. 6 min read.
• Module 03: Description: Asking for Great Results — How to describe what you want so it lands the first time: briefs, prompts, examples, and thinking out loud. 7 min read.
• Module 04: How It Thinks: Context, Tokens, Reasoning — What happens between your message and the answer: the context window, tokens, reasoning, and why deeper thinking costs more. 7 min read.
• Module 05: What It Knows: Memory, Training, Knowledge — How an AI employee remembers and learns your business, and why it sometimes makes things up. 7 min read.
• Module 06: What It Can Do: Tools, Integrations, MCP — What lets an AI employee take real action: tools, integrations, and MCP, the plain version. 7 min read.
• Module 07: Shaping a Specialist: Skills and Duties — The human words and the tech words side by side: skills, tools, and duties, and how to shape an employee around your work. 6 min read.
• Module 08: Putting It to Work: Automation and Teams — From one task to a working system: delegation, scheduling, autonomous work, and teams of agents that hand off to each other. 7 min read.
• Module 09: Trust, Quality and Responsible Use — Stay in control as it does more: judge the output, measure quality, keep approvals, and use it safely and responsibly. 7 min read.

What you will learn

• An AI employee is an AI agent: software you delegate a job to, and it does it.
• Its brain is an LLM, which predicts text; memory and tools turn that into a worker.
• A weak result is usually a briefing or setup problem, not "the AI is dumb".
• "Agentic" just means it can take actions and work over steps, not only chat.
• Start with automation candidates: repetitive, rules-based, text and data work.
• Keep judgment, relationship, and high-stakes calls with yourself for now.
• Delegate like a manager: give the goal, the context, and the standard, not the clicks.
• Stay human-in-the-loop for anything irreversible or sensitive, and let the rest run.
• A prompt is your request plus its context; rich context beats a terse order.
• One or two examples (few-shot) steer the output harder than more instructions.
• For multi-step work, asking it to reason first (chain-of-thought) improves the answer.
• The system prompt is the always-on brief that sets its role and rules, set once.
• Better results usually come from a better description, not a "smarter" AI.
• The context window is its working memory; once full, the oldest details fall out, so restate what matters.
• Everything is counted in tokens, and both the memory limit and the price scale with how much text you give it.
• Reasoning means thinking through more steps before answering: better on hard tasks, but slower and pricier.
• Match the effort to the job, and pick the right model for the task rather than always reaching for the heaviest one.
• Answers come from three places: general training, memory of you, and documents you provide.
• Training is fixed and general; it does not know anything recent or specific to you on its own.
• RAG means it looks things up in your files first, so it answers from your facts, not a guess.
• A "hallucination" is a confident wrong answer that fills a gap in what it knows.
• Grounding it in good documents and asking for sources is how you keep answers honest.
• A tool is one action the AI employee can call; the model choosing one is "function calling".
• An integration links it to your real apps through their API, so it works inside your stack.
• MCP is an open standard: one consistent way to connect many tools and data sources.
• More of what an AI employee can do comes from this wiring, not from the model alone.
• A tool is an action, a skill is packaged know-how, a duty is a standing responsibility.
• Save how you want recurring work done as a playbook so you stop re-explaining it.
• Shape behaviour with instructions and examples (configuration), not retraining (fine-tuning).
• Reach for configuration first; fine-tuning is the rare exception, not the starting point.
• Automation turns a task you keep repeating into work that runs by itself.
• A trigger is the signal that starts it: a schedule, or an event like a new lead.
• An autonomous agent keeps working between check-ins, inside the limits you set.
• Set the boundaries once: what it can do alone, what it must ask about first.
• Orchestration coordinates a multi-agent team, where a lead hands work to subagents and combines the results.
• Your job shifts from doing the work to judging it, so read output critically and verify what matters.
• "Discernment" is not being fooled by fluent writing; check sources and key facts before you trust them.
• An "eval" measures quality against a clear standard or examples, so good is something you can see.
• Human-in-the-loop and guardrails keep approvals on risky actions and you in control as it does more.
• Use it responsibly: guard personal data (PII), watch for bias, and keep an eye on cost.

Your instructor

Mahmoud Zalt, Founder and engineer, Sistava. Mahmoud builds AI employees every day, and created open-source tools used by hundreds of thousands of developers, including Laradock and Apiato. He teaches this the way he wishes someone had taught him: plain, honest, and practical.

Free AI certificate

Finish the course, pass the final quiz, and earn a free certificate of completion.

• Take the final quiz and earn a certificate

FAQ

Who is this course for?

People who run a business and want to work with AI employees without learning to code: founders, operators, marketers, agencies, and anyone new to AI.

What will I learn in this course?

How an AI employee works, how to delegate and brief it, tokens and context, memory and RAG, tools and MCP, skills and customization, automation and teams, and how to stay in control. Nine short modules.

Do I need any background or technical knowledge?

None. It starts from zero and explains each official technical term as it appears.

Is the course free, and do I get a certificate?

Yes. It is free to read, and you finish with a certificate of completion.

How long does the course take?

About an hour in total, self-paced, split into short modules you can do in one sitting or spread out.

Does this only apply to Sistava?

No. The skills carry over to any AI employee or agent. Sistava is simply an easy place to practice what you learn.