Core Concepts

Master the fundamental concepts that underpin agents and MCPs.

Agent Lifecycle

An agent goes through several stages in its lifecycle:

1. Initialization: Agent is created with system prompt and configuration
2. Context Building: Initial context and tools are provided to the agent
3. Message Processing: Agent receives and processes messages
4. Decision Making: Agent determines appropriate actions or responses
5. Response Generation: Agent formulates and returns a response
6. State Update: Context and memory are updated based on the interaction

Messages and Communication

Agents communicate through a message-based interface:

• User Message: Input from the user to the agent
• Assistant Message: Response from the agent
• System Message: Guidance for how the agent should behave
• Tool Message: Response from a tool that the agent called

Context and Memory

Context is the information an agent maintains about a conversation:

• Immediate Context: Current conversation history
• Long-term Memory: Persistent information across sessions
• Working Memory: Temporary data for current task
• Session State: User preferences and interaction history

Tools and Functions

Tools extend agent capabilities:

• API Integration: Connect to external services
• Data Access: Query databases or files
• Computation: Perform calculations
• Real-time Updates: Stream live data

Model Context Protocol (MCP)

MCP standardizes how agents interact with tools:

• Resource Protocol: Access to files, databases, APIs
• Tool Protocol: Standardized tool definition and calling
• Sampling Protocol: Structured model inference
• Server Model: Tools run as separate services

Agent Types

Different agent architectures serve different purposes:

• Reactive Agents: Respond directly to inputs
• Planning Agents: Create plans before acting
• Collaborative Agents: Work with other agents
• Specialized Agents: Focus on specific domains

Next Steps

Dive deeper into Designing AI Workflows to see how to structure complex agent interactions.