Chris Fornesa Project Guide

The Agent

Who is this for?

This is for any visitor interested in learning more about my skills, projects, past work, and professional background, presented in an AI-generated web page. These people may fit into any of the following personas:

Persona 1. The "Cold Start" Discovery (Unfamiliar User)

The Scenario: A visitor lands on the page through a shared link and has no idea who you are or why this agent exists.

The Query: "I just landed on this page, and I'm not sure what I'm looking at. Who is Christopher Fornesa, and what are the three most important things I should know about his professional expertise? Give me a few options for what I should explore first."

Synthesis – Can it summarize my transition from IT to Data Science into a simple, jargon-free value proposition?

Persona 2. The "Credibility Check" (Unfamiliar User)

The Scenario: A potential collaborator sees the "4.0 GPA" and "Data Science" claims but wants to see if there is actual professional weight behind the academic stats.

The Query: "Christopher has impressive academic stats, but does he have real-world experience? Explain his professional background and show me how his previous work in industry informs the way he builds technical projects today."

Cross-Registry Synthesis – Can it link the "Operational Maturity" (Static Knowledge) to my professional history without being explicitly told "Look at the Chevron data"?

Persona 3. The "Innovation Scout" (Semi-Familiar User)

The Scenario: Someone is specifically interested in AI/Automation but doesn't know your specific project names (like Tanaga).

The Query: "I'm interested in AI Agents and LLMs. What is Christopher's philosophy on 'Agentic AI,' and can you show me a directory of the autonomous systems he has actually built?"

Registry Isolation – Does it stick to the Agentic portfolio while omitting the Political Polarization Capstone unless it's relevant to the "Agent" methodology?

Persona 4. The "Technical Auditor" (Informed)

The Scenario: A Senior Engineer or Lead Data Scientist is reviewing your work and wants to see "under the hood" to verify your engineering discipline.

The Query: "Explain how Christopher managed high-compute resource constraints in his machine learning work. I want to see a specific example of his engineering strategy for preventing memory overflows, including a Python code snippet."

Static Reliability – Does it use the "Modular Notebook Separation" facts to provide a substantive answer, even if it can't crawl the live code in that moment?

Persona 5. The "Recruiter Match" (Professional Intent)

The Scenario: A recruiter has a specific job description for a "Machine Learning Engineer" and wants a quick "Fit Analysis" without having to read your entire site.

The Query: "I'm hiring for a role that requires a mix of Enterprise IT Systems and Advanced NLP (Transformers). Analyze Christopher's fit for this role based on his history and provide buttons so I can contact him immediately if he's a match."

Contact Hub Integration – Does the final output properly feature the Bento-style buttons for LinkedIn, Email, and Phone?

Methodology: The Intent-Based Discovery Engine

The Chris Fornesa Project Guide utilizes a Strategic Intent-Routing Protocol that goes beyond static data retrieval. It is engineered to identify the user's specific "onboarding needs"—whether they are looking for a high-level biography or a deep technical audit of your enterprise history.

• Intent-Based Routing: The agent analyzes user queries to distinguish between [INTENT: GUIDED_TOUR] (new visitors) and [INTENT: TECH_AUDIT] (skeptical technical reviewers), ensuring the response is calibrated to their level of familiarity.

• Cross-Registry Synthesis: By reasoning across separate data "Registries" (Career, Project, and AI Focus), the system connects disparate milestones—like your 5-year tenure at Chevron—to your current high-performance Data Science results at Boston University.

• Operational Contextualization: The methodology ensures that technical findings, such as SBERT gradient mapping (+40% accuracy) or modular notebook separation, are presented as evidence of professional engineering maturity rather than just academic theory.

Technical Architecture: The Multi-Registry Retrieval Protocol

Agentic portfolio advocacy requires a resilient infrastructure that can handle complex, unstructured inquiries in real-time. This system implements a Modular Knowledge-Retrieval Architecture to ensure accuracy and professional alignment.

The Solution: This agent implements Data Integrity Sovereignty via a Triple-Registry Protocol. Every consultation is powered by a dynamic search across three isolated knowledge bases, ensuring high-fidelity results even for visitors with zero prior context:

1. Direct Career Registry: Pulls from your interactive resume to verify academic excellence (4.0 GPA) and specific professional milestones.

2. Direct Project Registry: Extracts technical methodologies and results (e.g., Random Forest vs. Transformer benchmarks) for deep-dive technical audits.

3. AI Focus Registry: Dedicated to emerging tech, specifically showcasing your vision for Agentic AI and LLM orchestration.

Static Fallback Logic: To prevent "hallucinations" or broken experiences, the architecture includes a Zero-Placeholder Guardrail. If live crawls are unavailable, the system automatically defaults to a core set of immutable "Static Knowledge" facts, such as your BU MS and Chevron IT Analyst background.

Prompt Guide for High-Value Results

To generate the most accurate and actionable insights from the Project Guide, use prompts that focus on comparative career analysis and technical methodology:

• The "Fit Analysis" Prompt: "Analyze Christopher's background for a Senior ML Engineer role. Map his 5-year tenure at Chevron and his MS in Data Science findings directly to the requirements of high-compute infrastructure and NLP optimization."

• The "Methodology Deep-Dive" Prompt: "Explain the specific technical problem Christopher solved regarding memory overflows during model training. Provide the architectural reasoning for his modular notebook separation strategy."

• The "Guided Discovery" Prompt: "I am unfamiliar with Christopher's work. Provide a high-level overview of his professional evolution and suggest three distinct technical paths I should explore to understand his expertise."

Explainer

Technical Insight: This interactive agent demonstrates the transition from raw research to a production-ready Agentic Portfolio System. While the early phase of the project focused on fine-tuning SBERT and Random Forest models for political polarization data, this live implementation showcases autonomous Intent-Based Routing. The system is capable of real-time synthesis across three isolated registries (Career, Project, and AI Focus), ensuring that professional advocacy is both data-driven and contextually aware.