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Building AI Agents In 44 Minutes

BY 4xvky
July 31, 2025•
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2890 views

Blog Article: Comprehensive Guide to Building AI Agents

Objective

This document is a comprehensive guide intended for individuals who want to learn about building AI agents, ranging from beginners with no coding skills to seasoned software engineers seeking to develop advanced AI-powered systems or startups.

Table of Contents

  1. Introduction
  2. Defining AI Agents
  3. Core Components of AI Agents
  4. Common Agentic Workflows
  5. Prompt Engineering Crash Course for AI Agents
  6. Implementation Examples: No Code, Low Code & Fully Coded
  7. Choosing What to Build: Finding Your AI Agent Idea
  8. Tech-Enabled Innovations & Next Steps
  9. Final Advice

Introduction

  • Author has extensive experience building AI agents, runs a program called Lonely Octopus teaching AI skills, and helps people build AI agents for companies.
  • Guide covers frameworks, tools, agentic workflows, prompt engineering, and implementation for both non-coders and software engineers.
  • Includes real-world examples (no code and code) and interactive assessments throughout.

Defining AI Agents

  • AI Agent: A system that perceives its environment, processes information, and autonomously takes actions to achieve specific goals.

    • Often modeled after human roles or repetitive tasks.
    • Examples: Coding AI agents (e.g., Cursor, Windsurf), customer service chatbots.

  • Multi-Agent Systems:

    • AI agents are often composed of multiple sub-agents, each specializing in a specific task.
    • Example: Customer service agent broken into sub-agents for routing, billing, IT, and sales.

  • Why Multiple Agents?

    • Just as companies allocate roles to different people for efficiency, agents specialized in particular tasks perform better collectively than a single generalist agent.

Core Components of AI Agents

Analogy: Like building a burger (bun, patty, condiments), building an AI agent requires assembling modular components.

OpenAI's Agent Components:

  1. Models

    • Large language models (LLMs) that do reasoning, decision-making, multi-modal processing.
    • Examples: GPT-4.0, GPT-4.5, GPT-3.5, Claude Sonnet 3.7, Gemini 2.5 Pro, open-source models.
    • Selection depends on use case, speed, cost, context window, and required modalities.

  2. Tools

    • Enable agents to interact with external systems.
    • Examples: Web search, email, file access, application integration (Slack, Discord, Google Docs), custom tools.
    • Developers can define tools via SDKs (e.g., OpenAI Agents SDK, Anthropic MCP).
    • No/Low code solutions: Drag-and-drop tool configuration (e.g., n8n).

  3. Knowledge & Memory

    • Static Knowledge: Facts, documents, policies (e.g., for legal AI).
    • Persistent Memory: Tracks interaction and context over time (vital for chatbots and assistants).
    • Implementation: Vector stores (Pinecone, Weviate), RAG, embedded databases.
    • No code platforms often abstract this away.

  4. Audio & Speech

    • Speech synthesis (text-to-speech) & transcription (speech-to-text).
    • Tools: OpenAI's built-in models, Whisper for transcription, Eleven Labs for voice cloning.

  5. Guardrails

    • Constraints to prevent harmful, irrelevant, or off-topic output.
    • Tools: OpenAI's built-in, Guardrails AI, LangChain guardrails.
    • No/low code solutions typically include basic guardrails.

  6. Orchestration

    • Management and coordination of workflows among multiple sub-agents.
    • Handles deployment, monitoring, and ongoing improvements.
    • Tools: Crew AI, LangChain, LlamaIndex.

Common Agentic Workflows

Guided by Anthropic's "Building Effective Agents" and common industry patterns.

1. Prompt Chaining

  • Description: Task is broken into sequential “steps”; output of one sub-agent is the input to the next (assembly line style).
  • Use Case: Generating structured reports (input → outline → content → editing).

2. Routing

  • Description: An input is routed to the most relevant specialized sub-agent or model for processing.
  • Use Case: Customer service chatbot routes billing, tech support, general inquiries accordingly.

3. Parallelization

  • Description: Multiple sub-agents execute subtasks simultaneously; results are aggregated.
    • Sectioning: Each works on different parts of the problem.
    • Voting: Multiple agents perform the same task for consensus.
  • Use Case: News aggregator collecting from multiple sources, code review with multiple evaluators.

4. Orchestrator/Workers

  • Description: Dynamic assignment and coordination of subtasks, steps are not predefined.
  • Use Case: Coding agents making unpredictable code changes; research aggregators.

5. Evaluator/Optimizer

  • Description: Includes iterative cycles for evaluation and refinement until a solution meets quality standards.
  • Use Case: AI translation refined until nuance is captured; iterative search and reporting.

6. Truly Autonomous Agents

  • Description: Agent independently plans, acts, and evaluates progress based on environmental feedback.
  • Use Case: Open-ended tasks like software engineering, complex computer use.

Advice:

  • Always choose the simplest agentic workflow that meets your needs to avoid overengineering.

Prompt Engineering Crash Course for AI Agents

Key Principle: The quality of prompts can make or break an AI agent’s effectiveness.

Six Components of Agent Prompts:

  1. Role: Define the agent’s persona, style, and context (“You are an AI research assistant…”).
  2. Task: Explicitly state the goals (“Given a search term, produce a summary…”).
  3. Input: Specify expected inputs.
  4. Output: Describe the required format and content.
  5. Constraints: Boundaries on what to avoid or emphasize (no opinion, only current data, etc.).
  6. Capabilities & Reminders: List available tools and critical reminders (e.g., “Use web search. Only summarize news from the past 7 days.”).

Tip: Place the most important instructions last, as models weight recent context more heavily.

Implementation Examples: No Code, Low Code & Fully Coded

1. Customer Support AI Agent (No-Code/Low-Code: n8n)

  • Workflow: Routing
  • How it works:
    • Receives email inquiries, classifies them (billing, tech, general), routes to relevant workflow, and automates responses or escalates to humans as needed.

2. AI News Aggregator (No-Code/Low-Code: n8n)

  • Workflow: Parallelization (aggregates from newsletters and Reddit, then summarizes and sends via WhatsApp).
  • Limitation: Runs steps sequentially due to platform constraints, but conceptually parallel.

3. Multi-Input Daily Expenses Tracker AI Agent (No-Code/Low-Code: n8n)

  • Features: Accepts pictures/text of expenses, processes receipts, aggregates in Google Sheets, sends daily summary on WhatsApp.

4. Financial Research Assistant (Fully-Coded: OpenAI SDK, Python)

  • Workflow: Prompt Chaining and Routing
  • Steps:
    • Orchestrator agent receives query.
    • Planner agent breaks down query into searchable terms.
    • Search agents gather web results.
    • Specialized analysis agents (financial, risk) synthesize data.
    • Writer agent composes report; verifier checks for quality.
    • Supports voice interaction and translation (via tool integrations).

Choosing What to Build: Finding Your AI Agent Idea

  1. Start With Yourself: Identify repetitive or challenging tasks you could delegate to an AI agent.
  2. Shadow Others: If you lack an immediate use case, observe or interview professionals to discover pain points ripe for automation.
  3. Look for Patterns: Problems you solve for yourself are often shared by many (potential for startups/businesses).
  4. Vertical SaaS Analogy: For every SaaS tool, there’s likely to be an AI-agent equivalent to disrupt that market.

Tech-Enabled Innovations & Next Steps

Current & Emerging Frontiers:

  • Voice & Audio: Major advancements (e.g., realistic AI-generated voices, voice agents).
  • Image & Video: Breakthroughs in image generation (Gemini Flash, GPT-40), video models (OpenAI Sora).
  • Enablers: MCP for tool integration, improved context windows, more accessible SDKs.

Advice:

  • Focus on learning foundational frameworks and categories, as the specific tools and rankings evolve constantly.
  • Choose implementation approaches and tools that match your technical comfort level and problem requirements.

Final Advice

  • Stay patient and don’t be overwhelmed by rapid changes—return to core frameworks and components to navigate hype.
  • Prioritize real user problems over building features for novelty’s sake.
  • Keep learning, experimenting with projects, and watch for opportunities where your skills align with world needs.
  • There are plentiful resources and communities (e.g., Y Combinator, Lonely Octopus bootcamp) to support your AI agent journey.
  • Remember, every new generation of models/tools still fits into the fundamental agentic architecture—focus on the unchanging principles.

Useful Resources

  • HubSpot Practical Guide to AI Agents: For business-centric insights, ROI thinking, and deployment checklists.
  • Building Effective Agents (Anthropic): For technical workflows and architecture patterns.
  • Lonely Octopus: Hands-on learning for building real AI agents.
  • Y Combinator YouTube: AI entrepreneurship and ideation advice.

Endnote

  • Interactive assessments sprinkled throughout the guide to help reinforce learning.
  • Stay tuned for more hands-on examples, coded implementations, and community updates.
  • Keep building, be patient, and embrace the evolving landscape of AI agents!
    Building AI Agents In 44 Minutes