How it works: the ReAct loop
The core principle behind modern AI agents is the ReAct loop — a cycle of reasoning (thinking) and acting (doing). It runs until the goal is reached or an escalation is needed.
Receive the goal (Goal)
The agent is given a clearly formulated goal. The more precise the goal, the better the result. Vague goals like "improve my marketing" are useless. Precise goals like "send a personalized follow-up email to every lead who hasn't responded in 5 days" work.
Plan (Reason)
The agent breaks the goal into sub-steps, evaluates available tools, and sets the order. This step is the agent's "thinking" — it decides what information it needs and which actions it will take.
Select and execute a tool (Act)
The agent picks the right tool and executes the action: reading email, querying a database, drafting text, calling an API, reading a file. It only uses tools that have been explicitly made available to it.
Observe the result (Observe)
The agent evaluates the result of the action. Did it work? Does it need more information? Does the plan need adjusting? This observation feeds into the next reasoning step.
Repeat or conclude
The loop repeats until the goal is reached. If the agent hits a problem outside its defined scope, it escalates to the responsible human — with full context on everything it has done so far.
The 4 core components of an AI agent
| Component | Function | Example |
|---|---|---|
| LLM (Brain) | Understands goals, plans steps, interprets results | GPT-4, Claude, Gemini |
| Tools | Enable actions in external systems | Email API, calendar, web search, database |
| Memory | Stores context and results for later use | Vector database, conversation history |
| Planning | Breaks goals into sub-steps and coordinates their execution | ReAct, chain-of-thought, tree-of-thought |
Benefits of the agent architecture
- Adaptive problem-solving. The agent adjusts its plan when something doesn't work — instead of stopping at the first exception like a script.
- Tool integration. An agent can talk to any system with an API: CRM, ERP, email, calendar, internal knowledge base.
- Parallel execution. In multi-agent setups, several subagents run at the same time on different sub-tasks.
- Persistent memory. The agent remembers past interactions and results — it's not an intern starting from scratch every morning.
- Auditability. Every step in the ReAct loop is logged. You see what the agent thought, decided, and executed.
Drawbacks and limits of the architecture
- Latency from multi-step loops. Every reasoning step costs time. Time-critical applications needing sub-second response times are only a limited fit.
- Costs scale with complexity. More steps means more LLM calls means higher API costs. Simple, rule-based tasks are often cheaper with classic automation.
- Errors can compound. If the agent makes a mistake in an early step, it keeps building on a faulty foundation. Checkpoints and verification steps are necessary.
- Tool limits define agent limits. Whatever has no tool, the agent can't do. Careful tool design is critical.
Examples of AI agents in practice
Email agent (ReAct loop in 4 steps)
- Goal: Automatically answer all incoming customer questions about invoices.
- Plan: Read the email → identify the category (invoice question?) → load customer data from the CRM → fill a reply template with personalized data → save as a draft.
- Execution: API calls to the email system and CRM, text generation, draft creation.
- Observation: Draft created? If yes: done. If data is unclear: flag for human review.
Research agent (always-on)
A research agent runs daily: it scans defined sources (industry news, competitor websites, LinkedIn, patent databases), extracts relevant information, assesses relevance, and creates a prioritized briefing — without anyone entering search terms or filtering results.
Use cases for mid-sized businesses
The architecture of AI agents makes them especially well-suited for workflows too complex for a simple script, but too repetitive for qualified employees:
- Email management. Prioritization, categorization, routine replies — 200 emails a day without manual review.
- Outreach automation. Personalized sequences, engagement tracking, follow-ups — over weeks without human steering.
- Data processing. Contracts, invoices, applications — structured and processed in minutes instead of hours.
How Amazing Agents implements AI agents
Every agent Amazing Agents builds follows the same architectural principle: a clear goal, defined tools, persistent memory, an audit trail, human-in-the-loop at critical points. No agent goes live without a defined rollback path and monitoring.
For complex workflows, we combine several agents into a coordinated system. More on this: What is a multi-agent system?
Basics on AI agents: What are AI agents? · The difference from automation: AI agents vs. classic automation