What Are AI Agents and How Do They Work?

An AI agent is an autonomous system that can perceive its environment, reason about goals, and take actions to achieve those goals. In the context of LLM-powered agents, this means:

Perception: Understanding user requests, reading data from systems, and interpreting context.

Reasoning: Using an LLM to plan how to accomplish a goal, break it into steps, and decide what actions to take.

Action: Actually executing tasks—calling APIs, updating databases, sending emails, browsing the web.

Learning: Incorporating feedback and adjusting approach based on results.

The critical distinction from traditional chatbots: agents DO things. They don't just tell you how to do something—they do it for you.

Every AI agent system has these fundamental components:

LLM (Reasoning Engine) The large language model serves as the "brain" that understands goals, plans approaches, and decides actions. Models like GPT-4, Claude, or open-source alternatives provide the reasoning capability.

Tools External capabilities the agent can invoke: API calls, database queries, web browsing, file operations, code execution. Tools turn reasoning into action.

Memory - Short-term: Conversation context and current task state - Long-term: Persistent knowledge, learned preferences, past interactions - Working: Intermediate results during multi-step tasks

Orchestration Layer The system that coordinates the agent loop: receiving inputs, calling the LLM, executing tools, handling errors, and managing state.

Agents operate in a loop that continues until the task is complete:

1. Observe: Receive input or perceive current state
2. Think: LLM reasons about what to do next
3. Act: Execute chosen action (tool call, response, etc.)
4. Observe: See the result of the action
5. Repeat: Continue until goal achieved or limit reached

ReAct Pattern (Reasoning + Acting) The most common agent pattern interleaves reasoning and acting:

• Thought: "I need to find the customer's order history"
• Action: Call orders API with customer ID
• Observation: Received 5 orders from the last year
• Thought: "Now I can identify patterns in their purchases"
• Action: Analyze order data
• And so on...

This explicit reasoning makes agents more reliable and debuggable than pure chain-of-thought approaches.

Not everything should be an agent task. Agents excel at:

Good fit for agents: - Multi-step workflows requiring judgment - Tasks with unstructured inputs (natural language, documents) - Processes with many edge cases - Work that benefits from reasoning and adaptation

Poor fit for agents: - Simple, deterministic operations (use traditional code) - High-volume, low-value tasks (agent overhead is expensive) - Tasks requiring perfect precision every time - Real-time operations (agents have latency)

The agent value test: Would a smart human junior employee add value here? If yes, an agent might too. If a simple script would do, skip the agent.

Current LLM-powered agents can reliably:

Information tasks: - Research and summarize topics - Extract data from documents - Answer questions using multiple sources - Generate reports and analyses

Coordination tasks: - Send emails and messages - Schedule meetings - Update CRM/ticketing systems - Route requests to appropriate handlers

Data tasks: - Query databases and APIs - Transform and clean data - Generate visualizations - Create structured outputs

Limitations to understand: - Agents make mistakes—build in verification - Complex multi-step tasks have compounding error rates - Agents are slow (seconds to minutes, not milliseconds) - Costs add up with LLM calls and tool executions

Production agents need guardrails, monitoring, and graceful degradation.