What Are AI Agents? Types and A Technical Implementation Guide

In the early days of Generative AI, Large Language Models (LLMs) were passive. You asked a question, and they predicted the next token to form an answer. They were brilliant encyclopedias, but they had no hands.

An AI Agent is an LLM given the ability to act.

Technically defined, an AI Agent is a system that uses an LLM as a reasoning engine (The Brain) to determine which actions to take and in what order. Instead of just outputting text, an agent can interact with the external world through Tools (APIs, databases, file systems) to perceive its environment, make decisions, and achieve a specific goal autonomously.

The Core Shift: Prompting vs. Agentic Workflows

Andrew Ng and other AI leaders have highlighted a shift from better prompting to Agentic Workflows.

• Zero-Shot Prompt: “Write code for a snake game.” (One attempt, high failure rate).
• Agentic Workflow: “Write code for a snake game. Run the code. If it errors, read the error log, rewrite the code, and run it again until it works.”

Key Takeaway: An LLM generates text. An AI Agent generates actions based on reasoning.

Technical Overview: The Anatomy of an Agent

To architect an intelligent agent, you must move beyond standard API calls. A robust agent consists of four main components, often visualized as the Perception-Action Loop:

1. The Brain (Reasoning Engine)

Usually a high-performance LLM (e.g., GPT-5o, Claude 3.5 Sonnet). It is responsible for planning, decomposing complex queries into sub-tasks, and error correction.

2. Planning (The Orchestration Layer)

Before acting, the agent must decide how to solve the problem.

• Chain of Thought (CoT): Breaking a problem down step-by-step.
• ReAct Pattern (Reason + Act): The gold standard for agents (based on Yao et al., 2023). The agent explicitly writes down a thought, chooses an action, and observes the output.

3. Memory

LLMs are stateless; they forget interaction $A$ by the time interaction $B$ happens. Agents require state.

• Short-term Memory: Context window history (chat logs).
• Long-term Memory: Vector Databases (Pinecone, Milvus, Weaviate) enabling the agent to retrieve documents or experiences from weeks ago.

4. Tools (Actuators)

These are the hands of the agent. This relies heavily on function calling capabilities, where the LLM outputs structured JSON formatted to trigger specific APIs (e.g., send_email, query_sql_db, calculator).

Classification of Agents

Not all agents need the same level of autonomy. For enterprise architecture, we categorize them into four tiers:

Agent Type	Description	Complexity	Use Case
Simple Reflex Agent	Acts immediately based on current input. No history.	Low	Content moderation; simple email auto-responders.
Model-Based Reflex Agent	Maintains internal state (memory) to handle context.	Medium	Customer support bots that remember your order ID.
Goal-Based Agent	Uses search and planning to find a path to a specific goal.	High	Autonomous coding assistants; Supply chain optimization.
Utility-Based Agent	Weighs competing goals based on a utility function (maximizing value).	Very High	Stock trading agents; Dynamic ad bidding systems.

Comparison: LLM vs. Workflow vs. Agent

For the Technical Decision Maker, the hardest choice is often not which model to use, but which architecture to deploy.

The Decision Matrix

Feature	Standard LLM (RAG)	Robotic Process Automation (RPA)	AI Agent
Primary Function	Retrieve & Generate	Repeat & Execute	Reason & Adapt
Behavior	Passive (Wait for input)	Deterministic (Scripted)	Probabilistic (Autonomous)
Error Handling	None (Hallucinates)	Breaks on UI change	Self-Correction loops
Environment	Static Text	Rigid Rules	Dynamic Tool Use

When to use which?

• Use RPA if the process is linear and the input never changes (e.g., Copy Cell A to Cell B).
• Use a Workflow (LangGraph) if the steps are complex but predictable (e.g., Draft Blog -> SEO Check -> Human Approval).
• Use an Agent if the path to the solution is unknown and requires reasoning (e.g., “Analyze the last 5 competitors who changed pricing and draft a counter-strategy”).

Vendor-Agnostic Framework Comparison

Market leaders often push their own ecosystems. However, for a production-grade stack, you should evaluate the underlying frameworks objectively.

1. LangChain / LangGraph (Python/JS)

• Best For: Granular control and production engineering.
• Pros: Massive ecosystem, LangGraph offers stateful multi-actor orchestration (essential for preventing loops).
• Cons: Steep learning curve; documentation can be fragmented.

2. Microsoft AutoGen

• Best For: Multi-Agent Systems (MAS).
• Pros: Excellent at simulating “conversations” between agents (e.g., a coder agent talking to a reviewer agent).
• Cons: Can be difficult to constrain; agents chat too much, increasing token costs.

3. CrewAI

• Best For: rapid prototyping and role-based tasks.
• Pros: Very high-level abstraction. You define roles and tasks easily.
• Cons: Harder to debug when the magic fails under the hood.

Production Recommendation: Start with CrewAI for PoC to prove value. Migrate to LangGraph when you need strict state management and compliance controls.

Real-World Implementation: The Thought-Action Loop

To understand reliability, you must look at the logs. Below is a real example of the ReAct Pattern in action. The user asks: “What is the stock price of Apple compared to Microsoft, and which has a higher PE ratio?”

System Terminal Log:

> Entering new AgentExecutor chain...

THOUGHT:
I need to find the current stock price and PE ratio for both Apple (AAPL) and Microsoft (MSFT). I do not have this information in my training data. I must use the `finance_tool`.

ACTION:
finance_tool(ticker="AAPL", metrics=["price", "pe_ratio"])

OBSERVATION:
{"price": 224.50, "pe_ratio": 33.4}

THOUGHT:
I have Apple's data. Now I need Microsoft's data to compare.

ACTION:
finance_tool(ticker="MSFT", metrics=["price", "pe_ratio"])

OBSERVATION:
{"price": 415.20, "pe_ratio": 36.8}

THOUGHT:
I have both sets of data. 
AAPL: PE 33.4
MSFT: PE 36.8
Microsoft has the higher PE ratio. I can now answer the user.

FINAL ANSWER:
As of today, Apple trades at $224.50 (PE: 33.4) and Microsoft at $415.20 (PE: 36.8). Microsoft currently has the higher PE ratio.

Why this matters: A standard LLM would have hallucinated these numbers. The Agent recognized its ignorance, used a tool, and synthesized the answer.

7. Production Challenges & Governance

Most vendor pages gloss over the risks. If you are putting agents into production, you must address reliability and cost.

1. The Infinite Loop Risk

Agents can get stuck. If an agent tries to fix a bug, fails, tries again, and fails again, it can burn through $100 of API credits in minutes.

• Mitigation: Always implement max_iterations (e.g., stop after 5 steps) and time-outs.

2. Token Economics (Cost of Ownership)

Agentic workflows are verbose. The agent sends the entire conversation history, plus thoughts and observations, back to the LLM on every step.

• Reality Check: A simple 3-step agent task can cost 10x more than a single RAG prompt.
• Optimization: Use smaller, faster models (like Llama-3-70b or GPT-4o-mini) for the action steps, and reserve the heavy models (Claude 3.5 Sonnet / GPT-4o) for the planning step.

3. Hallucination in Function Calling

Agents sometimes invent parameters. For example, calling search_database(date="yesterday") when the API only accepts YYYY-MM-DD.

• Mitigation: Use Pydantic data validation or strict schema enforcement to reject malformed tool calls before they hit your API.

4. Human-in-the-Loop (HITL)

For high-stakes actions (e.g., “Delete Database”, “Refund User”), never allow full autonomy.

• Architecture Pattern: The Agent plans the action and pauses. A human receives a notification (Slack/Email), approves the plan, and then the Agent executes.

Use Cases: Where Agents Shine

Autonomous Coding (The Devin Model)

Agents that can access a file system, write code, run a linter, read the error, and patch the file.

• Stack: Docker containers (for sandboxing) + Anthropic Claude 3.5 Sonnet.

Dynamic Customer Support (Triage)

Instead of static FAQs, an agent can look up a user’s shipping status, check the warehouse policy, and issue a refund code if it meets the criteria all autonomously.

Deep Research

Agents that browse the web, scrape 20+ pages, summarize the findings, and write a report. (e.g., Perplexity-style workflows).

Future Outlook: 2026 and Beyond

We are currently transitioning from Chatbots to Action Bots.

• Multi-Agent Orchestration: Specialized agents (Researcher, Writer, Editor) working in a team structure is proving more effective than one “super-agent.”
• Standardized Interfaces: Adopting the Model Context Protocol (MCP) or similar standards to allow agents to connect to any data source without custom API wrappers.
• From “Human-in-the-loop” to “Human-on-the-loop”: Humans will shift from approving every step to auditing the logs after the work is done.

Resources for Builders

• Paper: ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al.)
• Governance: OpenAI Practices for Governing Agentic AI Systems.
• Benchmarks: Check the SWE-bench (Software Engineering benchmarks) to see which models currently lead in agentic coding performance.

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Disclaimer: Deploying autonomous agents involves significant security risks regarding data access and cost control. Always test in sandboxed environments.