AI Search for Agents – Find, Evaluate and Use the Right Information

Why this course matters

Giving an AI system web access does not make its answer reliable. A search agent needs a defined information need, source policy, freshness requirement, citation rule and path for conflicting or insufficient evidence. Without those controls, faster research may simply produce errors faster.

The course is designed for analysts, consultants, communicators, business developers and other knowledge workers. It suits people who already use generative AI and want a more structured, traceable and economical way to work. You do not need to be a developer, but you must be willing to review outputs and follow your organisation’s rules for data, tools and publication.

What you will do

You distinguish web search, retrieval, grounding and RAG. You then create a search plan, source policy and copyable instruction. Five scenarios test current facts, internal information, conflicting sources, missing evidence and prompt injection in retrieved material.

Each micro-lesson addresses one clear problem and ends with a decision, assessment or practical artifact. You progress from definitions and scope to instructions, testing, economics and a 30-day pilot. Your work is saved so that you can pause and continue.

Micro-lessons without superficial learning

The course is composed of focused five-to-nine-minute sections. This makes it easier to complete alongside everyday work, but the content is not reduced to quick tips. Every section has a learning outcome and contributes to the final artifact. The accurate description is therefore a short specialist course built from micro-lessons—not one single microlearning unit.

What “without code” means

The first research assistant can be built without programming as a reusable, human-controlled workflow. APIs, vector databases and automated retrieval should be added only when volume, access requirements and measured quality justify a more technical design.

No-code describes how the prototype can be implemented; it does not remove responsibility. Approved sources, access, human checkpoints, versions, tests and maintenance still need to be defined. The course recommends starting manually and adding APIs or automation only after quality and volume have been verified.

You leave with

• ✓a plan for web and internal search
• ✓a source policy with freshness and priority rules
• ✓a copyable research-agent instruction
• ✓five tests and an evaluation table
• ✓a cost estimate and 30-day pilot

The final knowledge check contains eight questions and requires seven correct answers. A printable completion certificate becomes available after passing. It confirms completion and is not an accredited professional certification.

Frequently asked questions

Do I need to know how to code?

No. The course uses instructions, templates and visually planned workflows. Technical specialists become necessary when production integrations, advanced authorization or risky automatic actions are introduced.

Is this a micro-course?

It is a short specialist course composed of micro-lessons. That is more accurate than describing the complete 50–60-minute course as one microlearning unit.

Will I build a fully autonomous agent?

No. You build or design a controlled workflow. A person initiates, reviews and approves outputs or external actions.

Which AI tool do I need?

The course is as platform-neutral as possible. Use a tool approved by your organisation and compare quality on your own task. Check current features, prices and limits in official documentation.

What should I take next?

The recommended next step is No-Code AI Agent Development.