Agents in Practice: Stack, Prompts and Use Cases
The model-to-system interface that lets an LLM trigger external actions. Tool Calling is the pattern where an LLM selects a predefined function, fills its arguments, and hands execution to software. This guide pulls together everything on Onexial tagged agents — 22 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind Agents
Before wiring anything together, the vocabulary has to be precise. These 15 definitions cover the terms that show up in almost every Agents discussion — each one links to a full entry with an example and its own connections inside the graph.
Tool Calling
The model-to-system interface that lets an LLM trigger external actions.
Agent Memory
Persistent context that lets agents retain preferences, decisions, and prior work.
MCP (Model Context Protocol)
Open protocol that lets LLMs connect to tools, data sources and apps through a standard interface.
Agentic RAG
RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query.
Agentic Workflow
A workflow where an LLM decides the next step instead of a hard-coded path.
Tool Use
An LLM invoking external functions to act beyond text generation.
Long-Term Memory
Persistent state an agent carries between sessions.
Episodic Memory
Session-scoped memory of specific past events.
Planner-Executor Pattern
Splitting agents into a planner LLM and a smaller executor LLM.
Reflection
An agent critiquing and revising its own output before returning.
Function Calling
The API mechanism that lets a model request a tool call.
MCP (Model Context Protocol)
Open standard letting AI clients call external tools, data and prompts.
Workflows: how Agents runs end to end
Concepts only matter once they become a repeatable process. Below are 2 documented workflows that apply Agents to a concrete problem, with the steps, the tools involved and the variations worth testing.
Build an MCP Server for Your Internal Data
Expose one internal data source to every AI client via the Model Context Protocol.
Automate Repetitive Ops Work With a Computer-Use Agent
Replace 5 hours/week of tab-switching with a supervised computer-use agent.
Trade-offs and comparisons
Most Agents decisions are trade-offs rather than right answers. These 3 comparisons break down the real differences, when each option wins, and the recommendation for the common case.
OpenAI vs Anthropic for Agents
Which provider builds better production agents in 2026.
CrewAI vs AutoGen vs LangGraph
Three approaches to multi-agent orchestration.
Claude Code vs Cursor: Which Coding Agent to Use
Terminal-native agent vs IDE-native pair programmer — different jobs, both worth using.
Prompts you can reuse
Prompts are reusable components. Each of these 1 prompts is written to be dropped into a Agents workflow with minimal editing, including the context it expects and an example output.
Real applications of Agents
Finally, 1 applied use cases: the situation, the system used to solve it, and the outcome. This is the layer that turns Agents from an idea into leverage.
Frequently asked questions
- What is Tool Calling?
- Tool Calling is the pattern where an LLM selects a predefined function, fills its arguments, and hands execution to software. It is the bridge between language reasoning and reliable system action.
- What is an example of Tool Calling?
- A support assistant decides to call get_order_status with an order ID instead of guessing the delivery date from conversation context.
- Why does Tool Calling matter for AI and automation?
- The model-to-system interface that lets an LLM trigger external actions. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What is Agent Memory?
- Agent Memory is the stored state an AI system can retrieve across sessions: user preferences, previous outputs, project facts, operational rules, and feedback loops.
- What is an example of Agent Memory?
- A research agent remembers the company ICP, preferred competitor categories, and rejected sources before generating the next market scan.
- Why does Agent Memory matter for AI and automation?
- Persistent context that lets agents retain preferences, decisions, and prior work. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What does MCP actually stand for?
- Model Context Protocol. It is an open standard for exposing tools, resources and prompt templates to AI models over a JSON-RPC channel.
- Who created MCP?
- Anthropic released the specification in November 2024. OpenAI, Google, Microsoft, Cursor and most agent frameworks have since adopted it.