AI Agent Builder

An AI agent is a type of artificial intelligence system that can autonomously perform tasks or achieve specific goals within an environment.

  Project Duration

           Jan-Feb 2025

Project Overview

The Problem

AI agents learn from data. If the training data reflects existing biases (e.g., racial, gender, socioeconomic), the agent will likely perpetuate those biases in its decisions and actions, which can lead to unfair or discriminatory outcomes. AI agents can be vulnerable to manipulation, where malicious inputs can cause them to malfunction or produce unexpected results.

Project Goals

  • Develop methods to identify and mitigate biases in AI systems, ensuring fair and equitable outcomes for all users.
  • Develop AI agents that are more resilient to adversarial attacks and can operate reliably in complex and unpredictable environments.
  • Build trust in AI systems by demonstrating their reliability, fairness, and transparency.

Design Process

 

Understanding the needs, motivations, and challenges of the users and the environment where the agent will operate.

Clearly define the AI agent’s purpose, scope, and desired outcomes.

Explore different AI architectures, algorithms, and approaches.

Develop initial versions of the AI agent, test their functionality, and gather feedback.

Evaluate the AI agent’s performance, gather user feedback, and iterate on the design based on the results.

User Research

I conducted research to understand the needs and expectations of users who interact with AI agents. I employed interviews, surveys, and observations to uncover insights on user behaviors, preferences, and pain points.

Crucial points from the research:

  • AI agents are designed to act independently, make decisions, and perform tasks on behalf of users.
  • These agents can automate repetitive tasks, analyze data for decision-making, and learn from experience to improve performance.

View full research report here

Surveys

Conducted user surveys to gather feedback on AI agent prototypes, identifying key areas for improvement in usability and interaction flow. Analyzed survey data to inform iterative design changes, resulting in a more intuitive and user-friendly agent experience

View full survey results here

I distributed a 10-question survey to 15 users and learned about shortcomings in AI agents designed to understand user requests. Specifically, they struggle with:

  • Contextual Understanding
  • Incomplete Actions
  • Misaligned Decisions
  • Lack of Transparency
  • Limited Options

User Interviews

How many people? I interviewed 3 users.

Who were they? 

  • Tech-Savvy Enthusiast: Individuals who are excited about new technology and want to push the boundaries of what AI can do.
  • Cautious Skeptic: Individuals who are hesitant to adopt new technology and need to be convinced of its value.
  • Healthcare Professionals: Individuals who require AI tools to be reliable, efficient, and safe for use in healthcare settings.

Best Results:

  1. To resolve incomplete actions and misaligned decisions, we propose a modular action planning system. The AI agent should break down complex tasks into smaller, manageable steps. Each step should have a verification mechanism to ensure it’s executed correctly. The system should also incorporate a feedback loop, allowing users to provide real-time corrections and guide the AI towards the desired outcome. This also enables the AI to learn from its mistakes and improve its decision-making over time.
  2. AI agents should maintain detailed task histories, including timestamps, inputs, outputs, and reasoning. Users should be able to easily access and review this history, with clear explanations of the agent’s actions.

User Needs/Goals:

  • Transparency: Users want to understand how the AI agent is performing tasks and making decisions.
  • Accountability: Users need to be able to track and audit the agent’s actions, especially for critical tasks.
  • Learning/Improvement: Users want to be able to identify patterns and areas for improvement in the agent’s behavior.

3. AI agents should proactively suggest tasks based on user context, past behavior, and external information. Before “deploying,” the agent should provide a clear preview of the intended actions, allowing users to confirm or modify them.

User Needs/Goals:

  • Anticipation: Users want the AI agent to anticipate their needs and provide relevant assistance.
  • Error Prevention: Users want to avoid unintended consequences by reviewing and confirming actions before execution.
  • Personalization: Users want the AI agent to adapt to their individual preferences and work patterns.

User Personas

Hebrew, the tech enthusiast, eagerly explores new AI features, seeing them as tools for innovation and efficiency.

Reigns, the cautious skeptic, prioritizes reliability and transparency, needing clear explanations before trusting AI actions.

Competitive Analysis

I looked at the competitors to identify best practices & areas of improvement

Customer Journey Mapping

Information Architecture/User flow

Wireframes

Illustrating Automation: Images of agents can demonstrate how tasks are automated and workflows are streamlined, showcasing the efficiency gains achieved.

Showing System Integration: They can illustrate how different systems or tools are connected and integrated, emphasizing the seamlessness of the solution.

Providing a Human Touch: In some cases, images of agents might also refer to human representatives or support staff, highlighting the human element of the service.

Illustrating Knowledge Sharing: Knowledge pages are crucial for capturing and sharing information. Images of knowledge pages in a case study can demonstrate how knowledge is disseminated and accessed within an organization.

Showcasing Resources: They can highlight the availability of helpful resources, such as documentation, tutorials, or FAQs, emphasizing the support provided.

Building Trust: By showcasing the depth and quality of the knowledge base, images of knowledge pages can build trust and credibility in the solution.

Visual Representation of Data: Dashboards are designed to present complex data in a clear and concise way. 

Demonstrating User Interface: They showcase the user-friendliness and intuitiveness of the software or platform, making it easier for potential customers to envision themselves using it.

Highlighting Key Features: Dashboards often highlight the most important features and functionalities of a product, drawing attention to its strengths.

Insights/Crafting AI Agent Builder

Onboarding

Offering "Sign in with Google". drastically reduces the effort required for new users to create an account.

Faster Onboarding

Users can quickly access your platform with just a few clicks, speeding up the onboarding process and allowing them to start using your product or service sooner.

Consistent User Experience

Social login provides a consistent user experience across different platforms and devices, as users are already familiar with the sign-in process.

Agent Management

This centralized platform empowers users to efficiently develop, deploy, and discover relevant AI agents, accelerating the adoption and impact of AI solutions.

Add Agent

simplifies AI agent creation by capturing essential details like name, description, type, use case, and activation status in a structured form. This streamlined process ensures consistency, facilitates organization, and empowers users to quickly define and prepare AI agents for deployment.

Deploy Agent

streamlines the process of making AI agents operational by consolidating key information like agent name, type, action, and use case, along with advanced configuration options. This centralized approach simplifies deployment, ensures consistency, and empowers users to tailor agent behavior to specific needs, accelerating the integration of AI into practical applications.

UI Style Guide

Usability Testing

Objective: To evaluate the usability and effectiveness of the AI agent designed for automating repetitive tasks across diverse sectors, focusing on its ability to understand complex instructions, adapt to changing conditions, and autonomously execute tasks.

Target Users: Representatives from target sectors (automation, retail, technical support, healthcare, finance, content creation) with varying levels of technical expertise. Recruit users who would realistically interact with such an agent in their daily work.

Tasks: Users will be asked to perform a series of realistic tasks  designed to test different aspects of the AI agent’s functionality.

Conclusion

This AI agent represents a significant advancement in automation, poised to revolutionize workflows across diverse sectors. By leveraging the power of NLP and ML, it transcends the limitations of traditional rule-based automation, offering a flexible and intelligent solution capable of understanding complex instructions, adapting to changing conditions, and autonomously executing repetitive tasks ultimately driving efficiency and productivity gains.