You can create a basic AI agent with a model, clear instructions and a few relevant tools. Start with one task whose completion you can check; getting a prototype to run is much easier than making it dependable enough for production.
What is the simplest way to create an AI agent?
Think of a basic agent as three parts: a model that reasons and makes decisions, instructions that describe its task and boundaries, and tools it can use to interact with external functions or services. Google’s Agent Development Kit (ADK) describes a similar starting point: a model, task instructions and, optionally, tools. A model can answer questions without tools; tools let an agent take steps such as looking up information or calling an API.
As an Amazon Associate I earn from qualifying purchases.
Choose a narrow task with an observable result. For example, an agent might classify incoming support messages into a small set of categories. You can check whether its output uses an allowed category; if it has permission to update a system, you can also inspect whether it chose the right action. Keep the initial failure cost manageable, especially if the agent can change or send anything outside the conversation.
How do you build a first agent?
- Choose one bounded task. State what the agent should accomplish and how you will recognize completion. Prefer a task where an incorrect result is easy to detect and does not cause serious harm.
- Write testable instructions. Specify the agent’s role, the expected output, relevant constraints and what to do when it lacks enough information. Plain language is enough to begin; vague goals such as “handle everything” are not.
- Add only necessary tools. Give each tool a clear name and description. Grant only the access needed for the task, and be especially cautious with tools that send messages, alter records or trigger other consequential actions.
- Run realistic examples. Include ordinary cases, ambiguous inputs and cases the agent should decline or escalate. Inspect its responses, tool choices and errors rather than judging only the final answer.
- Add safeguards according to risk. Use validation, tighter constraints or human review when the task’s consequences warrant them. A person should approve actions that should not happen automatically.
- Evaluate before expanding. Keep representative examples and check how the agent performs on them as you change instructions or tools. Add architectural complexity only when observed runs show a specific limitation.
Should you start with one agent or several?
For many first builds, one agent is the simpler choice. OpenAI’s practical guide puts it directly: “Our general recommendation is to maximize a single agent’s capabilities first.” A single agent avoids introducing coordination between agents before there is evidence that coordination is needed.
#1 Best Overall
![Arduino® UNO™ Q 4GB [ABX00173]- Hybrid Board, Qualcomm Dragonwing QRB2210 microprocessor (MPU) & STM32U585 Microcontroller(MCU), AI Vision, Voice, IoT, Robotics, Linux Debian OS, Wi-Fi 5, USB-C](https://m.media-amazon.com/images/I/51cl4xygwGL._SL160_.jpg)
- Dual-Brain Hybrid Power: Combines the Qualcomm Dragonwing QRB2210 MPU (Quad-core Arm Cortex-A53 @ 2.0 GHz CPU, Adreno GPU, AI acceleration) and the real-time, low-power STM32U585 MCU for advanced applications like object recognition, voice commands, and motion detection.
- AI & Linux Capabilities: Unlocks AI-powered vision and sound solutions; runs Linux Debian OS for coding in Python and supports the Arduino ecosystem with libraries and Sketches; quick start with Arduino App Lab.
- Advanced Features: Equipped with 4 GB LPDDR4 RAM, 32 GB eMMC built-in storage, ideal for single-board computer (SBC) mode, running multiple simultaneous high-level processes, more complex AI or ML models, extensive logs. Dual-band Wi-Fi 5 (2.4/5 GHz), Bluetooth 5.1, and high-speed headers for vision, audio, and display peripherals.
- Seamless Expansion & Connectivity: Features the classic UNO form factor for shields compatibility, an 8x13 LED matrix, and a Qwiic connector for easy expansion with Modulino nodes; power and connect via the USB-C connector.
- Intended Use & Development: The perfect platform for prototyping robotics or IoT projects, empowering innovators with a unified development experience to mix Arduino Sketches, Python scripts, and containerized AI models in a single interface.
Consider dividing work when instructions have become hard to follow, the agent repeatedly chooses the wrong tools, the task contains clearly distinct specialties, or context and code organization are getting in the way. Google ADK also identifies instruction-following performance, context limits, code modularity, and mixing deterministic processes with model-driven steps as reasons a workflow may help. Multiple agents can make a system easier to organize in those cases, but they also add coordination and implementation overhead.
When is a prototype ready for real use?
A working demonstration proves that a model can attempt the task; it does not establish that the agent will perform reliably across the situations it will encounter. Before relying on it, evaluate representative cases, inspect tool use and failure modes, and decide where validation or human approval is necessary. The more consequential an action is, the less appropriate it is to rely on an unchecked model decision.
Rank #2
- Dual-Brain Hybrid Power: Combines the Qualcomm Dragonwing QRB2210 MPU (Quad-core Arm Cortex-A53 @ 2.0 GHz CPU, Adreno GPU, AI acceleration) and the real-time, low-power STM32U585 MCU for advanced applications like object recognition, voice commands, and motion detection.
- AI & Linux Capabilities: Unlocks AI-powered vision and sound solutions; runs Linux Debian OS for coding in Python and supports the Arduino ecosystem with libraries and Sketches; quick start with Arduino App Lab.
- Advanced Features: Equipped with 2 GB LPDDR4 RAM, 16 GB eMMC built-in storage, ideal to develop in PC-connected mode, running the OS, Python scripts, and basic network services (SSH) without a demanding GUI or heavy multitasking; great for lightweight AI and memory-optimized TinyML applications, needing local storage for basic OS and core libraries. Dual-band Wi-Fi 5 (2.4/5 GHz), Bluetooth 5.1, and high-speed headers for vision, audio, and display peripherals.
- Seamless Expansion & Connectivity: Features the classic UNO form factor for shields compatibility, an 8x13 LED matrix, and a Qwiic connector for easy expansion with Modulino nodes; power and connect via the USB-C connector.
- Intended Use & Development: The perfect platform for prototyping robotics or IoT projects, empowering innovators with a unified development experience to mix Arduino Sketches, Python scripts, and containerized AI models in a single interface.
- Check outputs: Validate format, allowed values and required information where those checks can be made deterministically.
- Check actions: Review which tools were called and whether the agent had more access than its task required.
- Plan for uncertainty: Define what should happen when information is missing, instructions conflict or a tool fails.
- Keep a human in the loop when needed: Require approval for actions whose mistakes would be costly or difficult to reverse.
- Re-evaluate changes: Re-run representative cases when you modify instructions, tools, models or workflow logic.
Which framework should you use?
A framework is an implementation choice, not a guarantee of quality. Compare the languages and model providers it supports, the integrations you need, how it handles state and context, its tracing and evaluation options, and who controls the runtime, deployment and approval decisions. The sources cited here do not establish a neutral benchmark showing that one framework is universally faster or better.
| Option | Languages and model choices | Runtime and deployment | Tools and orchestration | State, observability and evaluation |
|---|---|---|---|---|
| OpenAI Agents SDK | Typed TypeScript or Python application code; consult the current documentation for model and provider details. | The application server controls deployment, tool implementation, storage and approval decisions. | Documentation covers agent definitions, running agents and orchestration; consult current docs for available integrations. | Documentation includes state, observability, guardrails, human review and evaluations. |
| Google ADK | Consult current ADK documentation for supported languages and model or provider options. | Consult current documentation for deployment and runtime choices. | Supports workflows combining multiple agents and executable nodes. | Its documentation discusses context limits and workflow design; consult current docs for state, tracing and evaluation details. |
For a code-first OpenAI path, the Agents SDK documentation points from its quickstart to agent definitions, models and providers, running agents, orchestration, guardrails and human review, state, observability and evaluations. Google ADK is another framework path, particularly when its workflow patterns suit the task. Check the live documentation for current setup and capabilities: OpenAI Agents SDK documentation and Google ADK documentation.
Rank #3
- Single core ARM Cortex-A7 32-bit core, integrated with NEON and FPU
- Built in Micro's self-developed 4th generation NPU, with high computational accuracy and support for mixed quantization of int4, int8, and int16. Among them, int8 has a computing power of 0.5 TOPS and int4 has a computing power of up to 1.0 TOPS
- Built in self-developed 3rd generation ISP3.2, supports 4 million pixels, and supports various image enhancement and correction algorithms such as HDR, WDR, and multi-level denoisin
- It has powerful encoding performance, supports intelligent encoding, adapts to save bit rates according to the scene, and saves more than 50% of the bit rate compared to conventional CBR mode, making the captured images high-definition, smaller in size, and doubling the storage space
- The design with built-in RISC-V MCU supports low-power fast startup, 250ms fast capture, and simultaneous loading of AI model library, enabling facial recognition to be completed within 1 second
What changed in the agent-building landscape?
On March 11, 2025, OpenAI announced the Responses API, built-in web search, file search and computer-use tools, the Agents SDK, and observability capabilities as building blocks for agents. That announcement is historical context, not a guarantee of current availability, rollout status or pricing. Check current product documentation for details before choosing a service: OpenAI’s March 2025 announcement.
Where can you learn more?
If you prefer a book-length guide, Manning lists Build an AI Agent (From Scratch) by Jungjun Hur and Younghee Song as a practical guide to agent design, development and deployment. It is optional; you do not need to read a book before building a small prototype. LangChain Academy’s course catalog is another learning path for readers who choose that ecosystem, with courses on LangChain, LangGraph and multi-agent applications. Course catalogs and product documentation can change, so confirm current offerings directly: Manning’s book page and LangChain Academy.
Quick Recap
Rank #4
- 【POWERFUL ESP32‑S3 CONTROLLER】Built‑in Xtensa 32‑bit LX7 dual‑core processor, 512KB SRAM, 8MB PSRAM, 16MB Flash for stable AI voice computing and multitask processing.
- 【Preloaded Dual AI Platforms】Comespre-installed with complete Deepseek and OpenAI voice dialogue projects.Experience intelligent voice interaction instantly. (Note: OpenAI functionality requires your own API key.)
- 【STABLE WIRELESS & CLEAR AUDIO】Integrated 2.4GHz Wi‑Fi + Bluetooth 5 (LE); dedicated audio decoding module for natural, responsive voice interaction.
- 【USER‑FRIENDLY VISUAL & PLUG‑AND‑PLAY】2” TFT‑SPI color screen shows real‑time chat; modular design, no extra wiring, ready to use after setup.
- 【FULL LEARNING SUPPORT】45 programmable GPIOs, rich interfaces, online web tutorials, free technical support for beginners & developers.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
