How to Build an AI Agent - Part 1: Vision and Planning

This article introduces the first part of GMI Cloud’s AI Agent series, focusing on the vision and planning phase of building a functional AI agent from scratch. It explores how to define goals, evaluate project feasibility, and plan the core components of an MVP using open-source tools and real-world examples.

What you’ll learn:

• What an AI Agent is and its key characteristics (perception, decision-making, action, and adaptability)
• How to define a clear vision and scope before development
• Comparing two project ideas: a Convention Research Assistant vs. an Outfit Seeker
• Identifying data sources, models, and infrastructure needs
• Anticipating technical challenges and planning for scalability

AI Agent Planning at a Glance:

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AI Agents are useful applications of AI and machine learning, but how are they made? This is a multi-part blog series going through the full steps of building an AI agent. 

But first, what's an AI Agent? An AI agent is a software entity that perceives its environment, processes information, plans, makes decisions, and takes actions to achieve specific goals. AI agents can range from simple rule-based software and fixed workflows to fully autonomous systems.

Key Characteristics of an AI Agent:

1. Perception – It gathers data from its environment using sensors, APIs, knowledge base, or input data streams.
2. Processing & Decision-Making – It applies logic, rules, or AI models to analyze the input and determine an appropriate response.
3. Memory – It could store and manage interaction records with users and help future decisions.
4. Action – It performs actions based on its decisions, which may include generating responses, automating tasks, or interacting with other systems.
5. Autonomy – Workflow agents automate complex/repetitive tasks for enhancing productivity. Autonomous agent operates complex tasks without constant human intervention.
6. Adaptability – Some AI agents can learn from interactions and improve over time.

Our documented steps will result in an MVP AI agent that anyone can follow. This is Part 1: Vision and Planning, where we ideate between two AI agent ideas for fun and explore what is necessary for creating a minimal viable product (MVP) before settling on which one to build.

Exploring Two AI Agent Ideas

Success comes from three key factors: a planned vision, achievable means, and efficient use of resources. — GMI's motto

We start by exploring the vision for two ideas: 

• Convention Research Assistant: an AI agent for discovering industry events and calculating the associated costs and expected returns for attending
• Outfit Seeker: an AI agent for viewing a photo or image, understanding the style and clothing, and then scraping online clothing options to generate options for buying the identified style

What’s the purpose?

Ideation helps narrow down feasible projects, balancing ambition with practicality before investing in development.

Both of these are projects with tangible use-cases, detailed below:

Convention Research Assistant

Keeping up with industry conventions and conferences can be overwhelming. This AI agent aims to streamline the process by:

• Finding relevant industry conventions through web scraping and data aggregation.
• Estimating costs, including travel, tickets, accommodations, and other expenses.
• Calculating the expected return on investment (ROI) based on factors such as audience, networking opportunities, and speaker lineups.
• Generating concise summaries to help users make informed decisions quickly.

Why This is a Feasible MVP

• Convention schedules and details are often available online in structured formats.
• Web scraping and simple data processing can be implemented relatively quickly.
• Cost estimation models are straightforward, relying on public travel and ticket pricing data.
• ROI estimation can start with basic heuristics and be refined over time. We actually consider this to be the hardest part of the AI agent's job as we'll need probably need to teach the agent how to score expected values of each convention.

Outfit Seeker

This AI agent would take an image of a person’s outfit—whether from a photo or a drawing—and attempt to find purchasable clothing items that match the look. The main functionalities include:

• Image recognition and classification to break down clothing items by type, color, and style.
• Searching online for similar clothing items using computer vision and web crawling.
• Handling challenges such as variations in lighting, angles, and availability of matching products.

GPU Acceleration for Building AI Agents

Even minimal viable AI agents rely on GPU power to process data efficiently.

From training computer vision models to running inference for NLP tasks, GPUs provide the parallel computing that makes real-time AI possible.

Using cloud GPU instances allows developers to train models faster, test multiple prototypes simultaneously, and scale workloads dynamically without purchasing physical hardware.

For rapid prototyping and iteration, leveraging GPU acceleration — especially through cloud providers — shortens the path from idea to working AI agent.

Why This is Significantly Harder to Build

As fun as this would be to build, it's a great example of a simple idea with technical complications.

• Image recognition of fashion items requires a sophisticated deep learning model trained on extensive datasets.
• Finding exact or highly similar matches across different online stores is a complex problem involving multiple APIs and custom search engines.
• Clothing items often go out of stock or vary in price and availability, making real-time accuracy a challenge.

You want to avoid overcomplicating an AI agent, so we chose to not build this one. It's still something we'd like to probably create somewhere down the line as a fun project.

Vision AI Agents in Practice

Computer vision–based AI agents can interpret the physical world just like language models process text.

They take in visual data — from cameras, images, or sensors — and make meaningful decisions based on what they “see.”

For instance, vision AI agents can recognize objects, detect defects, classify products, or even analyze human movement.

Beyond the examples in this article, similar systems already power warehouse automation, where robots identify and sort items; and medical imaging, where models detect anomalies faster than humans could.

By combining perception and reasoning, vision AI agents connect digital intelligence with real-world awareness — a key step toward fully autonomous systems.

Defining the AI Agent

Once the idea is selected, it’s essential to clearly define:

• The specific problem the agent solves.
• The target users and their needs.
• Success metrics to gauge effectiveness.

This clarity ensures a focused development process with measurable goals. In our case, we can easily define these:

• Problem it solves: Automating the manual process of evaluating industry-specific events for ROI.
• Users and needs: We'll be the ones using this! In this case, our needs are for our teammates to be given a pre-researched list of conventions and industry events with the AI agent's summary of expected value weighed against associated costs. The human makes the decision at the end, and the AI agent's job is to provide conden
• Success metrics: Did it accelerate their workflows and help make them more productive? There's also the failure state where the AI agent provides wrong/incorrect/inaccurate information where it creates more work. Maybe we can compare their previous workflow against an AI-assisted workflow.

All of the above is our Vision. Now it's time to plan on how we'll execute with a Plan.

What’s the goal?

Clarity here ensures that the agent’s purpose, users, and success metrics are aligned — preventing feature drift later.

Specialized AI Agents: Parts Request Example

AI agents aren’t limited to research or analysis — they can also automate operational workflows.
Consider a Parts Request AI Agent in a manufacturing or supply chain context:

• It could analyze maintenance logs to identify which components need replacement.
  
  
• Check inventory databases or vendor APIs for availability and pricing.
  
  
• Automatically generate a purchase request once parts fall below stock thresholds.
  Defining requirements for this kind of agent differs from a research assistant — the focus shifts from external data gathering to internal database integration, vendor communication, and transactional reliability.

This example shows how defining purpose and requirements tailors an AI agent’s design to its environment and business function.

Identifying Requirements

Building a functional AI agent requires:

• Data sources (e.g., industry event listings, clothing retailer APIs, image databases).
• Core AI models and techniques (e.g., NLP for summarization, computer vision for outfit recognition).
• Infrastructure to collect, process, and present information to users.

To make this easier on us, we'll use a few open-source tools:

• Dify.ai – This is a low-code platform for building generative AI applications.
• DeepSeek-R1 – This is a lightweight and open-source LLM model. This might be overkill for the task at hand but hey, we want to play with the cool new toy. Also, GMI Cloud is hosting DeepSeek-R1 now, so we're dogfooding our own thing!

Why this step matters?

Because selecting the right data sources, tools, and models determines the feasibility and scalability of the project.

Expected Challenges & Complications

Every AI project comes with its own hurdles. Some key challenges for our project include:

• Data availability: Some information might be behind paywalls or require advanced scraping techniques.
• Technical feasibility: The convention assistant is relatively simple, but the outfit search assistant involves complex AI and search algorithms. 
• Accuracy and performance: Ensuring accurate event ROI estimates or precise outfit matches requires significant refinement over time.
• Limitations of existing AI models: Pre-trained models may need fine-tuning or additional data to be effective for our use cases.
• Solid infrastructure: We'll probably do basic refining for this project, but good infrastructure is key for continuously refining the Agent.

By anticipating these challenges, AI agent builders can make informed decisions on feasibility and development strategies.

Part 2: Building an MVP – The Process

Stay tuned for part 2, where we'll document the steps we use to build an MVP of our Events Research AI Assistant!

Future Directions for AI Agent Development

Where are AI agents heading next?

As the field evolves, we’ll see AI agents grow from single-task systems into collaborative multi-agent ecosystems.

They’ll combine vision, language, and action capabilities to operate across domains — reading documents, interpreting visuals, and taking action autonomously.

Scaling from MVPs to production-ready systems will also depend on better infrastructure, monitoring, and orchestration, ensuring that agents can learn and adapt continuously.

The convergence of vision, reasoning, and real-time action marks the next frontier for intelligent agents and it’s closer than we think.

Frequently Asked Questions about How to Build an AI Agent – Part 1: Vision and Planning

1. What does the article mean by an “AI agent,” and what are its key characteristics?

An AI agent is a software entity that perceives its environment, processes inputs, plans, decides, and takes actions toward specific goals. Core traits include perception, processing & decision-making, memory, action, autonomy (from workflow agents to fully autonomous systems), and adaptability through learning over time.

2. Which AI agent ideas were explored, and why was one chosen over the other for an MVP?

Two concepts: Convention Research Assistant and Outfit Seeker. The MVP choice is the Convention Research Assistant because event data is accessible, cost estimation is straightforward, and ROI can start with simple heuristics. Outfit Seeker is deferred it needs sophisticated computer vision, multi-store matching, and constant availability checks, making it significantly harder.

3. What problem does the Convention Research Assistant solve and for whom?

It automates evaluating industry events for ROI: finds conventions, estimates costs (travel, tickets, accommodations), scores expected value (audience, networking, speakers), and produces concise summaries. Target users are teams who need a pre-researched, ranked list to speed up decisions while a human makes the final call.

4. How does the article define success metrics for the MVP AI agent?

Success = faster workflows and higher productivity versus the prior process. A failure state is when the agent’s inaccuracies create extra work. Comparing the team’s previous workflow against the AI-assisted workflow provides a concrete benchmark.

5. What requirements and tools are proposed to build the MVP?

Requirements: data sources (industry event listings), core models/techniques (NLP for summarization computer vision is noted for the harder idea), and infrastructure to collect, process, and present results. Tools: Dify.ai (low-code generative AI builder) and DeepSeek-R1 (lightweight open-source LLM, hosted on GMI Cloud for dogfooding).

6. What challenges should teams expect when planning the agent?

The article flags data availability (paywalls, advanced scraping), technical feasibility (the outfit idea is complex), accuracy and performance (ROI scoring or matching needs refinement), limits of pre-trained models (may need fine-tuning), and the need for solid infrastructure to continuously refine and operate the agent.