A practical, recently released, 34-page guide from OpenAI is designed to help technical teams build AI agents that go beyond simple chatbots and truly act on behalf of users. Drawing from real-world deployments, the document offers a structured approach to designing, orchestrating, and safely deploying agents powered by large language models. It covers when and why to use agents, how to architect them with tools and instructions, and what guardrails are necessary to ensure reliability and trust. Whether you’re exploring agents for the first time or scaling up a production system, this guide offers actionable insights for building intelligent, autonomous workflows.
Since most people don’t have time to slog through 30+ pages, we summarized it for your reading enjoyment. Enjoy!
When to Build an Agent (And When Not To)
Agents aren’t a fit for every use case. They shine where traditional, rule-based systems struggle — especially when:
- The workflow requires judgment or nuance, like evaluating refund claims or onboarding a new vendor.
- There’s unstructured input, such as emails, documents, or natural language queries.
- The system is overloaded with edge cases, making rules hard to maintain or scale.
For example, in payment fraud detection, a static rule engine may flag predictable patterns. But an LLM agent can infer fraud from subtle contextual clues — even if no rule is directly broken.
On the flip side, if your process is simple, linear, and deterministic (e.g., sorting form submissions), a traditional script may be faster and cheaper.
Before committing to building an agent, ensure the task:
- Benefits from contextual awareness
- Has a high cost of failure when misinterpreted
- Involves dynamic user input or a high degree of ambiguity
How to Design an Effective Agent
Every agent is built around three core components:
- Model
The LLM powers reasoning and decision-making. Start with a capable model to establish a strong baseline, then test smaller models to optimize cost and speed without sacrificing quality. - Tools
These are APIs or functions the agent can call to get things done — like sending emails, searching a database, or initiating a refund. Tools are categorized as:- Data tools: to gather context or info (e.g., search, database lookup)
- Action tools: to update systems or notify users
- Orchestration tools: to call other agents or chain tasks
- Instructions
This is where you define exactly what the agent should do, how it should do it, and how to respond when things go off script. Clear instructions dramatically improve reliability.
Best practices for instruction design:
- Break tasks into steps.
- Use existing workflows and SOPs as templates.
- Anticipate edge cases and define fallback behaviors.
- Test across variations in input — especially those that tend to trip up humans.
Building for Scale: From Single Agent to Multi-Agent Systems
Most teams start with a single-agent architecture, and that’s often enough. A single agent can handle complex workflows by being equipped with multiple tools and smart instructions. It operates in a loop until the task is complete, with built-in stop conditions for errors or successful output.
But as complexity grows, teams may choose to split work across multiple agents. This can improve performance and modularity.
There are two main orchestration patterns:
- Manager pattern: A central agent delegates tasks to specialized agents via tool calls. One model keeps control, ensuring consistency.
- Decentralized pattern: Agents hand off control to each other based on task type — useful for triage, customer support, or highly specialized domains.
The decision to use multiple agents often comes down to:
- Tool overload: Too many overlapping tools confuse a single agent.
- Conditional complexity: Prompts with many branches benefit from being broken up.
Both approaches require a reliable “run loop” — a mechanism that keeps the system moving forward, handles handoffs, and gracefully exits when finished.
Guardrails and Safety: Ensuring Agents Operate Responsibly
Because agents act with autonomy, guardrails are essential for safe, predictable performance — especially in sensitive or regulated environments.
Key types of guardrails include:
- Relevance filters: Keep the agent focused on the task.
- Safety classifiers: Prevent prompt injections and unsafe queries.
- PII filters: Catch and suppress personally identifiable information.
- Moderation tools: Flag toxic or harmful inputs.
- Tool risk ratings: Restrict actions based on sensitivity (e.g., issuing refunds vs. viewing help content).
You can layer in these protections:
- Using blocklists and regex to catch known threats
- Embedding classifier models to evaluate real-time output
- Defining “tripwires” that escalate high-risk tasks to humans
Human-in-the-loop is not just a fallback — it’s a strategic safeguard. Trigger intervention when:
- The agent exceeds retry limits
- The action involves significant risk (e.g., financial decisions, customer escalations)
Done right, these layers combine to create systems that are resilient, adaptable, and enterprise-ready.
Learn how AI Agents can supercharge your company’s profits and productivity at TMC’s AI Agent Event in Sept 29-30, 2025 in DC.
Aside from his role as CEO of TMC and chairman of ITEXPO #TECHSUPERSHOW Feb 10-12, 2026, Rich Tehrani is CEO of RT Advisors and a Registered Representative (investment banker) with and offering securities through Four Points Capital Partners LLC (Four Points) (Member FINRA/SIPC). He handles capital/debt raises as well as M&A. RT Advisors is not owned by Four Points.
The above is not an endorsement or recommendation to buy/sell any security or sector mentioned. No companies mentioned above are current or past clients of RT Advisors.
The views and opinions expressed above are those of the participants. While believed to be reliable, the information has not been independently verified for accuracy. Any broad, general statements made herein are provided for context only and should not be construed as exhaustive or universally applicable.
Portions of this article may have been developed with the assistance of artificial intelligence, which may have contributed to ideation, content generation, factual review, or editing.
