AI engineering is not only about calling a model. It also includes context, tools, workflows, and safety boundaries.
AI Agent
An AI Agent can be understood as an execution layer between user goals and large language model capabilities.
It often includes:
- Understanding the task
- Planning steps
- Calling tools
- Reading results
- Adjusting strategy
- Producing the final answer
For example, a coding agent can search code, edit files, run tests, and explain errors.
MCP
MCP can be understood as a protocol for connecting models to external tools and data sources.
It lets models access:
- File systems
- Databases
- GitHub
- Design tools
- Enterprise knowledge bases
Its value is standardizing tool access instead of building custom integrations for every application.
Prompting
A prompt gives the model task instructions and context. A good prompt usually includes:
- Clear goal
- Input data
- Output format
- Constraints
- Examples
Prompting is not magic. It is mainly about making task boundaries explicit.
Function Calling
Function calling lets a model produce structured arguments that a program can use to call real functions.
Typical flow:
- User makes a request
- Model decides which function is needed
- Model generates arguments
- Program executes the function
- Model continues based on the result
This makes AI systems more controllable and easier to connect to business systems.