Professional Development @ Boulder

Learning Multi-Agentic AI Systems

This Spring, I’ve reached the midpoint of my program in computer science at the University of Colorado Boulder (UCB) and am starting to branch out to more interesting courses outside the core curriculum. One of these is CSPB 3112 Professional Development in Computer Science where I can choose any CS topic and work on it during the semester for one credit.

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Therefore, I’m going all in on agentic AI systems. Here’s what I’m proposing to work on during the next 13 weeks.

Project Proposal

This project is about learning how to design, build and deploy multi-agentic AI systems.

1. Vision statement

   This project will add to my skills in AI system design and development following the different projects implemented during the CSPB program:

   • CSPB 1300: C++ Implementation of a Basic Recurrent Neural Network.

   • CSPB 4830: n8n AI automation workflow for construction costing.

   • CSPB 3308: AI Technical Interview Assistant.

   • Personal Project: AI Grader & Rich Feedback for continuous education programs.

2. Motivation

   My moonshot goal is to obtain a PhD in Computer Science and I’m studying different CS topics such as HCI, AI and Social Computing and their application in adult learning (Andragogy), upskilling, reskilling, self fulfillment and career change.

3. Specific and measurable goals (learning objectives) for the project Ability to understand, explain and implement the following:

   • Agent Design Concepts (Chain of Thought - CoT and Reasoning/Acting Framework - ReAct)

   • Multi Agent Design Patterns (Router, Parallel, Serial, Orchestrator)

   • Adding Guardrails to Agent output (Programmatic and LLM Judging)

   • Use of Short and Long Term Memory

   • Retrieval Augmented Generation (RAG) and vector databases (Pinecone, PgVector, etc…)

   • Integration with Tools (Model Context Protocol)

4. Risks to project completion, possibly including:

   1. Busy with life and work commitments.

5. Mitigation strategy for the risks listed above

   1. Take a structured approach to learning by following the Udacity Agentic AI Curriculum.

   2. Implement the 4 projects in the Udacity Agentic AI course and have them graded.

   3. Review the Github Actions course on MS certification page and Linkedin Learning.

   4. Blog about my learnings on a weekly basis.

   5. Choose a capstone project with publicly available data.

   6. Follow this loosely designed schedule:

   • [x] Week 01: Prepare project proposal & research topics

   • [x] Week 02: Udacity: Course 1 - Prompting For Effective Reasoning & Planning - Submit P1

   • [ ] Week 03: Udacity: Course 2 - Agentic Workflows - Submit P2

   • [ ] Week 04: Udacity: Course 3 - Building Agents

   • [ ] Week 05: Udacity: Course 3 - Building Agents

   • [ ] Week 06: Udacity: Course 3 - Submit P3

   • [ ] Week 07: Udacity Course 4 - Multi-Agent Systems

   • [ ] Week 08: Udacity Course 4 - Multi-Agent Systems

   • [ ] Week 09: Udacity Course 4 - Submit P4 & Obtain Certificate

   • [ ] Week 10: Github Actions - Review MS official course

   • [ ] Week 11: Github Actions - Review Linkedin Learning Course - Take Exam

   • [ ] Week 12: Capstone Project: Graduate Record Exam (GRE) AI Essay Tutor

   • [ ] Week 13: Capstone Project: Graduate Record Exam (GRE) AI Essay Tutor

6. Project assessments - provide a list of evaluation criteria for the project

   1. Obtained the Udacity Agentic AI Nanodegree.

   2. Deployed an Agent using a CI/CD pipeline.

   3. Bonus: Take the Github Actions Certifications Exam

7. Project portfolio link: https://starterpad.com

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Week 02 [Jan 26 - Feb 1, 2026]

• What did you do last week?
  I successfully completed Course #1 and passed the first project.

  

  One thing I really like about Udacity is that their projects are reviewed by humans to provide detailed feedback and code reviews (don’t know if they’re still using humans or have shifted to AI grading after the Accenture acquisition).

  

  I added the review above to summarize my takeaways from the lesson. Agentic AI is all about system design and less about actual machine learning. LLMs are treated as black boxes with prompts as inputs to the system and assistant responses as outputs. This distinction will become important in Course #2 as I work on workflow design.

• First step to optimize the system is to work on refining the input. In other words, how do we construct a good prompt?

  The course suggests to divide the prompt into 5 sections as follows:

  [Role]: The persona the LLM should adopt (e.g., “Act as a high school teacher.”).

  [Task]: The specific instruction or question (e.g., “find a solution to the following trigonometric identity.”).
  

  [Output Format]: How the response should be structured (e.g., “One sentence answer”).

  [Examples]: Sample input/output pairs

  (e.g., “Q: What is the value of

  \(sin^2(x) + cos^2(x)\)

  A: It is 1”).

  [Context]: Additional information needed for the task (e.g., current date, if asking for the date).

• Use Chain of Thought (CoT), which is a process to divide tasks into smaller sized sub tasks executing one after the other. This is accomplished by explicitly mentioning in the prompt to “Reason step by step”.
  
  ChatGPT explains this quite well:
  CoT is the agent’s internal deliberation / scratchpad that helps it:

  • break a goal into steps (“what sub-problems do I need to solve?”)

  • choose the next action (“which tool/API should I call?”)

  • keep state (“what have I learned so far?”)

  • handle errors (“that tool failed—what’s the fallback?”)

• Insure that we specify a schema for the output and validate the output. This can be done by using Pydantic models and asserting if the LLM output adheres to the specified schema. Here is an example from the Pydantic documentation:

  

  and an output field can be verified by asserting the following:

  

  the model_validate() class function can be used as well to validate the LLM output against the schema.

• Use ReAct (Reasoning /Acting framework)

ReAct is a pattern where the LLM can alternate between thinking and taking actions:

1. Think: a step where an LLM can decide what’s missing and what to do next (e.g. “I need to get the current date to find suitable events”)

2. Act: perform an action or call a tool (e.g. “use tool get_current_date with no arguments“ )

3. Observe: Read the result and use it to choose the next step (e.g. “Today is February 4, 2026, now I can filter events for today.”)

4. Final Answer: This loop repeats until the model has enough information, then it produces a Final Answer.

The purpose is to reduce hallucinations by checking real world information.

I applied these concepts to a travel AI agent that plans a trip to the wonderful city of AgentsVille! You can check the code here.

• What do you plan to do this week?
  This week, I’ll be continuing with Course 2 from the Udacity Nanodegree looking at Agentic workflow patterns such as prompt chaining, parallelization and routing and then I’ll review two important agentic workflows:

  1. Evaluator-Optimizer workflow

  2. Orchestrator-Worker workflow
     

• Are there any impediments in your way?
  Currently no problems in sight.
  

• Reflection on the process you used last week, how can you make the process work better?

  To be truthful, I didn’t stick to my slotted time and had to delay it from my scheduled Friday sessions to the next available times on Monday and Tuesday when I reviewed the course content and completed the project on one day and then worked on my blog post and providing updates on the second. I still have my next scheduled session on Friday and I hope that I can stick to it this time. I feel that spacing out my work between sessions helps me process and recall concepts better.

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Week 01 [Jan 20 - Jan 26, 2026]

I’ll be providing Agile style weekly standup updates and here’s the first one:

• What did you do last week?
  Last week was focused on narrowing my topic of interest and since I’m on a trajectory to develop my AI skills, I went for Agentic AI. It’s a trendy topic and I’d love to gain a better understanding.

  I’ve been experimenting with construction cost estimations on several projects using line item descriptions and a basic knowledgebase of two previously priced construction projects with chatGPT in Thinking mode (i.e. using agents) but with mixed results. Here’s a sample of a project that was recently estimated using AI agents vs a real cost estimator for mechanical works in a 5 stars hotel:

  

  I’m working on refining the AI system architecture for better use of short and long term memory as well as vector databases for Retrieval Augmented Generation (RAG). Therefore, I’ve identified the Udacity Agentic AI course as the source for my learning throughout the next 13 weeks.
  

• What do you plan to do this week?
  I’ve already started with the first module of the Udacity Agentic AI course and will be completing and submitting the first project this week. I’ll then be writing my first article summarizing my learnings from the week.
  

• Are there any impediments in your way?
  Currently no problems in sight.
  

• Reflection on the process you used last week, how can you make the process work better?

  I haven’t really tested the process last week but with the plan set in the proposal, I’ll be starting to time my commitment to the slotted weekly study time.