Free AI Agents & Automation Course with Certificate
Free self-paced course on AI agents and workflow automation: triggers, tools, memory, orchestration, no-code builders, evaluation, cost control, security and deployment. Flash cards, quizzes and a verifiable certificate.
A workflow runs a fixed sequence you defined. An assistant answers when a human asks. An agent is given a goal and chooses its own steps and tools until it decides it's done. Capability rises left to right — and so does unpredictability, cost and blast radius when it goes wrong.
Use the least autonomous option that solves the problem.
Write down a task you want automated and classify which of the three it truly needs.
Every expert you admire learned it exactly this way — one scene at a time.
What you will be able to do
- Tell the difference between a workflow, an assistant and an autonomous agent — and pick the right one
- Design automations with clear triggers, actions, error paths and idempotency
- Give agents tools and structured function calls with sane permissions
- Choose and manage agent memory without wasting context or leaking data
- Orchestrate multi-step and multi-agent systems that stay debuggable
- Build with no-code platforms and know when to graduate to code
- Evaluate agents on task success, cost and latency rather than demos
- Deploy with logging, approvals, rate limits and a kill switch
Course structure
Foundation — Automation thinking before agents
Triggers, actions, data shapes and failure paths: the unglamorous fundamentals that decide whether an agent survives contact with reality.
Advanced — Building agents that finish the job
Loops, memory, retrieval, multi-agent patterns and no-code delivery — with the failure modes that eat beginners.
Mastery — Reliability, cost and production ownership
Evaluating agents on task success, controlling spend, staying compliant, and running automations people can trust.
Full syllabus — every topic covered
30 topic cards across three levels. Each card teaches one idea, ends with a takeaway, and is followed by a checkpoint quiz every five cards.
Level 1: Foundation — Automation thinking before agents
Triggers, actions, data shapes and failure paths: the unglamorous fundamentals that decide whether an agent survives contact with reality.
- 1. Workflow, assistant, agent: three different things — Use the least autonomous option that solves the problem.
- 2. Every automation starts with a trigger — Name the trigger first — everything downstream depends on it.
- 3. Actions, side effects and idempotency — Any action with a real-world side effect needs an idempotency key.
- 4. Data shapes hold the whole thing together — Validate at every boundary; never trust the previous step's output blindly.
- 5. Design the failure path first — Every step needs an answer to 'what if this fails?'
- 6. Where the AI step actually belongs — Use AI for judgement and language; use code for logic and maths.
- 7. Tools: how a model touches the outside world — The model requests a tool call; your code decides whether to run it.
- 8. Good tool design is half of agent quality — Tool descriptions are prompts. Write them as carefully as one.
- 9. Human approval gates — Automate the preparation; gate the irreversible action.
- 10. Start with one workflow, measured — Ship one measured workflow before designing a platform.
Level 2: Advanced — Building agents that finish the job
Loops, memory, retrieval, multi-agent patterns and no-code delivery — with the failure modes that eat beginners.
- 1. The agent loop: think, act, observe — Every agent needs a maximum step count and an explicit definition of done.
- 2. Planning before acting — Plan, execute, re-plan on failure — and keep the plan visible.
- 3. Memory: short-term, long-term and none — Memory is a design decision: decide what to keep, in what form, for how long.
- 4. Vector search and when you don't need it — Use vector search for fuzzy meaning at scale, not for exact lookups.
- 5. Multi-agent patterns — and their tax — Add an agent only when a single one demonstrably can't do the job.
- 6. Building it in no-code — No-code for speed and handover; code when state, testing or scale bites.
- 7. Rate limits, queues and backpressure — Queue and throttle by default — bursts are normal, not exceptional.
- 8. Security: permissions and the confused deputy — Least privilege plus full tool-call logging is the baseline, not the bonus.
- 9. Debugging with traces — If you can't replay the run, you can't fix the agent.
- 10. Handover: making it someone else's to run — Ship the runbook and the off switch with the automation.
Level 3: Mastery — Reliability, cost and production ownership
Evaluating agents on task success, controlling spend, staying compliant, and running automations people can trust.
- 1. Evaluate agents on task success, not vibes — A scenario suite with pass/fail criteria is the difference between a demo and a product.
- 2. Regression testing non-deterministic systems — Run scenarios repeatedly and compare pass rates over time.
- 3. Cost engineering for agent loops — Optimise cost per successful task; steps and resent context are the biggest levers.
- 4. Latency and the human experience — Parallelise what you can, stream the rest, and always show the current step.
- 5. Guard rails that hold — Enforce limits in code; prompts are policy, not enforcement.
- 6. Compliance, records and consent — Know which regime your data falls under before you automate outreach.
- 7. When automation is the wrong answer — Automate a good process; never automate a broken one.
- 8. The economics you can defend — Quote payback period including maintenance, and check it again in 90 days.
- 9. Rolling out without breaking trust — Shadow first, expand on evidence, communicate throughout.
- 10. The durable skill set — Portfolio plus measured outcomes beats tool-name trivia in every interview.
Frequently asked questions
- What is the difference between automation and an AI agent?
- A classic automation follows a fixed path you designed. An AI agent decides which steps and tools to use at run time to reach a goal. Agents are more flexible and far less predictable, so they need tighter guard rails.
- Do I need to code to complete this course?
- No. The course teaches the design thinking and shows both no-code builders and code-based patterns. You can pass the final test without writing code.
- How long is the course?
- About 9 hours across Foundation, Advanced and Mastery. Most learners finish within two weeks.
- Which tools does it cover conceptually?
- No-code orchestration platforms, function calling in model APIs, vector search, schedulers and webhooks. Principles are tool-agnostic so they survive product changes.
- How does the certificate work?
- Pass the final test at 70% or above — two attempts per week — and you receive a Moyan AI certificate showing the course duration, with a unique verification ID and QR code you can share to LinkedIn.