How to build AI agents

AI agents are here to stay, but building one that actually works is still a mystery for many teams.

Are they just fancy chatbots?

Do you need a research lab to build one?

What does it take to make them useful in the real world?

This post breaks it all down.

Whether you're shipping your first agent-powered product or trying to make your APIs agent-friendly, you'll get a clear, no-fluff guide to doing it right.

We’ll cover how agents think, what they need, when to use them, and how to avoid the most common traps. Let’s get into it.

What is an AI agent?

An AI agent, or autonomous agent, is a software system powered by a language model that can perceive input (usually in natural language), plan actions, execute them using external tools or APIs, and learn from past interactions to improve over time.

Unlike traditional chatbots, agents aren’t just answering questions—they’re completing goals.

Agents:

• Interpret user intent and work toward a defined objective.
• Take initiative when needed, rather than just responding passively.
• Engage in back-and-forth with humans or other systems.
• Call APIs, search the web, write files, access databases, etc.

When to build an AI agent

You don’t need an agent for everything. Sometimes, a simple prompt to a model is enough.

Use an agent when:

• You need chained reasoning (e.g., research → analysis → report).
• The user’s request is ambiguous or open-ended.
• You want the system to call APIs or external tools.
• Tasks may span multiple steps, retries, or validations.
• You want a system that can track its progress, remember earlier actions, and adjust.

The three building blocks of an AI agent

Every agent needs exactly three things:

1. A model to think
2. Tools to act
3. Instructions to know what to do

Let’s break those down and see where they fit in an agent’s architecture.

1. The model

The model is the language model powering your agent’s "thinking." It interprets inputs, reasons through problems, decides what to do next, and generates responses. Think of it as the brain behind the operation.

Popular options include models like OpenAI’s GPT-4, Anthropic’s Claude, or open-source ones like Mistral or Mixtral.

When choosing a model for your agent follow these principles:

• Use the best model you can afford—at least at first. You want to validate your agent’s logic and behavior before worrying about cost. Cheaper models often hallucinate more, follow instructions less reliably, or break on edge cases.
• Don’t get locked in. Build your agent with an abstraction layer (like OpenAI’s functions, LangChain, or your own wrapper) so you can swap out models later.
• Prototype fast, optimize later. Once you know the agent works, then explore distillation, quantization, or hybrid approaches (e.g. LLM + rules engine).

And don’t forget: your model doesn’t need to do everything. It just needs to do the thinking—you’ll offload most of the doing to tools.

2. The tools

Agents aren’t useful just because they talk nicely—they're useful because they do things. Tools are what turn a smart language model into a real agent that can act in the world.

A tool is just a function the model can call. It could be an API, a database query, a shell script, or even another agent.

There are 3 types of tools:

1. Get info: Reads data to understand the situation (e.g., check database, read email, search web, etc.)
2. Take action: Does something meaningful (e.g., send email, update record, make payment, etc.)
3. Work with others: Collaborate with other agents (e.g., "Let the refund agent handle this")

Design principles for tools:

• Do one thing well. Don’t make giant "Swiss Army knife" tools. Each tool should do one thing well. Clear, narrowly scoped functions are easier for the model to learn and for you to debug. Better to have 10 clear tools than 3 confusing ones.
• Be explicit about inputs and outputs. Define JSON schemas or function signatures. Agents need structure.
• Fail loudly and clearly. Give agents helpful errors they can respond to—don’t just return 500s and hope for the best.
• Name tools wisely. The tool name and description are part of the prompt. “getWeatherForecast” is better than “weatherFunc1.”

Pro tip: start with just a few essential tools. You can always add more once your agent proves it can use them correctly.

!!When choosing your model and building your tooling layer, consider using an agent framework like LangChain or CrewAI to help with abstraction, routing, and memory—especially if you're planning to scale beyond a simple prototype.!!

3. The instructions

Instructions are what tell your agent how to think and behave. They're like your internal company playbook, but written in a way a model can understand and follow.

These are often called "prompts," but think of them more like policies and procedures: a structured blueprint for how the agent should approach its tasks.

Good instructions include:

• A clear role and objective: What is this agent for? What outcome are we aiming for?
• Step-by-step guidance: Break down tasks into specific steps. Tell the model when to call a tool, when to ask questions, and when to escalate.
• Fallback logic: What should it do if something goes wrong? If the user didn’t give enough info? If the tool throws an error?
• Tone and behavior rules: Should it be friendly? Formal? Keep responses short? Include emojis? These details matter, especially in customer-facing use cases.

Here’s a basic example of structured instructions:

	
You are an Order Lookup Agent for Acme Inc.
Your goal is to help users check the status of their orders.

Follow these steps:
1. Greet the user and ask for their order number.
2. Call the `lookupOrder(order_number)` tool.
3. If the order is not found, ask for the user’s email and try `lookupOrderByEmail(email)`.
4. If still not found, apologize and escalate to a human using `escalateToHuman()`.
5. If the order is found, return status and tracking info.

Always be concise, polite, and professional.
	

Instructions can live inside the prompt or be stored separately (e.g., YAML or JSON config files) if you’re building more complex agents.

How to organize your agents

Should you build one super-agent that does everything? Or should you break things up into specialist agents that each handle a specific job?

There’s no one-size-fits-all answer, but there are good rules of thumb. Let’s walk through them.

Option 1: One general-purpose agent (start here)

Most teams should begin with a single, general-purpose agent that can call multiple tools and handle a variety of tasks.

Why? Because it’s:

• Easier to build (you don’t have to deal with routing logic or cross-agent coordination).
• Easier to debug (all decisions are made in one place).
• Faster to iterate (you can change prompts, tools, or instructions without breaking a chain of agents).
• Good enough for most use cases (especially in early-stage or internal-facing agents).

In this model, you give your agent access to a toolbox and a clear set of instructions. It decides what tool to use, when, and how. This works great for things like:

• Customer support triage
• Internal operations automation
• Research agents
• Personal productivity tools

Use this approach most of the time. Stick with one agent until you start seeing signs that it’s not scaling well. Then it’s time to consider the next option.

Option 2: Multiple specialized agents

As your use cases get more complex, you may want to break things up into smaller, focused agents—each with its own tools, instructions, and responsibility.

You’ll know it’s time to split when:

• Your main agent's instructions are getting too long to manage.
• It keeps choosing the wrong tools because it has too many options.
• Different parts of your system are managed by different teams or domains.
• You want to parallelize work across agents for performance.

Once you split things up, you’ll need a way to organize and coordinate those agents. There are two main patterns for doing that:

• Manager style: One "boss" agent acts as the entry point and delegates work to specialist agents. It receives the user’s request, decides which specialist agent(s) should handle it, and delegates accordingly. It may then combine results or return control to the user. This option is good for:
  
  • A consistent interface (one agent talks to the user).
  • Keeping logic centralized.
  • Coordinating tasks across domains.
• Handoff style: In this pattern, each agent handles a specific step in the process and passes control to the next agent in line. There's no single manager—instead, agents coordinate with each other directly. It's good for:
  
  • Long workflows with well-defined stages.
  • Modular processes that change over time.
  • Agent systems where steps can be skipped or reordered.

Manager style example:

	
User: “Translate ‘hello’ into Spanish, French, and Italian.”

Manager Agent:
  → Calls Spanish Translator Agent
  → Calls French Translator Agent
  → Calls Italian Translator Agent
  → Returns combined response to user
	

Handoff style example:

	
User: “I want to return this item.”

Triage Agent:
  → Recognizes it’s a return
  → Hands off to Returns Agent

Returns Agent:
  → Verifies order
  → Initiates return
  → Handles communication with the user
	

Frameworks like LangChain and CrewAI can help manage these orchestration patterns. LangChain provides support for tool routing, memory, and agents-as-APIs. CrewAI focuses on multi-agent collaboration with role-based flows and task delegation. You don’t have to use a framework—but they can save a lot of scaffolding.

Choosing the right approach

You don’t have to commit forever. Start with one agent. If it grows into chaos, refactor it into multiple.

Think of this like microservices: you don’t need them until you do.

‍

Follow these steps:

1. Start with one agent—keep it simple.
2. If things get messy, split into specialist agents.
3. Use a Manager if you want centralized control.
4. Use Handoff if you want modular, flexible workflows.
5. Design with clarity, separation of concerns, and observability in mind.
   
   1. Use unique instructions per agent—don’t just copy-paste prompts
   2. Log decisions and transitions between agents for observability
   3. Consider a shared memory or state system so agents can pass context
   4. Give agents clear roles and responsibilities, just like humans

Authentication: How your agents access other systems

If your agent needs to do real work—like reading customer data, sending messages, or initiating payments—it needs secure access to external systems.

That means it needs to authenticate just like a human or another app would. But since your agent is non-human, you need to be intentional about how it gets those credentials.

Use API keys for static, service-level access

If the agent is acting on behalf of your product, not an individual user, then an API key is often the simplest option.

This is great for:

• Internal systems
• Admin-level automation
• Background agents with fixed scope

Best practices to follow:

• Use scoped API keys (e.g., read-only, write-only)
• Rotate keys regularly
• Log and monitor usage per key
• Store securely—don’t hardcode in source

Use OAuth when acting on behalf of a user

If your agent is performing actions as a user (e.g., reading their calendar, managing their account), then use OAuth 2.0.

This gives you scoped, time-limited access, consent from the user, and a way to revoke/refresh access securely.

The flow works like this:

1. User authenticates and grants permission
2. Agent receives an access token
3. Agent uses token to act on user’s behalf
4. Refreshes token when it expires

Best practices to follow:

• Use short-lived tokens + refresh tokens
• Keep track of which token belongs to which user
• Fail gracefully if a token is revoked or expires

Tip: Some platforms support service accounts or device code flows that are easier to automate without a full browser loop.

Auth best practices

Whether you're using API keys or OAuth tokens:

• Encrypt credentials at rest
• Never log secrets
• Test with revoked/expired credentials
• Don’t let your agent "guess" how to authenticate—build it in explicitly

Keeping your agents safe

Agents can misuse tools, leak data, hallucinate bad advice, or get tricked by clever prompts. You need layers of defense, just like you would for a human employee with access to real systems. Start strict, monitor everything, and gradually relax your guardrails as you gain trust.

• Input filtering: Check what users send before your agent sees it. Users (or attackers) can prompt-inject, confuse, or overload your agent. Garbage in = garbage (or worse) out. Consider using a lightweight classifier or regex filters before even invoking the model.
  
  • Block harmful content (e.g. abuse, threats, illegal requests)
  • Catch attempts to trick your agent (e.g. “Ignore previous instructions…”)
  • Filter out irrelevant requests (e.g. excessive emojis or gibberish)
• Output validation: Before the agent sends a response—or triggers a tool—double-check its output. LLMs are prone to hallucination and overconfidence. You don’t want your agent to lie to a user, send sensitive info, or call delete_all_users(). Add a response post-processor layer (e.g. regex, schema validation, second model), use moderation APIs (like OpenAI’s or your own), or require human approval for risky responses or actions. What to check:
  
  • Is the response appropriate? (Tone, format, accuracy)
  • Is sensitive info leaking? (User data, tokens, internal links)
  • Is the proposed action allowed? (E.g., spending $10,000 without approval)
  • Does it match expectations? (Schema, content, context)
• Tool safety: If you give a model a hammer, everything looks like a nail. Just because a tool exists doesn’t mean the agent should always be able to use it. Control what your agent can actually do. Implement the following best practices:
  
  • Scope tools to the right agent (don’t give the returns bot access to billing APIs).
  • Mark high-risk tools (label actions like delete, transferFunds, or escalate).
  • Require approvals for sensitive tools (prompt a human to confirm before continuing).
  • Add rate limits and quotas (cap usage to prevent loops or runaways).
  • Audit every call (log all tool usage for review).
• Human oversight: No matter how good your system is, edge cases and ambiguity will creep in. Let humans step in when it gets weird.
  
  • Auto-escalate after repeated failures or confusion.
  • Require human review for high-impact tasks (refunds, deletions, policy changes).
  • Design graceful handoffs – Let the agent say “Let me connect you to a teammate” naturally.
  • Log and label edge cases to improve future behavior.
• Emergency controls: Sometimes things go sideways fast. You need panic buttons. Built these from day one:
  
  • Emergency stop: Shut down an agent or block tool access in real-time.
  • Anomaly detection: Alert when something weird is happening (e.g., 100 tool calls in 60s).
  • Chaos testing: Regularly break things on purpose to test fail-safes.

The TL;DR of safety rules is:

1. Start restrictive, then loosen up as you gain confidence.
2. Log everything so you can see what went wrong.
3. Test the breaking points - try to make your agent fail safely.
4. Have an "emergency stop" button.

Testing your agent (like it’s a real user)

Building an agent is only half the job. The other half? Making sure it actually works—in real-world conditions, with noisy input, flaky tools, and unclear goals.

Agents don’t just break—they get confused, loop endlessly, or confidently do the wrong thing. These tests help catch that before production does.

1. End-to-end behavior testing

Run your agent through real tasks, not just happy-path inputs.

• Does it complete multi-step goals correctly?
• Can it recover from tool failures or bad responses?
• Does it retry when appropriate—or spiral in a loop?
• Does it escalate when it hits uncertainty?

Use agents in staging environments with production-like tools and real-world scenarios.

Run your agent through real-world tasks using frameworks like LangChain or CrewAI, which make it easier to manage multi-agent orchestration, tool calls, and memory. Try use cases like:

• Creating and reading records
• Following step-by-step instructions
• Testing tool behavior and edge cases

2. Instruction testing

Your prompt is your agent’s brain—treat it like code.

• Are instructions clear, consistent, and followed?
• Do changes to prompts affect behavior in unexpected ways?
• Can the agent handle edge cases and fallbacks gracefully?
• Are "refusal" and "escalation" behaviors built-in and testable?

Tip: Version your instructions like you would code. Prompt diffs can cause regressions too.

3. Tool usage testing

Tools are where things get real—and break.

• Are the right tools being selected in context?
• Are tools being called with valid, complete inputs?
• Do tool descriptions help or confuse the model?
• What happens when a tool fails or times out?

Use structured logging to trace which tools are used and why.

4. Resilience and safety testing

What happens when things go wrong?

• Does the agent handle incomplete input?
• Does it recognize it’s stuck—or pretend everything is fine?
• Can it be tricked by prompt injection?
• Is it rate-limited safely? Are unsafe actions blocked?

Break your own agent on purpose. It should fail gracefully, not dangerously.

5. Multi-model testing

Try your agent logic across different models (GPT-4, Claude, Mistral, etc.). You'll often uncover surprising differences.

• Does the agent behave consistently across models?
• Which models follow instructions more reliably?
• Where do hallucinations or tool misuse increase?

The bottom line

Building good agents is more about clear thinking than fancy technology. Start simple, test constantly, and improve step by step.

Remember: Your agent doesn't need to be perfect. It just needs to be better than what you're doing now, and safe enough to trust with real work.

The goal isn't to replace humans—it's to handle the repetitive stuff so humans can focus on what they do best.