AI Agents: AI Systems That Act Independently

A chatbot reacts. An AI Agent acts. The difference lies in four capabilities:

An AI Agent is based on a Large Language Model like Claude or GPT, but goes beyond it: it uses the LLM as a "reasoning engine," combines it with tool access and a planning loop.

The concept is more broadly described as Agentic AI — an AI Agent is the concrete implementation of an agent-based system.

Every AI Agent consists of four components:

1. LLM (Reasoning Engine): The language model that understands tasks, creates plans, and makes decisions. Claude Sonnet is a commonly used model for agent applications.

2. Tools: Tools the agent can use — APIs, databases, file systems, web search. Via the Model Context Protocol (MCP), these are provided in a standardized way.

3. Memory: Working memory for the current task (short-term) and learned knowledge from previous tasks (long-term). Often implemented via vector databases.

4. Orchestration: The control logic that manages the flow: accept task → plan → execute step → check result → plan next step.

The architecture follows a ReAct pattern (Reasoning + Acting): Think → Act → Observe → Think.

LangGraph (LangChain): The de facto standard for complex agent workflows. Graph-based control, large community, good documentation. Ideal for businesses that need maximum control over the workflow.

CrewAI: Specialized for multi-agent systems. Multiple agents with different roles work together — e.g. a research agent, a writing agent, a QA agent. Easy onboarding.

Anthropic Agent SDK: Anthropic's own SDK for Claude-based agents. Deep integration with MCP and Claude-specific features like tool use and long context windows.

AutoGen (Microsoft): Framework for multi-agent conversations. Agents communicate with each other and solve tasks collaboratively.

Custom builds with MCP: For simple agents, an LLM with MCP connectivity often suffices — no additional framework needed. arocom evaluates for each project whether a framework adds value or introduces unnecessary complexity.

1. Content pipeline agent: Receives a topic, researches facts and competitors, creates an SEO-optimized draft, checks internal linking, and prepares the text for the CMS.

2. Support triage agent: Analyzes incoming support tickets, categorizes by urgency and topic, suggests solutions from the knowledge base, and escalates to human staff when needed.

3. SEO audit agent: Crawls your website, checks technical SEO, content quality, and internal linking. Delivers a prioritized action plan with concrete recommendations.

4. Data extraction agent: Processes unstructured documents (PDFs, emails, contracts), extracts structured data, and passes it to your CRM or ERP.

5. Website monitoring agent: Regularly checks availability, performance, and content of your website. Reports problems proactively with context and suggested solutions.

A solid introduction to the topic:

AI Agents with tool access require clear security architecture:

Principle of Least Privilege: An agent receives only the permissions it needs for its task. A content agent can read and write, but not manage user accounts.

Human-in-the-Loop: For critical actions (modifying data, sending emails, publishing), a human must confirm. The agent suggests, the human decides.

Audit Trail: Every agent action is logged: which tool, which data, which result. Full traceability.

Sandbox Testing: New agents are tested in a sandbox environment before accessing production data.

Rate Limiting: Limiting API calls and actions per time period. Prevents an agent from entering an infinite loop on error.