AI Agent Development
This tutorial briefing distills essential concepts, frameworks, and practical applications for building AI agents.It aims to provide a clear understanding of AI agents, their components, common workflows, and strategies for identifying impactful use cases.
1. Defining AI Agents
An AI agent is fundamentally "a system that perceives its environment, processes information and autonomously takes actions to achieve specific goals." From a human perspective, AI agents often serve as "an AI counterpart to a human role or a task that a human performs."
Key Characteristics:
- Autonomy: Agents can independently take actions to achieve goals.
- Perception: They gather information from their environment.
- Processing: They analyze and interpret perceived information.
- Goal-Oriented: Their actions are directed towards specific objectives.
Common Use Cases:
- Coding AI Agents: Examples include Cursor and Windsurf, which are AI-powered code editors with "agent mode" capable of autonomously performing coding tasks.
- Customer Service Chatbots: Many companies are experimenting with agents that can "handle inquiries, communicate with the customer, file a complaint for them or to resolve specific issues."
2. The Multi-Agent System Paradigm
A crucial insight is that a single AI agent rarely operates in isolation. Instead, AI agents are "oftentimes a bunch of sub agents that do specific things and ultimately come together in multi-agent systems to form what we perceive as the actual like complete agent." This mirrors human organizational structures, where specialized roles lead to greater efficiency and effectiveness. "Humans have different roles you don't have just one human that is trying to do everything at the same time... and it's the same for agents when we have different agents that are specialized in different things the results of it all coming together is going to be far better than just having a single AI agent try to do everything."
3. Core Components of an AI Agent (OpenAI Framework)
OpenAI offers a comprehensive framework for understanding the essential components of an AI agent, likening them to the components of a burger: you can swap out types, but all are necessary for a functional agent. Even as new tools emerge, they will generally fit within these categories.
The six crucial components are:
Models:
These are the "AI models your large language models that are the core intelligence capable of reasoning, making decisions and processing different modalities."
Examples:
- OpenAI models (GPT-4o, GPT-4.5, O3 Mini, O3 Mini High)
- Claude 3.7 Sonnet
- Gemini 2.5 Pro
Selection Criteria:
- Reasoning/Complex Problem Solving: GPT-4o (flagship), Claude 3.7 Sonnet (for coding/STEM).
- Writing/Exploring Ideas: GPT-4.5.
- Speed/Cost-Effectiveness: Smaller models, most Google models, open-source models (self-hosted).
- Context Window: Google models often offer longer context windows.
Resource:
- Websites like Vellum rank model performance to aid selection.
Tools:
Tools empower models to "interface with the world," extending their capabilities beyond their base intelligence.
Functionality:
- Web search, accessing applications (Gmail, Calendar, Slack, Discord, YouTube, Salesforce, Zapier), hard drive access, screen access.
Customization:
- Developers can build custom tools using OpenAI's agents SDK (requires coding).
MCP (Model Context Protocol):
- Developed by Anthropic, MCP standardizes how tools are provided to LLMs, simplifying development.
No-Code/Low-Code Options:
- Platforms like N8N offer drag-and-drop interfaces for integrating tools (e.g., internet search, data analysis, email for a market research agent).
Knowledge & Memory:
Essential for providing agents with both static and dynamic information.
Knowledge Base (Static Memory):
- "Static facts, policies, documents, just information that I can reference and access that remains relatively static over time." (e.g., legal documents, company policies).
Persistent Memory:
- "Memory that will allow an AI agent to be able to track conversation histories or user interactions past just a single session." (e.g., for personal assistants).
Implementation:
- OpenAI's vector stores, file search, embeddings; open-source databases; Retrieval Augmented Generation (RAG) solutions like Pinecone or Weaviate. No-code solutions often handle this natively.
Audio & Speech:
Enables natural language interaction, significantly enhancing user experience.
Innovations:
- Recent advancements in audio formats make this a distinct category.
Tools:
- OpenAI's own implementations, 11 Labs (voice cloning/generation), Whisper (audio transcription).
Guardrails:
Crucial for preventing "irrelevant, harmful or undesirable behavior" and ensuring the agent stays on task.
Purpose:
- Ensures a customer service agent focuses on customer service, not generating haikus.
Tools:
- Guardrails AI, LangChain Guardrails. Many no-code platforms include built-in guardrail solutions.
Orchestration:
Overlooked but vital for managing, deploying, monitoring, and improving multi-agent systems.
Functionality:
- Chaining sub-agents, deployment, continuous monitoring, and improvement.
Tools:
- OpenAI's system, CrewAI (for multi-agent systems), LangChain (agent interactions, deployment), LlamaIndex (document-heavy agents, static memory).
4. Common Agentic Workflows
The Anthropic "Building Effective Agents" guide outlines several common patterns for structuring AI agent systems, moving from simple to more autonomous. A general rule of thumb is to always "go with the simplest implementation possible."
Augmented LLM (Basic Building Block):
The foundational unit where an LLM generates search queries, selects tools, and manages memory. Often referred to as a "sub-agent."
Prompt Chaining:
- Concept: Decomposes a task into a sequence of steps, where "each sub agent processes the output of the previous one." (e.g., report generation: outline -> checker -> writer -> editor).
- Ideal for: Tasks easily broken into sequential subtasks.
Routing:
- Concept: An input is directed by a primary sub-agent to a "specific follow-up task," each handled by a specialized sub-agent. (e.g., customer service: general, refund, technical support).
- Ideal for: Complex tasks with distinct, separately handled categories. Can also route to different models based on query complexity.
Parallelization:
- Concept: Sub-agents work simultaneously on a task, and their "outputs then aggregated together."
- Variations:
- Sectioning: Breaking a task into independent subtasks run in parallel (e.g., evaluating different aspects of model performance).
- Voting: Running the same task multiple times with different sub-agents to get diverse outputs for aggregation (e.g., code vulnerability review).
Orchestrator-Workers:
- Concept: Similar to parallelization but without a predetermined list of subtasks. An orchestrator dynamically assigns tasks to workers as needed.
- Ideal for: Complex problems where subtasks cannot be predicted (e.g., coding agents changing multiple files, research assistants gathering diverse information).
Evaluator-Optimizer:
- Concept: Involves an iterative refinement loop where a generator sub-agent produces a solution, an evaluator sub-agent assesses it against clear criteria, and provides feedback for improvement until the solution is deemed satisfactory.
- Ideal for: Situations with clear evaluation criteria and where iterative refinement is beneficial (e.g., literary translation, complex research aggregation).
Truly Autonomous Agent:
- Concept: The agent is given an open-ended task and independently "figures out itself how to do the thing," performing actions and reacting to the environment to judge progress.
- Ideal for: Very open-ended problems where the number of steps or exact path is unpredictable (e.g., complex software engineering tasks, computer use agents).
Caution: While powerful, this approach can be unpredictable and is "not something that you generally want to do because in most situations you can actually go with a more predetermined agentic workflow and it would yield more predictable results and be a lot cheaper."
5. Practical Prompt Engineering for AI Agents
Effective prompt engineering is paramount as "the prompt is literally the thing that's going to make or break your agent." The full prompt must be provided upfront, as interactive correction is not possible.
Six Key Components of an AI Agent Prompt:
Role:
Define the agent's identity, tone, and behavior (e.g., "You are an AI research assistant task with summarizing the latest news in artificial intelligence. Your style is succinct, direct and focus on essential information.").
Task:
Clearly state what the agent needs to accomplish (e.g., "Given a search term related to AI news, produce a concise summary of the key points.").
Input:
Specify exactly what information the agent will receive (e.g., "The input is a specified AI related search term provided by the user.").
Output:
Detail the desired final deliverable, including format, length, and content (e.g., "Provide only a succinct information dense summary capturing the essence of recent AI related news relevant to the search term. The summary must be concise, approximately two to three short paragraphs totaling no more than 300 words.").
Constraints:
Crucially, define what the agent should not do or include (e.g., "Focus on capturing the main point succinctly; complete sentences and perfect grammar are not necessary; ignore fluff, background information and commentary; do not include your own analysis or opinions.").
Capabilities & Reminders:
- Tools: "You have access to the web search tool to find and retrieve recent news articles relevant to the search term."
- Reminders: Address common LLM limitations, like date awareness: "You must be deeply aware of the current date to ensure the relevance of news summarizing only information published within the past seven days."
Tip: Place the most important reminders lower down in the prompt as AI models have a bias towards processing more recent information first.
6. Real-World AI Agent Implementations
The briefing provides examples of AI agents built using both no-code/low-code tools and full code.
No-Code/Low-Code (using N8N):
- Customer Support AI Agent: Implements a routing pattern. Classifies email inquiries (technical support, billing, general) and routes them to specific workflows. Includes escalation to human agents for complex issues.
- AI News Aggregator Agent: Utilizes a parallelization workflow (though N8N processes sequentially). Gathers news from various sources, aggregates, and summarizes for delivery (e.g., via WhatsApp). Emphasizes source citation in the prompt.
- Multi-Input Daily Expenses Tracker AI Agent: Interacts via WhatsApp, processing text and image inputs (receipts). Aggregates expenses, stores them in Google Sheets, and sends daily reports. This example implicitly uses evaluator-optimizer or orchestrator-worker patterns to handle diverse inputs and continuous tracking.
Coded Implementation (using OpenAI's Agents SDK in Python):
- Financial Research Assistant: Follows a prompt chaining pattern, orchestrated by a "main manager."
- Workflow: User query -> Planner agent (breaks down query into search terms) -> Search agent (performs searches, aggregates results) -> Analysis phase (Financials agent, Risk agent) -> Writer agent (synthesizes report) -> Verifier agent (checks accuracy).
- Additional Features: Voice interaction for querying the report, translation capabilities (demonstrating MCP for tool access).
7. Identifying AI Agent Business Opportunities
Building useful AI agents requires identifying real-world problems.
Start with Yourself:
"What is it that you're currently doing that if you were to offload to an AI agent would make your life so much easier?" (e.g., an agent to screen sponsorship emails for good leads).
Go Undercover (for those without direct work experience):
Shadow individuals or businesses to "figure out their problems." Identify tasks that can be automated, even if the person doesn't realize it. "You're coming in with a fresh pair of eyes so look at what they're doing and try to identify where it is that you can build an AI agent."
"AI Agent Equivalent" of SaaS Companies:
A profound insight from Y Combinator is that "for every SAS company that you see out there software as a service company that you see out there there will be an AI agent equivalent of that." This provides a clear framework for ideation.
8. Current Tech-Enabled Innovations (as of 2025)
The AI industry is rapidly evolving, with significant advancements in specific modalities:
Voice and Audio:
"Audio generation is just freaking unreal right now." Innovations like those from Sesame enable highly realistic voice cloning and generation, opening up numerous use cases for voice agents. This is why OpenAI dedicates a component category to it.
Image Models:
Massive developments in image generation (e.g., Rev, Gemini Flash image generation, GPT-4o image generation).
Video Models:
Advances in video generation (e.g., Sora).
These areas are "ripe for disruption" and represent fertile ground for building new AI agents.
9. Overarching Advice
Focus on Fundamentals:
In a rapidly changing field, understanding the "fundamental components, the fundamental frameworks and the fundamental technologies" is key. This allows you to categorize new innovations and determine their true importance.
Be Patient:
"Keep learning, keep doing your own projects, build out your own AI agents and when the time comes when the opportunity comes where your skill set and your interest they align together with what is in demand in the world right now you'll be off building a successful AI agent business or startup or just side hustle or fun project as well."
Frequently Asked Questions
AI Agent Development: Components, Workflows, and Opportunities
An AI agent is a system that perceives its environment, processes information, and autonomously takes actions to achieve specific goals. From a human perspective, they often act as AI counterparts to human roles or tasks, such as coding AI agents or customer service chatbots. For implementation, AI agents are frequently composed of multiple "sub-agents" that specialize in distinct tasks, forming multi-agent systems. This approach mirrors human organizations where different individuals specialize in different roles, leading to far better results than a single entity attempting to do everything. This specialization allows for more effective processing, prioritization, and overall performance.
OpenAI provides a comprehensive framework for understanding the crucial components of an AI agent, which include:
- Models: These are the core AI models, such as large language models (LLMs), responsible for reasoning, decision-making, and processing various data types. Examples include GPT-4o, GPT-4.5, Claude Sonnet, and Gemini Pro.
- Tools: Tools empower AI agents to interact with the world by giving them capabilities like web search, access to applications (e.g., Gmail, Slack), or custom-built functionalities. The Model Context Protocol (MCP) helps standardize how tools are provided to LLMs.
- Knowledge and Memory: This category includes static memory (knowledge bases) for referencing factual information, policies, or documents, and persistent memory for tracking conversation histories or user interactions across sessions.
- Audio and Speech: This component enables agents to interact using natural language through audio input and speech generation, crucial for enhancing user experience in chatbots.
- Guardrails: Guardrails are essential to prevent AI agents from exhibiting irrelevant, harmful, or undesirable behavior, ensuring they stay on task and adhere to predefined guidelines.
- Orchestration: This component involves chaining together different sub-agents, deploying the agent, monitoring its performance, and continuously improving it over time. Frameworks like Crew AI and LangChain are popular for orchestration.
Agentic workflows describe how sub-agents interact to form a larger AI agent, ranging from simple to truly autonomous:
- Prompt Chaining: Tasks are decomposed into a sequence of steps, with each sub-agent processing the output of the previous one, like an assembly line. Ideal for easily decomposable tasks.
- Routing: An initial sub-agent directs incoming input to a specialized sub-agent based on the input's nature. This is highly effective for complex tasks with distinct categories (e.g., customer service inquiries).
- Parallelization: Sub-agents work simultaneously on a task, with their outputs aggregated. This has two variations: "sectioning" (breaking a task into independent, parallel subtasks) and "voting" (running the same task multiple times with different sub-agents to get diverse outputs).
- Orchestrator Workers: Similar to parallelization, but without a predetermined list of subtasks. An orchestrator dynamically assigns tasks to worker agents, useful for complex problems where exact subtasks cannot be predicted (e.g., coding, research).
- Evaluator Optimizer: An agent generates a solution, which is then evaluated by another sub-agent. If the solution isn't good enough, feedback is provided for iterative refinement in a circular loop until criteria are met. Useful when clear evaluation criteria and iterative improvement are possible.
- Truly Autonomous Agents: After an initial human interaction to clarify the task, the agent operates completely independently, performing actions, receiving environmental feedback, and self-determining progress until the task is completed. This is suited for very open-ended problems but can lead to unpredictable results. The general rule of thumb is to always go with the simplest implementation possible.
Effective prompt engineering is crucial for an AI agent's performance as it holds everything together. A complete prompt for an AI agent should consider six components:
- Role: Define the AI agent's identity, tone, and desired behavior (e.g., "You are an AI research assistant, your style is succinct and direct.").
- Task: Clearly state what the AI agent needs to accomplish (e.g., "Produce a concise summary of key points given a search term.").
- Input: Specify the type and format of information the AI agent will receive (e.g., "The input is a specified AI-related search term provided by the user.").
- Output: Detail the desired format and content of the AI agent's final deliverable (e.g., "Provide only a succinct, information-dense summary... no more than 300 words.").
- Constraints: Crucially, specify what the AI agent should not do, preventing irrelevant or undesirable behavior (e.g., "Ignore fluff, do not include your own analysis or opinions.").
- Capabilities and Reminders: Inform the AI about available tools (e.g., "You have access to the web search tool") and provide important reminders, especially for time-sensitive information or crucial directives, placing the most important reminders lower in the prompt.
Yes, AI agents can be implemented using both no-code/low-code platforms and full code:
No-Code/Low-Code Examples (using N8N):
- Customer Support AI Agent: Implemented with a "routing" pattern. It receives email inquiries, classifies them (technical support, billing, general), and routes them to specialized workflows. It can respond directly or escalate to a human agent on Discord if needed.
- AI News Aggregator Agent: Uses a "parallelization" workflow. Scheduled daily, it gathers news from various sources (e.g., newsletters, Reddit), aggregates the information, and sends a summarized report with source citations via WhatsApp.
- Multi-Input Daily Expenses Tracker AI Agent: Interacts via WhatsApp, accepting text or picture receipts of expenses. It aggregates this information, stores it in Google Sheets, and sends a daily summary report via WhatsApp.
Code-Based Example (using OpenAI's agents SDK in Python):
- Financial Research Assistant: Follows a "prompt chaining" workflow. A main orchestrator (Financial Research Manager) kicks off the process. It uses a planner agent to break down queries, a search agent to perform searches, specialized analysis agents (financials, risk) to analyze results, a writer agent to synthesize reports, and a verifier agent for accuracy. It also includes voice interaction and translation functionalities.
To find useful AI agent ideas, especially for business or startups, consider these approaches:
- Start with Yourself: Identify tasks you currently perform that, if offloaded to an AI agent, would significantly ease your life. This can be a direct route to building a personally beneficial or business-viable solution.
- Go Undercover (for students/non-workers): If you lack daily work experience, shadow someone who owns a business or has a job. Observe their daily tasks to identify pain points or inefficiencies that could be automated by an AI agent. People often don't realize their own problems due to being deeply entrenched in their routines.
- Look for SAS Equivalents: For every Software as a Service (SAS) company, there's likely an AI agent equivalent. This provides a broad strategic guideline: identify successful SAS models and conceptualize their AI-driven, automated counterparts.
As of 2025, significant fundamental developments making areas ripe for disruption include:
- Voice and Audio Generation: There have been huge leaps forward in audio generation and voice cloning (e.g., 11 Labs, OpenAI's audio capabilities), enabling new use cases for voice-enabled AI agents and enhancing natural language interaction. OpenAI's SDK even dedicates a whole category to voice agents due to the vast possibilities.
- Image Models: Advancements in image generation and processing (e.g., Rev, Gemini Flash, GPT-4o image generation) present opportunities for AI agents that interact with or generate visual content.
- Video Models: The emergence of sophisticated video models like Sora indicates a burgeoning area for AI agents capable of understanding, generating, or manipulating video.
The AI industry is moving incredibly fast, and it's easy to feel overwhelmed by new tools and technologies emerging daily. The key advice is to:
- Focus on Foundations: Instead of chasing every new "revolutionary" tool, concentrate on understanding the fundamental components (models, tools, knowledge, memory, audio, guardrails, orchestration) and frameworks (agentic workflows) discussed. New innovations will typically fit within these established categories, acting as specialized improvements rather than entirely new paradigms.
- Prioritize Big Innovations: Pay attention to major fundamental developments, such as advancements in core AI models (e.g., Gemini 2.5 Pro) or protocols that enable better tool use (e.g., MCP).
- Keep Learning and Building: Continuously engage in personal projects and build your own AI agents. This hands-on experience, combined with a foundational understanding, will prepare you to capitalize on opportunities when your skills and interests align with market demand.
- Be Patient: Building successful AI agent businesses or projects takes time and persistence.
