Research Agent
Collect selected sources, summarize them, compare arguments, and return a brief with references. Good for learning, analysis, and internal documentation.
Practice projects
Build small systems that teach the concepts.
The best way to learn agentic AI is to build. Start with a narrow workflow, measure the results, and only then expand the system.
Learning note: These project ideas are educational guidance only. If they do not fit your situation, ignore them and use the parts that make sense for your goals.
Code Assistant
Review a repository, explain a bug, suggest a fix, and validate the result through tests or static checks.
Planner Agent
Break a big goal into phases, tasks, risks, and checkpoints. This is useful for project planning and execution tracking.
How to build a research agent
Step-by-step example
Define the goal
Example: “Research Kubernetes ingress patterns and produce a short summary with risks, best practices, and practical recommendations for an internal platform team.”
Create the instruction file
Write a plain instruction file or agent prompt describing the role, objective, required output, and boundaries. Example: only use trusted sources, no invented facts, and provide 5 bullet points with risks.
Create the project files
Typical structure: agent.py for orchestration, tools.py for search or API calls, prompt.md for instructions, and config.yaml for settings and model names.
Add tools
Let the agent search the web, read internal docs, or access a knowledge base. It can use a search tool and a summarization tool, then combine the results.
Add a workflow
Research → collect sources → extract key facts → compare them → summarize → validate accuracy → return final brief.
Test and improve
Run it on real topics. If it hallucinates facts, improve the prompt and add stronger source validation rules.
Example file structure
my-project/
agent.py
tools.py
prompt.md
config.yaml
data/
notes.txt
runbook.md
Multi-agent workflow
One agent passes work to another
This pattern is often called an orchestration workflow or multi-agent workflow. A Research Agent gathers raw information. A Summarizer Agent converts it into a clean brief. A Validator Agent checks it for gaps, unsupported claims, and missing references.
Research Agent
Finds sources, reads documents, and extracts facts.
Summarizer Agent
Creates the summary, converts raw notes into structured output, and ranks the most important points.
Validator Agent
Checks that references are valid, that claims are supported, and that the answer is consistent with the original data.