AI Agents
Autonomous agents: ReAct, planning, tool use, function calling, multi-agent.
What is an AI Agent?
An AI agent is an LLM-powered system that can autonomously **perceive, reason, plan, use tools, and act** to reach a goal, adjusting its strategy based on intermediate results. LLM = brain. RAG = brain with reference library. Agent = brain + reference library + hands + decision loop.
Building Blocks of AI Agents
Effective agents are built on six building blocks: **role-playing, focus, tools, cooperation, guardrails, memory**. Skip any one and your agent drifts, hallucinates, or loops. These map directly to framework primitives (CrewAI roles, LangGraph state, OpenAI function calls).
Function Calling (tool use primitive)
Function calling is the primitive that turns an LLM into something that can act. You describe tools as JSON schemas. The LLM decides when to call them and with what arguments, returning a structured function call you execute in your code. The result goes back to the LLM. This is the atom of every agent.
ReAct Pattern (Reason + Act)
ReAct is a prompting loop that combines reasoning with tool use. The LLM alternates **Thought** (plan), **Action** (call a tool), **Observation** (tool result), over and over, until it produces an **Answer**. It is the foundational agent pattern: CrewAI, LangGraph, and many others default to it.
Agent Memory
Memory turns a stateless LLM into a stateful agent. Short-term memory = conversation window. Long-term memory persists across sessions. Episodic = past events. Semantic = learned facts. Procedural = learned how-to. Without memory, every interaction is a blank slate. With memory, agents personalize, learn, and accumulate knowledge.
Agentic Design Patterns (5 core + 7 multi-agent)
Daily Dose DS lists 5 core agentic patterns (Reflection, Tool Use, ReAct, Planning, Multi-Agent) and 7 multi-agent orchestration patterns (Parallel, Sequential, Loop, Router, Aggregator, Network, Hierarchical). Real systems mix them. Picking the right pattern determines cost, latency, and reliability.
Agent Protocols (MCP, A2A, AG-UI)
Three protocols standardize how agents plug into the world. **MCP** (agent-to-tool, Anthropic 2024), **A2A** (agent-to-agent, Google 2025), **AG-UI** (agent-to-user, CopilotKit 2025). They are not competing, they are complementary layers of the same stack. Learn the three, choose which ones fit your app.
Agent Levels, Architecture Layers, Deployment Strategies (+ glossary)
Three complementary frames for understanding agentic systems: 5 levels of autonomy (who controls the flow), 4 layers of the stack (LLM -> Agent -> Multi-agent -> Infrastructure), 4 deployment patterns (batch, stream, real-time, edge). Plus a 30-term glossary so you stop confusing Orchestration with Routing.