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You can create a basic AI agent with one model, clear instructions, and a few carefully chosen tools. Start with a single task that has an observable finish line; making the prototype dependable enough for real users takes testing, safeguards, and ongoing evaluation.
What makes an AI agent different from a chatbot?
A chatbot can generate a response from a prompt. An agent can also choose and use tools—such as a function or API—to carry out steps toward a task. OpenAI’s practical guide to agents describes three core parts: a model that reasons and makes decisions, tools it can use, and instructions that define its behavior. Google’s Agent Development Kit documentation describes a similar foundation: a model, task instructions, and optionally tools.
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That is enough to begin. The key is to keep the task narrow and the agent’s authority limited. A prototype that can complete a small, low-risk workflow is a realistic first goal—not a promise that one prompt produces a production-ready system.
How do you create a first AI agent?
- Choose one bounded task. Define an input and an observable completion condition. For example, an agent might classify support messages into a fixed set of categories and return the category with a brief reason. Keep the consequences of a mistake manageable while you learn.
- Write testable instructions. State the agent’s role, what it should do, what it must not do, and what to do when information is missing or uncertain. Prefer specific requirements you can check over vague directions such as “be helpful.”
- Add only necessary tools. A tool gives the agent a way to interact with an external function or service. Give each tool a clear name and description, and avoid granting access unrelated to the task. Tool calls can cause real changes, so distinguish actions the agent may take on its own from those that need approval.
- Run realistic examples. Try ordinary inputs, incomplete information, and likely edge cases. Inspect not just the final answer but also the tool calls the agent made, whether they were appropriate, and where a run failed.
- Add safeguards in proportion to risk. Use validation, human review, or tighter constraints when errors could have meaningful consequences. A useful early boundary is to let an agent prepare an action for review rather than execute it automatically.
- Evaluate before expanding. Keep representative cases and check whether the agent meets the completion condition consistently. Add tools or architectural complexity only when runs reveal a specific limitation.
Why start with one agent?
Multiple agents can divide work, but they also introduce coordination and implementation overhead. OpenAI’s guide says, “Our general recommendation is to maximize a single agent’s capabilities first.” In practice, begin with one agent and a focused set of tools; consider a multi-agent workflow when there is a concrete reason to split the work.
#1 Best Overall
Signs a more complex workflow may help
- The instructions have become difficult to follow reliably.
- The agent repeatedly chooses the wrong tool despite clear descriptions.
- The task contains specialties that can be separated cleanly.
- Context limits, code organization, or a mix of predictable and judgment-based steps are getting in the way.
These are reasons to investigate a split, not proof that more agents will automatically improve results. Google ADK also documents workflows that combine multiple agents and executable nodes; use such patterns when the task’s structure warrants them.
Which framework should you use?
A framework is an implementation choice, not a prerequisite for understanding the basic design. Compare current documentation against your language, model and provider needs, runtime ownership, integrations, state and context requirements, tracing and evaluation support, and the control you need over deployment, tools, and approvals. The sources cited here do not establish a neutral benchmark showing that one framework is faster or better overall.
| Consideration | OpenAI Agents SDK | Google ADK |
|---|---|---|
| Languages and model/provider choices | Documentation covers typed TypeScript or Python application code, models, and providers. Check the current SDK documentation for supported options. | Check the current ADK documentation for supported languages and model/provider options. |
| Runtime and deployment control | The application server controls deployment, tool implementation, storage, and approval decisions. | Check current ADK documentation for runtime and deployment options that fit your application. |
| Tools and integrations | Define tools in application code and choose those relevant to the task. | Review the documented tools and integrations for the services your task needs. |
| Orchestration | The SDK documentation covers orchestration and running agents. | ADK documents workflows that can combine multiple agents and executable nodes. |
| State, tracing, and evaluation | The documentation includes state, observability, and evaluations as topics to use as needed. | Check the current documentation for its state, tracing, and evaluation capabilities. |
The table is a starting point for checking fit, not a performance ranking. Capabilities and product details can change, so use the linked official documentation for current support and setup instructions.
When does a prototype become a dependable application?
A prototype demonstrates that an agent can complete a bounded example. A dependable application must also handle unanticipated inputs, tool failures, incorrect decisions, and the consequences of its actions. Treat those as separate milestones: inspect runs and evaluate representative cases before increasing autonomy or relying on the agent in a consequential workflow.
For a code-first OpenAI path, the Agents SDK documentation points to its quickstart and then topics such as agent definitions, models and providers, running agents, orchestration, guardrails and human review, state, observability, and evaluations. Its application-code approach leaves deployment, tool implementation, storage, and approval decisions under application-server control. Google ADK offers another framework path; choose based on the workflow and the controls you need rather than popularity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you learn next?
If you want a guided, deeper treatment, Manning lists Build an AI Agent (From Scratch) by Jungjun Hur and Younghee Song as a practical book covering agent design, development, and deployment. It is optional; you can start with the official framework documentation without reading it first.
If you decide to use the LangChain ecosystem, LangChain Academy’s course catalog lists agent-building courses and projects, including material on multi-agent applications. Course catalogs can change, so check the current listing for available content.
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