Aug 22 • AI Tools
Build Your First AI Agent: No Coding Required
An AI agent is defined as a system capable of reasoning, planning, and taking actions autonomously based on given information. It can manage workflows, utilize external tools, and adapt to changing circumstances, essentially acting like a "digital employee" that can think, remember, and accomplish tasks.
A key distinction is made between AI agents and automations:
• Automations follow predefined, fixed, rule-based steps, running from A to B to C without reasoning. Even complex automations that use AI, like summarizing top Reddit posts, are not agents if they lack dynamic decision-making.
• Agents, by contrast, are dynamic, flexible, and capable of reasoning, making decisions on the fly to complete tasks and choosing tools and actions as needed. For example, a simple weather agent can reason about whether to recommend an umbrella based on weather data, adapting its response.
AI agents rely on three core components:
1. The Brain: This is the large language model (LLM) that powers the agent, such as ChatGPT, Claude, or Google Gemini. It handles the agent's reasoning, planning, and language generation.
2. Memory: This gives the agent the ability to remember past interactions and use that context to make better decisions. It can recall previous steps in a conversation or draw from external sources like documents or vector databases. A "simple memory" option is available for temporary context within a single run, with a configurable context window length to remember a set number of previous messages.
3. Tools: These are how the agent interacts with the outside world. Tools typically fall into three categories:
◦ Retrieving data or context (e.g., searching the web, pulling information from a document).
◦ Taking action (e.g., sending an email, updating a database, creating a calendar event).
◦ Orchestration (e.g., calling other agents, triggering workflows, chaining actions). Tools can include common services like Gmail, Google Sheets, or Slack, as well as specialized APIs (Application Programming Interfaces) like NASA's API or advanced math solvers.
Understanding APIs and HTTP Requests:
• API (Application Programming Interface): This is how different software systems communicate and share information or actions, acting like a "vending machine" where you make a request and get a response. It defines what requests are possible.
• HTTP Request: This is the actual action of making a request to an API, like pressing a button on the vending machine. The most common types are "Get" (to pull information) and "Post" (to send information).
• Function: A specific action available through an API, such as "get weather" or "create event," which the agent calls when sending a request.
Building an AI Agent using N8N (No Coding): The video demonstrates building an agent using N8N, a visual interface tool for automations and agents.
1. Workflow Setup: Workflows are built by dragging and dropping "nodes," each representing a specific step (e.g., calling an API, using ChatGPT).
2. AI Agent Node: N8N features a dedicated AI agent node where the three core components (brain, memory, tools) can be plugged in.
3. Setting up the Brain (LLM): Users select their preferred language model (e.g., OpenAI's GPT-4 Mini, Claude, Gemini) and connect it using an API key and credentials. Funding an OpenAI API account separately from ChatGPT Plus may be required.
4. Setting up Memory: A simple memory option is available for temporary context during a single run, with a configurable context window length. N8N allows direct chat interaction with the agent to test memory functionality.
5. Adding Tools: N8N offers a wide range of pre-built integrations for services like Google Calendar, Open Weather Map, Google Sheets, and Gmail. For services not pre-integrated, a custom tool can be built using an HTTP request node to connect to any public API. An example is given for connecting to airnow.gov's API to get air quality data, which involves finding the API documentation, creating an account, generating an API key, and constructing a URL for a "Get" request. The "optimize response" checkbox in N8N helps autoparse JSON data for easier LLM use.
6. Writing the Prompt: This is the crucial final step where you instruct the agent on its role, task, available data, tools it can use, constraints, and desired output. ChatGPT can be used to generate a structured prompt based on the agent's purpose.
7. Error Handling: The video demonstrates a common error-fixing process by screenshotting error messages and asking ChatGPT for step-by-step instructions to resolve issues.
Types of Agent Systems and Guardrails:
• Single-agent systems: These are recommended as a starting point and are often sufficient.
• Multi-agent systems: More complex setups where one agent acts as a manager, delegating tasks to specialized sub-agents (e.g., for research, sales, customer support). These systems are compared to human organizational structures.
• Guardrails: These are essential to prevent agents from hallucinating, getting stuck in loops, or making bad decisions, especially for business applications. They involve identifying risks and edge cases, optimizing for security and user experience, and adjusting over time as the agent evolves.
Practical Applications and Resources: AI agents can be used for tasks like:
• AI assistants that summarize emails and tasks.
• Social media managers that generate and post content.
• Customer support agents that reply to common questions.
• Research assistants that fetch real-time data.
• Personal travel planners. The video highlights an example of building a personal assistant that checks a calendar for run events, assesses local weather and air quality, and recommends a suitable trail from a saved list, then messages the suggestion.
Two free resources are offered by HubSpot as companions to the video:
1. A document covering core concepts, specific use cases across marketing, sales, and operations, a guide on building a smart human-AI collaboration strategy, pitfalls, and best practices.
2. "How to Use AI Agents in 2025," a practical checklist for organizations to adopt AI agents effectively.
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Build Your First AI Agent: No Coding Required
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