Building Your Own AI Copilot, Persona, or Agent
- Neena Sathi
- Jun 26
- 4 min read
Updated: 3 days ago
By
Neena Sathi, Principal, Applied AI Institute and
Phane Mane, AI-Practitioner | Blogger | eCommerce SME
Overview
AI isn't just a buzzword anymore; it's now a daily collaborator. For students, educators, and entrepreneurs alike, crafting your own AI Copilot or Agent has never been more attainable. But what does it truly take to build one?
Let’s delve into the tools, mindsets, and frameworks needed to bring an AI companion to life. Notably, the Applied AI Institute (AAII) is leading the charge in making this a reality.
1. Understanding AI Copilot vs. Persona vs. Agent
Copilot: This AI integrates into your workflow. Think of tools like Microsoft Word's Copilot, which aid in drafting and summarizing tasks.
Agent: More autonomous than a Copilot, an Agent can reason and take action. It is ideal for multi-step or multi-agent systems.
Persona: Not an independent assistant, but the layer that shapes behavior and tone in both Copilots and Agents. It defines how the assistant interacts, whether as a friendly tutor or a formal legal expert.
As Microsoft describes, agents function as the apps of the AI era, while Copilots act as the interfaces connecting you to them.
2. Essentials for Building Your AI Companion
If you're ready to start, here’s what you’ll typically need:
A Foundation Model: Popular choices include GPT-4, Claude, or Mistral.
An Orchestration Layer: Options like LangChain, Langflow, or Microsoft Copilot Studio can guide the process.
Knowledge Sources: These could be PDFs, websites, SharePoint, or vector databases such as Astra DB.
A Persona Definition: Establish the tone, role, and boundaries of your AI assistant.
Optional Integrations: Things like sending emails or querying databases can enhance functionality.
Construct agents either declaratively with Microsoft Copilot Studio or visually with tools like Langflow and Flowise.
3. Step-by-Step: Building Your Own AI Agent
Now that we've explained the concepts, let’s outline how to create your AI agent. Follow these steps from ideation to testing.
Step 1: Define the Purpose
What will your agent do? Here are some examples:
A grading assistant for teachers.
A resume reviewer for job seekers.
A policy writer for HR teams.
Step 2: Select Your Tools
Consider the following tools for your project:
Microsoft Copilot Studio: Ideal for building agents that integrate with SharePoint, Teams, and Power Automate.
Langflow: Allows for visual orchestration of multi-agent workflows.
Flowise: A low-code builder perfect for LLM apps.
Astra DB: A vector database tailored for RAG-based memory.
LlamaIndex / LlamaCloud: Connects LLMs to your specific content.
Google Gemini Gems: A platform for swiftly developing customized agents.
Google ADK: An open-source framework for agent lifecycle development.
Step 3: Add Knowledge
Apply RAG (Retrieval-Augmented Generation) methods to ground your agent in your content:
Upload PDFs and documents.
Connect to SharePoint, Google Drive, or any relevant websites.
Leverage vector databases like Astra DB.
Step 4: Define the Persona
Shape your assistant’s personality and behavior. The persona defines the tone, personality, and communication style. For example: “You are a friendly academic assistant who helps students brainstorm research ideas and cite sources.”
Step 5: Test and Refine
Utilize testing panes in tools like Langflow, Gemini, or Copilot Studio. Iterate prompts, adjust knowledge sources, and correct misunderstandings through feedback.
To see what is possible, check out some working agents built by AAII across education, finance, and public policy.
4. Real-World Inspiration: Agents by the Applied AI Institute (AAII)
The Applied AI Institute has successfully developed an impressive portfolio of working agents, showcasing their potential in both education and enterprise:
🧠 Gradebot
A GPT-powered grader that evaluates student assignments based on fairness audits across many universities. It’s built using Langflow, Astra DB, and a private LLM.
💼 Investment Advisor Agent
This agent helps users understand stock portfolios, using real-time data and personal risk preferences. It also emphasizes that the final decisions rest with the user.
🧪 Generative AI Sandbox
This is a testing ground for modular agents, including:
Resume Consultant
Health Equity Bot
EV Infrastructure Advisor
Legal Risk Analyzer
Spotify Music Recommender
Accessible and customizable, these agents can be tailored by students, educators, or business professionals.
📚 Explore more: AAII Agentic Solutions
5. Want to Build One Yourself? AAII’s Courses Can Help
The Applied AI Institute provides Credly-certified courses designed to teach you how to build agents from the ground up:
Course | Topics | Credential |
Prompt design, role patterns, tone shaping | Credly badge: Prompt Engineering | |
RAG, embeddings, classification, LangChain | Credly badge: Foundation | |
Orchestration, multi-agent design, sandbox capstone | Credly badge: Intermediate |
🎓 Delivered live over Zoom with LMS access, templates, and real instructor feedback. Scholarships available.
6. Assessing Your Agent’s Maturity with a Framework
After creating your first Copilot or Agent, it's crucial to determine its capabilities. To aid in this, AAII has developed a maturity model.
Not all agents are created equal. Some merely follow commands, while others can reason and adapt. Our maturity model has five levels:
Level 1: Reactive agents with basic functions
Level 2: Multi-agent systems with sequenced actions
Level 3: Agents that can integrate various tools
Level 4: Orchestrated agents capable of dynamic planning
Level 5: Goal-driven agents that can negotiate and manage constraints
This model can help developers diagnose their current capabilities and strategize for more autonomous systems. It’s particularly useful for transitioning from prototypes to full production.
📌 Tip: Use this maturity framework to benchmark your own Gemini Gem, Langflow pipeline, or Copilot Studio agent.
7. Additional Resources for Learning
To further develop your skills:
Microsoft: Copilot 101 - Core concepts and functions.
LangChain Documentation - Guidance on LLM orchestration and agents.
LlamaIndex - Connect LLMs to your own data effectively.
Google Gems Help - Customize AI agents through the Gemini platform.
McKinsey: AI Superagency Report - Insights on scaling human efforts with agents.
Final Thoughts: Your Agent Journey Awaits
You don't need to be a machine learning expert to construct your assistant. With the right tools and a clear vision, your first Copilot or Agent is nearer than you believe.
Whether you're engaged in teaching, coaching, analyzing, or simply exploring what’s possible, your first intelligent agent is only a few clicks away.
💡 What will your agent do?
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