Agents Explained: Concepts, Workflows and Tools
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in late 2024 and now supported by every major AI vendor, that lets language models talk to tools, files and APIs through a single shared interface. Before MCP, every AI product had to build custom plugin systems and every tool vendor had to write a separate integration for ChatGPT, Claude, Cursor and each new client. 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 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.
- How is MCP different from an OpenAI plugin or a Claude tool call?
- Plugins and tool calls are proprietary and tied to one vendor. MCP is a shared protocol: one MCP server works with Claude, ChatGPT, Cursor, VS Code, custom agents and anything else that speaks MCP.
- Do I need to run my own MCP server?
- Only if you want to expose your own data or tools. Public MCP servers already exist for GitHub, Postgres, Notion, filesystems and hundreds of SaaS tools.
- Is MCP secure for internal data?
- MCP transport is transport-agnostic (stdio, HTTP, SSE) and defers auth to the transport. Self-hosting a server behind your VPN with scoped credentials keeps data inside your perimeter.
- Are computer-use agents safe to run on my own machine?
- Best practice is to run them inside a sandboxed VM or container with scoped credentials and no access to sensitive data. Do not point one at your primary workstation.
- How reliable are computer-use agents in 2026?
- Reliable for short well-scoped tasks (5–20 steps). Long-horizon tasks still drift and need checkpoints, retries or human review at key steps.
- When should I use a computer-use agent instead of an API integration?
- When the target app has no API, the API is worse than the UI, or the workflow spans several apps and building custom glue would cost more than tolerating a slower agent run.