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AI Agent Beginner's Guide: From Concepts to Protocols

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AIAgentMCPArchitecture
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Agent vs Copilot

This is the key distinction for understanding the current forms of AI tools:

Agent (Driver)Copilot (Co-driver)
RoleAutonomous decision-making and executionAssists, follows instructions
BehaviorPerceive environment -> Plan -> Execute -> Achieve goalYou give instructions -> It helps complete the task
User RequirementsDefine the goal and boundariesNeeds prompting skills, domain knowledge, ongoing exploration
ProcessNon-deterministic, dynamic decisionsRelatively fixed, human-driven

Why We Need Agents

Models cannot answer every question. For instance, events after the training knowledge cutoff are simply unknown to the model.

The evolution path:

Pure LLM
  |
Combining APIs for answers (Compound AI System / Agentic System) -> Fixed Pipeline
  |
Agents -> LLM makes dynamic decisions based on task goals and environment; the process is non-deterministic

The key distinction: a Pipeline is a fixed workflow; an Agent is a dynamic workflow. An Agent decides what to do next in real time based on the current environment and task goals.

Agent Architecture Patterns

Single Agent

A single LLM instance that autonomously perceives the environment, plans, executes, provides feedback, and completes end-to-end tasks.

Multi-Agent

  • Master-Worker pattern: One master Agent handles planning and task dispatch, while multiple worker Agents execute specific subtasks
  • Peer collaboration pattern: All Agents can make decisions, determining who acts based on capability and context

Computer Use / Web Agent

The difference from traditional automated testing: traditional test instructions are written by humans, while Web Agent instructions are dynamically generated by the Agent. An Agent can operate browsers and desktop applications like a human, autonomously deciding the operation path based on objectives.

Protocol Layer: MCP and SGP

For AI Agents to work in practice, they need to interface with external resources and services. There are two key protocol directions:

MCP (Model Context Protocol)

The problem it solves: How to integrate local computer resources?

  • RPA (Robotic Process Automation)
  • Local documents (Local Doc)
  • Local software (Local Software)

MCP enables Agents to call local capabilities without routing everything through the cloud.

SGP (Standard Gateway Protocol)

The problem it solves: How to connect remote services?

  • API services
  • Remote models

SGP unifies the interaction protocol between Agents and remote services.

Implementation Strategy and Business Opportunities

Architecture-Level Opportunities Belong to Small Businesses

Data processing architecture + systems engineering + application paradigms are uncertain – this is where small entrepreneurs have opportunities, filling gaps that models alone cannot handle. The bottom layer consists of numerous models. But model and algorithm paradigms are certain – that is what big companies do.

A notable finding: large models distilled into small models perform worse than small models trained from scratch.

A New Paradigm for Personalized Recommendations

Implementing personalized recommendations through Agents rather than traditional search/recommendation algorithms:

  • Models handle knowledge and general awareness
  • On-device processing for privacy without cloud dependency (1B parameters, 300-500M memory)
  • 25ms search response time; models suit asynchronous IM scenarios
  • Hyper-personalization: every user sees a different UI

Pick Vertical Scenarios, Don’t Spray and Pray

Don’t try to do everything at once. Choose vertical scenarios and go deep.

Pipeline Thinking

Don’t be constrained by paradigms; instead consider what it can do and experience it firsthand:

For example: use a model to identify intent -> use a cutout tool for image segmentation -> use other tools for subsequent processing. This is a Pipeline where data + engineering + algorithms can work as a full stack. Capability doesn’t need to be 100%; 50% is OK.

Text as a Service

FaaS is a service, Serverless is a service, and text can also be a service:

Push me five AI news items every day, English only and meeting certain criteria, then execute a task to publish to a public account – this can become a service. Implement the code yourself and it becomes a service. Code is productivity.

Business Scenario Examples

  • Public sentiment and comments: Extracting key signals buried in massive amounts of information
  • Infrastructure tools: All usable; the key is the solution and its future-readiness

Practical References

fastrtc

A real-time communication framework suitable for building voice/video Agents: fastrtc.org/cookbook

References