Introduction to AI Agents by OpenAI

Introduction

AI agents are LLM‑powered systems that autonomously execute multi‑step workflows on behalf of users, dynamically selecting tools and adapting to changing contexts. Agents “intelligently accomplish tasks, ranging from executing simple workflows to pursuing complex, open‑ended objectives” OpenAI Platform. On March 11, 2025, OpenAI released a suite of building blocks—including the new Responses API, built‑in tools, integrated observability, and the Agents SDK—to streamline agent development for both developers and enterprises

Core Components of AI Agents

What Makes an Agent

At its heart, an agent:

Leverages an LLM to manage workflow execution, recognize completion conditions, and self‑correct or hand control back to the user upon failure.

Uses Tools—external functions or APIs—to gather context and take actions, dynamically chosen based on the workflow state.

Operates Within Guardrails, explicit guidelines that validate inputs and constrain behavior to ensure safety and predictability.

SDK Primitives

OpenAI’s Agents SDK abstracts these components into three lightweight primitives:

Agents (LLMs with instructions and tools)

Handoffs (delegation mechanisms between agents)

Guardrails (input‑validation checks) OpenAI GitHub.

Together, these primitives let you express complex multi‑agent workflows with minimal boilerplate and built‑in tracing for debugging and evaluation.

Building Agents with the OpenAI Agents SDK

To get started, install and initialize the SDK:

1pip install openai-agents
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1from agents import Agent, Runner
2
3agent = Agent(
4    name="Assistant",
5    instructions="You are a helpful assistant who can manage tasks end-to-end.",
6    tools=[/* your tool instances here */],
7    input_guardrails=[/* optional guardrail functions */]
8)
9
10result = Runner.run_sync(agent, "Please draft an executive summary of last quarter’s sales data.")
11print(result.final_output)
12
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This “Hello World” example demonstrates the core loop: the agent uses tools, loops until an exit condition, and returns structured output—all with built‑in observability OpenAI GitHub.

Best Practices for Agent Design

Clear, Structured Instructions

Leverage Existing Docs: Transform operating procedures or policy texts into numbered, unambiguous steps for the agent.

Break Down Tasks: Prompt the agent to decompose complex routines into smaller actions to minimize ambiguity.

Anticipate Edge Cases: Include conditional branches for missing inputs or unexpected queries OpenAI.

Guardrails & Safety

Run guardrails in parallel for prompt‑injection prevention, policy enforcement, and content‑safety checks.

Optimistic Execution: Allow the agent to generate outputs while guardrails monitor; exceptions trigger safe fallback behavior.

Human‑in‑the‑Loop: Escalate high‑risk or repeated failures to human operators to refine performance and handle sensitive actions.

Model Selection & Optimization

Establish Baselines using the most capable models, then swap in smaller, faster variants to meet cost and latency targets.

Incremental Orchestration: Start with single‑agent loops; evolve to multi‑agent graphs only when complexity demands it.

Real‑World Applications

Financial Research: OpenAI’s “Deep Research” agent autonomously browses the web for expert‑level analysis, launched February 3, 2025 Wikipedia.

Web Automation: The “Operator” agent fills forms, places orders, and schedules appointments via real‑world browser interactions; released January 23, 2025 Wikipedia.

Enterprise Prototyping:

Coinbase used the Agents SDK to build AgentKit, enabling agents to interact with crypto wallets and on‑chain data in hours OpenAI.

Box created agents that combine secure internal data queries with live web search to surface real‑time insights for enterprise users OpenAI.

Future Directions

OpenAI plans to:

Deprecate the Assistants API by mid‑2026 in favor of the Responses API as their unified, tool‑enabled primitive OpenAI.

Release additional built‑in tools, deeper API integrations, and enhanced evaluation capabilities to support agents at scale OpenAI.

Conclusion & Next Steps

Agents represent the next frontier in workflow automation—intelligent, adaptable, and capable of full‑cycle task execution. To start building:

Explore the APIs.

Install the Agents SDK and experiment with guardrails and handoffs.

Validate with real users, iterate on instructions, and monitor performance using built‑in tracing.

With a solid foundation and an iterative mindset, your team can leverage OpenAI’s agentic toolkit to automate entire workflows, unlock new efficiency gains, and deliver real business impact.

Sources

New tools for building agents — OpenAI blog post introducing the Responses API, built‑in tools, and Agents SDK, published March 11, 2025. Link OpenAI

Agents Guide — Official OpenAI documentation on AI Agents, outlining their capabilities and use‑cases. Link OpenAI Platform

OpenAI Agents SDK — Detailed guide to the Agents SDK, showing how to orchestrate single‑ and multi‑agent workflows. Link OpenAI Platform

You can access OpenAI's "A Practical Guide to Building Agents" directly via the following link:

Introduction to AI Agents