How to build AI Agents by OpenAI
Recently, OpenAI released a short guide on how to build AI agents, and this guide is proving to be a gold mine for anyone who is interested in starting off with AI agents, especially beginners.
But the guide is 34 pages long
Data Science in Your Pocket – No Rocket Science
If you don’t have time to read the guide, I am here to summarise it for you and extract the key points. Let’s get started on building AI agents for beginners.
https://medium.com/media/8d5a0f70c421878ce81f33dfea523740/href
What is an Agent?
When Should You Build an Agent?
AI Agent Components
How to Select Which LLM to Use?
Which Tools to Use?
Configuring Instructions
Types of AI Agent systems
Guardrails
The guide starts by explaining what an AI agent is…
1. What is an Agent?
An AI agent is a system that autonomously performs multi-step tasks on behalf of users, unlike basic chatbots that only respond to single prompts.
Key Characteristics:
Independence: Makes decisions without needing constant human input.
Workflow Execution: Handles sequences (e.g., book flight → check availability → process payment).
Self-Correction: Detects and recovers from errors (e.g., retry a failed API call).
Tool Integration: Uses APIs, databases, software (e.g., fetch weather data, update CRM).
Example Use Case:
A customer support agent who:
Reads a complaint
Check the order history
Approves a refund — without human help
Don’t confuse AI agents with any basic AI system.
Agents vs. Basic AI:
A basic AI like an FAQ chatbot just answers questions — it’s reactive, single-turn, and doesn’t take action. An agent, on the other hand, handles multi-step tasks autonomously. For example, a refund agent doesn’t just explain the policy — it checks your order, evaluates conditions, makes a decision, and initiates the refund. One gives info, the other gets stuff done.
The next section talks about when you should build an AI agent and when you should not..
2. When Should You Build an Agent?
Ideal Scenarios:
Complex Decisions
- Tasks requiring contextual judgment, like approving refunds based on customer loyalty + purchase history, + dispute patterns.
- Example: A fraud detection agent analysing subtle behavioural red flags instead of rigid rules.
Brittle Rule-Based Systems
- Overly complicated workflows with endless “if-then” conditions, like vendor security reviews with 50+ ever-changing rules.
- Example: Automating loan approvals where policies vary by region, customer tier, and loan type.
Unstructured Data Handling
- Interpreting free-form inputs like handwritten insurance forms, voice notes, or messy PDFs.
- Example: Extracting claim details from a scanned doctor’s note with typos and abbreviations.
Not Ideal For:
- Simple, repetitive tasks (e.g., sending templated emails or bulk data entry) — traditional automation (Zapier, macros) suffices.
- Static workflows (e.g., password resets) where rigid rules work reliably.
Key Question: ”Does this task require adaptability or human-like judgment?” If yes, an agent may be the solution.
The next section of the guide discusses a key component of an AI agent.
AI Agents Components
1. Model (“The Brain”)
- What it does: The LLM (e.g., GPT-4) powers the agent’s reasoning, decision-making, and ability to understand context.
- Why it matters: A smarter model can handle complex logic, while a smaller one may struggle.
- Pro tip: Start with the most capable model (like GPT-4) to ensure strong performance, then test smaller models (e.g., GPT-3.5) for cost optimization once the workflow is stable.
2. Tools (“The Hands”)
- What they do: Tools are APIs or functions that let the agent interact with the outside world — fetching data, taking actions, or delegating tasks.
Types & Examples:
- Data Tools → Retrieve information (e.g., query a customer database, search the web).
- Action Tools → Perform tasks (e.g., send emails, update a CRM, process refunds).
- Orchestration Tools → Call other specialised agents (e.g., a “translation agent” or “fraud detection agent”).
- Key insight: Well-designed tools are reusable, documented, and narrowly scoped (e.g., get_weather_API vs. a generic search_tool).
3. Instructions (“The Rulebook”)
- What they do: Instructions are the agent’s step-by-step playbook, telling it how to behave, what tools to use, and how to handle edge cases.
Best practices:
- Break tasks into small steps:
❌ Unclear: “Help the user with their order.”
✅ Clear: “1. Ask for the order number. 2. Fetch order status from the database. 3. If delayed, offer a 10% discount code.”
- Anticipate edge cases: Example: “If the user doesn’t have an order number, ask for their email and phone number to look it up.”
- Use natural language: Write as if teaching a human, avoiding jargon.
- Advanced tip: Use the LLM itself to refine instructions (e.g., “Convert this FAQ into agent-friendly steps”).
Why This Trio Matters
- Without a strong model, the agent makes poor decisions (e.g., misinterpreting user requests).
- Without tools, it’s “brainless” — unable to act (e.g., a customer service agent that can’t check order history).
- Without clear instructions, it’s unpredictable (e.g., approving refunds it shouldn’t).
Example: A travel booking agent might:
Use GPT-4 (model) to understand a user’s request (“Find a Paris hotel under $200/night”).
Call a data tool to search hotels, then an action tool to reserve a room.
Follow instructions like: “If no hotels are available, suggest alternatives in nearby cities.”
How to select which LLM to use?
Quick Tips:
- Start with GPT-4 for best performance
- Use GPT-3.5 for simpler, cheaper tasks
Tradeoff Triangle: Accuracy ⚖️ Speed ⚖️ Cost
Examples:
- Use GPT-4 for refund logic
- Use GPT-3.5 to classify user intent
And which tools to use?
Data Tools
- Serve as the agent’s information-gathering capability
- Retrieve structured and unstructured data from various sources
- Common examples include:
Database lookup tools for customer records
Web search APIs for real-time information
Document parsers for extracting key details from files
Critical for providing context to decision-making
Action Tools
- Enable the agent to make changes in external systems
- Handle both digital and physical world interactions
- Typical implementations cover:
Communication tools (email/SMS/notification systems)
CRM updates for sales and support workflows
Transaction processors for payments/refunds
Often require additional security safeguards
Orchestration Tools
- Facilitate complex workflows through agent collaboration
- Allow for specialisation and modular design
- Implementation patterns include:
Service routing (handoffs to specialized agents)
Parallel task delegation
Workflow coordination
Key Recommendation
Standardise and document all tools for cross-agent compatibility. For instance, a well-designed search_database tool with clear input/output specifications can be reused by multiple agents across different departments, reducing development overhead and ensuring consistency in data access patterns. Always include proper error handling and logging in tool implementations to maintain operational visibility.
Example Scenario
When handling a customer service request, an agent might:
Use data tools to pull order history and account details
Leverage orchestration tools to consult a returns specialist agent
Finally employ action tools to process the refund and send confirmation
6. Configuring Instructions
Why It Matters:
Bad instructions = confused agents, and wrong tools may get picked up.
Best Practices:
- Use existing manuals/policies
- Be specific:
❌ “Help the user.”
✅ “1. Ask for order ID → 2. Fetch DB → 3. If late, offer 10% discount.”
- Handle edge cases:
If no order ID? Ask for an email.
Types of AI agent systems
This section talks about the different types of AI systems
1. Single-Agent System
- How it works: A single agent handles all tasks using multiple tools.
- Best for: Simple, linear workflows where one “brain” can manage everything.
Example:
- A weather bot that:
Takes a location input
Calls a weather API (data tool)
Formats the response (action tool)
Pros:
- Easy to build and debug
- Low overhead (no coordination needed)
Cons:
- Can become unwieldy as complexity grows
2. Multi-Agent System
For more complex workflows, multiple agents work together in two key patterns:
A. Manager Pattern (“Boss and Team”)
How it works:
- One manager agent acts as the “boss,” delegating tasks to specialized sub-agents.
- The manager decides when and how to use each specialist.
manager_agent = Agent( tools=[ spanish_agent.as_tool(),
# Handles Spanish translations
french_agent.as_tool(),
# Handles French translations
refund_agent.as_tool() # Processes refunds
] )
- The manager might:
- Detect a French translation request → Call french_agent
- Receive a refund claim → Route to refund_agent
Pros:
Clean separation of concerns
Easier to scale (add new specialists without rewriting the manager)
Cons:
A manager can become a bottleneck if overloaded
B. Decentralised Pattern (“Peer-to-Peer”)
How it works:
- Agents directly hand off tasks to each other (no central manager).
- Each agent specialises in one area and decides when to involve others.
Example:
- A customer service triage agent who:
Receives a query (“Where’s my order?”)
Hands off to an order_tracking_agent
If the order is late, the tracking agent might escalate to a refund_agent
Pros:
More flexible for dynamic workflows
No single point of failure
Cons:
Harder to debug (complex interactions)
When to Split Into Multiple Agents
Too Many Tools (>15): If a single agent juggles too many tools, it may struggle to pick the right one.
Example: A support agent with 20+ tools for tickets, refunds, surveys, etc. → Split into specialized agents.
Complex Logic (Nested Conditionals): If prompts look like spaghetti code (“If X, then Y, unless Z, then A…”), it’s time to decentralise.
Example:
❌ Single agent handling loan approvals with 50+ rules →
✅ Split into risk_assessment_agent, document_verification_agent, etc.
Conflicting Objectives: Example: A sales agent trying to upsell while also handling complaints → Split into separate agents.
Key Takeaways
Start simple (single agent) → Scale to multi-agent only when needed.
Manager pattern = Good for structured workflows (e.g., translation teams).
Decentralized pattern = Better for dynamic routing (e.g., customer support).
Split agents when:
Tools become overwhelming
Logic gets too tangled
Tasks have conflicting goals
This ensures your agents stay efficient, maintainable, and scalable.
8. Guardrails
Purpose: Prevent harmful or off-topic actions. Certain rules which the AI Agent can’t violate
Types:
Input Checks:
- Relevance (e.g., block “What’s the weather?” in banking app)
- Safety (e.g., block prompt injection)
Output Checks:
- Keep the tone aligned
- Avoid offensive content
Tool Safeguards:
- Block risky tools (e.g., require approval for refunds > $500)
Conclusion,
This was a quick summary of the entire guide released by OpenAI for building AI agents. I hope this was useful and makes it quicker for you to understand how to build an AI agent.
AI agents are like smart assistants that can autonomously handle multi-step tasks — from customer service to fraud detection. To build one:
- Start with the right use case — Agents shine for complex, judgment-based tasks (like refund approvals), not simple, repetitive jobs.
- Give it three key parts:
A brain (LLM like GPT-4 for decision-making).
Tools (APIs to fetch data or take actions).
Clear instructions (step-by-step rules).
3. Keep it simple at first — Use one agent with a few tools. Split into multiple agents only if things get too complex.
4. Add guardrails — Safety checks to prevent mistakes or misuse.
The key? Start small, test with real users, and improve over time. Whether you’re automating support, analysing data, or managing workflows, AI agents can save time and effort — if you build them right.
OpenAI’s Practical Guide to Building AI Agents summary was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
