Agents

An AI agent is a system that uses an LLM to plan, use tools, and complete tasks.

Simple idea: an agent is an AI worker with tools and rules.

Why It Matters

Chatbots mainly answer. Agents can act.

Examples:

• Search documents.
• Call APIs.
• Create tickets.
• Update records.
• Send draft messages.
• Run checks.
• Ask for approval before risky actions.

Basic Agent Loop

1. Understand the goal.
2. Decide the next step.
3. Use a tool if needed.
4. Observe the result.
5. Continue or stop.
6. Return the final answer or action summary.

Agent Components

Component	Purpose
LLM	Reasoning and language
Tools	APIs, search, database, browser, code execution
Memory	Context from previous steps or user history
Instructions	Rules and role
Guardrails	Safety limits
Approval	Human review for risky actions
Logs	Record of what happened

Good Agent Use Cases

• Repetitive workflows.
• Research and summarization.
• Support-ticket handling.
• Compliance checks.
• E-commerce operations.
• Internal operations.

Bad Agent Use Cases

• High-risk actions without approval.
• Tasks with unclear success criteria.
• Work requiring private data without access control.
• Systems where errors create legal or financial damage.

CookieYes Ideas

• Regulatory monitoring agent.
• Consent implementation review agent.
• Cookie classification agent.
• AI privacy risk review agent.

Mozilor Ideas

• WooCommerce inventory agent.
• Fraud review agent.
• Accessibility remediation agent.
• Support triage agent.

Related

• LLM
• Prompt Engineering
• RAG
• MCP
• A2A