A practical guide to building agents is a guide published by OpenAI for product and engineering teams exploring how to build their first agents. It presents itself as distilling insights from numerous customer deployments into practical and actionable best practices, covering frameworks for identifying promising use cases, patterns for designing agent logic and orchestration, and practices intended to keep agents running safely and predictably.
Its stated scope is deliberately foundational: after reading it, the guide says, the reader will have the knowledge needed to start building a first agent. It is organised into five parts — what an agent is, when to build one, agent design foundations, guardrails, and a conclusion — and illustrates each design point with short Python examples written against OpenAI Agents SDK, while noting the same concepts can be implemented with any preferred library or from scratch.
Details
The guide defines agents as systems that independently accomplish tasks on a user’s behalf, and draws the line at workflow control: applications that integrate LLMs but do not use them to control workflow execution — simple chatbots, single-turn LLMs, sentiment classifiers — are not agents. It gives an agent two core characteristics: it uses an LLM to manage workflow execution and decide when a workflow is complete, correcting its actions or halting and returning control to the user on failure; and it has access to tools with which to interact with external systems, selecting among them dynamically and always within defined guardrails.
On when to build one, the guide argues agents suit workflows that have resisted conventional automation, and names three: complex workflows involving nuanced judgment or exceptions, systems made difficult to maintain by extensive and intricate rulesets, and workflows relying heavily on unstructured data. Its worked contrast is payment fraud analysis, where a traditional rules engine works like a checklist while an LLM agent evaluates context and subtle patterns. Where a use case does not clearly meet these criteria, the guide says a deterministic solution may suffice.
Its design foundations are three components — the model powering reasoning, the tools the agent can call, and the instructions defining its behaviour. It recommends prototyping with the most capable model for every task to establish a baseline, then swapping in smaller models to find where they still suffice. Tools are grouped into data, action and orchestration tools, the last being agents exposed as tools to other agents. For instructions, it recommends deriving routines from existing operating procedures and policy documents, breaking dense resources into smaller steps, making every step correspond to a specific action or output, and anticipating edge cases with conditional branches.
Orchestration is presented as a choice between single-agent systems, in which one model equipped with tools runs in a loop until an exit condition is reached, and multi-agent systems. The guide’s general recommendation is to maximise a single agent’s capabilities first, adding tools incrementally, and to reach for multiple agents when prompts accumulate many conditional branches or when tools overlap enough to confuse selection — noting that the issue is tool similarity rather than tool count, with some implementations managing more than 15 distinct tools and others struggling with fewer than 10 overlapping ones. Where multiple agents are warranted, it names two broadly applicable categories: the Manager Pattern and the Decentralized Pattern.
The final section treats Guardrails as a layered defence rather than a single control, combining LLM-based classifiers, rules-based protections such as regex and blocklists, and a moderation API. It lists seven types — relevance classifier, safety classifier, PII filter, moderation, tool safeguards, rules-based protections and output validation — and offers a three-step heuristic for building them: focus first on data privacy and content safety, add guardrails as real-world edge cases and failures appear, and tune for both security and user experience as the agent evolves. It closes by treating Human-in-the-loop intervention as a critical safeguard, especially early in deployment, triggered by exceeding failure thresholds or by high-risk actions.