What Are AI Agents? A Practical Guide
Learn what AI agents are, how they differ from chatbots, and how to build production-ready agents. Primary keyword: what are ai agents.
Most teams encounter the "what are AI agents" question the day a product manager reads a blog post and books a meeting. The honest answer isn't a vendor pitch; it's an architectural one. An AI agent is a software system that uses a language model to choose actions, run those actions in the real world (or in a simulated environment), observe the results, and decide what to do next. That loop (perceive, reason, act) is what separates agents from chatbots that only respond.
The distinction matters because it changes how you design, test, and operate software. A chatbot has no memory between turns unless you inject it. An agent can read a file, call an API, write code, run it, see the error, and fix it without a human in the loop for each step. That's powerful and, depending on how much trust you grant it, potentially dangerous. We'll cover both sides.
This guide is written for engineers and technical founders who want a clear mental model before committing to a stack.
What you'll learn
- What makes something an agent (and what doesn't)
- The core components every agent needs
- How agents plan and use tools
- Agent types and when to use each
- Where agents break down in production
- How to evaluate an agent before shipping it
What makes something an agent
An AI agent is a system that autonomously takes a sequence of actions to complete a goal, using an LLM as its reasoning engine. The word "autonomously" is doing real work here. If a human approves every step, you have a copilot. If the system decides for itself which steps to take and executes them, you have an agent.
Three properties distinguish agents from simpler LLM applications:
- Agency over actions — the model chooses what to do, not just what to say.
- Environmental feedback — the system observes the result of each action and updates its plan.
- Goal persistence — the system keeps working toward a goal across multiple steps, not just answering one prompt.
A plain chat completion doesn't have any of these. A retrieval-augmented generation (RAG) pipeline adds a tool call but usually lacks persistence. An agent has all three.
Core agent components
Every production agent, regardless of framework, needs the same building blocks.
LLM backbone. The model does the reasoning. Claude Sonnet and Opus are common choices for tasks requiring nuanced judgment; smaller models like Claude Haiku work for high-volume, structured subtasks. Model selection affects cost, latency, and error rate simultaneously; you rarely get to optimize all three.
Tool registry. Tools are functions the model can call: web search, code execution, database queries, file I/O, HTTP requests. The model doesn't run them directly; it outputs a structured call, and the host application executes it and feeds the result back.
Memory. Four kinds matter in practice:
- In-context — everything in the current prompt window.
- External short-term — a scratchpad or working memory the agent writes to during a task.
- External long-term — a vector store or database the agent can query across sessions.
- Episodic — logs of past runs the agent (or a human) can inspect.
Orchestration loop. The loop reads the current state, calls the model, parses its output, executes any tool calls, appends the results, and repeats until the agent signals it's done (or a stopping condition fires). This is where frameworks like LangGraph, AutoGen, and plain Python async code live.
Stopping conditions. This one's often skipped in demos. Without explicit stopping conditions (max steps, max tokens, confidence thresholds, human-approval gates), agents can loop until they exhaust your API budget. Not hypothetically. This has happened on real projects.
How agents plan and use tools
Planning is how an agent decides the sequence of actions needed to reach a goal. There are two broad approaches: ReAct (Reasoning + Acting interleaved) and plan-then-execute.
In ReAct, the agent alternates between a Thought (what it intends to do and why) and an Action (the tool call or response). The observation from each action feeds into the next thought. This works well for open-ended tasks where the path isn't known in advance.
Plan-then-execute generates a full step-by-step plan first, then executes each step, replanning if something fails. It's more predictable and easier to audit, which matters for regulated industries or anything that touches money.
Here's a minimal ReAct loop in Python using the Anthropic SDK:
import anthropic
client = anthropic.Anthropic()
tools = [
{
"name": "search_web",
"description": "Search the web and return a summary of results.",
"input_schema": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Search query"}
},
"required": ["query"]
}
}
]
messages = [{"role": "user", "content": "What is the current LTS version of Node.js?"}]
while True:
response = client.messages.create(
model="claude-sonnet-4-5",
max_tokens=1024,
tools=tools,
messages=messages
)
if response.stop_reason == "end_turn":
# Agent is done
print(response.content[-1].text)
break
if response.stop_reason == "tool_use":
tool_use = next(b for b in response.content if b.type == "tool_use")
# Execute the tool (simplified)
tool_result = execute_tool(tool_use.name, tool_use.input)
messages.append({"role": "assistant", "content": response.content})
messages.append({
"role": "user",
"content": [{"type": "tool_result", "tool_use_id": tool_use.id, "content": tool_result}]
})
This loop is production-ready in shape if not in scale. You'd add retry logic, error handling, and token-count guards before shipping it.
Agent types and when to use each
Agents aren't one-size-fits-all. Here's how the main patterns compare:
| Agent type | Best for | Key limitation |
|---|---|---|
| Single-agent ReAct | Research, Q&A, simple automation | Context window is the ceiling |
| Multi-agent (parallel) | Tasks with independent subtasks | Coordination overhead |
| Multi-agent (hierarchical) | Complex workflows with specialization | Harder to debug |
| Human-in-the-loop | Anything irreversible or regulated | Slower; requires good UX |
| Code-execution agent | Data analysis, testing, scripting | Sandbox escape risk |
Single-agent systems are easier to reason about and debug. Reach for multi-agent patterns only when a single agent genuinely can't fit the task in one context window, or when parallel execution matters for throughput. We've seen teams jump to multi-agent setups prematurely and spend weeks untangling coordination bugs that a simpler single-agent loop would have avoided.
If you're building workflows with conditional branching and typed state, LangGraph handles the graph topology well. For lighter tasks, a plain loop with the Anthropic SDK often beats a heavy framework.
Where agents break down in production
This is the part demo videos skip. Agents fail in predictable ways, and knowing the failure modes before you ship is the difference between a reliable product and an on-call nightmare.
Context overflow. Long tasks fill the context window. The agent starts hallucinating or loses track of earlier state. Fix: summarize completed steps, move older state to external memory, and prune aggressively.
Tool errors cascading. If a tool call fails and the agent doesn't handle it gracefully, it either hallucinates a result or loops. Fix: return structured errors from every tool, and include error-handling instructions in the system prompt.
Goal drift. Over long runs, the agent's interpretation of the original goal can drift. Fix: include the original goal verbatim in every prompt turn (not just the first), and add a goal-adherence check in the loop.
Irreversible actions. An agent that can send emails, delete records, or charge cards can do real damage if it misinterprets a goal. Fix: classify actions by reversibility and require human approval for irreversible ones.
At Laxaar, we treat irreversibility as the primary safety axis. Before any agent deployment, we map every tool to a reversibility level and gate the high-risk ones behind explicit confirmation. It's not glamorous engineering, but it's what keeps incidents from becoming outages.
How to evaluate before shipping
You can't eyeball your way to production confidence with agents. Evaluation needs to be systematic.
Trajectory evaluation checks whether the agent took a reasonable path, not just whether the final answer is right. An agent that gets the right answer via a lucky shortcut will fail on the next variant.
Tool-call accuracy measures whether the agent called the right tool with the right arguments. A tool-call error rate above 5% in evals usually means your tool descriptions need work, not your model.
Failure injection deliberately feeds the agent broken tool responses and checks that it recovers gracefully rather than hallucinating.
Latency and cost budgets are first-class eval dimensions. A correct agent that costs $2 per task may not be viable at your volumes. Track these from day one.
For a deeper look at building evaluation systems for LLM apps, see our article on LLM evaluation systems. For a broader view of the architectural choices that come after you understand the basics, AI agent architectures compared is the next read.
The Laxaar team has shipped agents across customer-support automation, internal data pipelines, and agentic coding tools. If you want to understand what goes into AI agent development at production scale, that page covers the work we actually do.
Frequently Asked Questions
What's the difference between an AI agent and a chatbot?
A chatbot generates a response to a single input. An AI agent takes a sequence of actions (using tools, observing results, and replanning) to complete a goal over multiple steps. Agents have memory and autonomy; most chatbots don't.
Do I need a framework like LangChain or AutoGen to build an agent?
No. Frameworks reduce boilerplate, but a well-structured loop using the Anthropic SDK or OpenAI SDK directly is often easier to debug and extend. Start without a framework; add one when the coordination complexity genuinely justifies it.
How much does it cost to run an AI agent?
It depends heavily on model choice, task complexity, and how many tool-call rounds the agent needs. A Claude Haiku-based agent handling structured tasks might cost fractions of a cent per run. A Claude Opus-based research agent doing a dozen web searches can cost $0.10–$0.50 per task. Always set hard token budgets before going to production.
Are AI agents safe to use for tasks that involve real money or data?
They can be, with the right guardrails. The key controls are: classifying actions by reversibility, requiring human approval for irreversible steps, running agents with least-privilege credentials, and logging every action for audit. Agents without these controls shouldn't touch financial or sensitive data.
What's the best model for running an AI agent?
It depends on the task. Claude Opus 4 handles complex, ambiguous reasoning well. Claude Sonnet 4 hits a good balance of quality and cost for most production workloads. Claude Haiku is the right choice for high-volume, structured subtasks where speed and cost matter more than nuanced judgment.
Ready to move from concept to shipped product? The Laxaar team works with engineering teams at every stage, from architecture review to full-stack delivery. Tell us about your project and we'll respond within one business day.
