Work History

Jonathan Franks

Senior Consultant – Agentic AI, AI Engineering & Enterprise Automation

United States

Senior Consultant – Agentic AI, AI Engineering & Enterprise Automation

Professional Summary

Senior Consultant specializing in RPA and Agentic AI, operating at the intersection of architecture, delivery, and leadership. I design and ship automations that combine deterministic RPA with LLMs, agents, and retrieval to solve problems that were previously too complex to automate.

Over the last several years, I’ve acted as a solutions architect, pre-sales engineer, people manager, and hands-on developer across insurance, healthcare, credit unions, and financial services. I’m often the one who takes new technology (“could an agent do this?”) and turns it into a working system—then helps package that into a pattern the wider team can reuse.

Under the hood, I still think like an engineer with a mathematics mindset: my early background in biomedical engineering and math courses trained me to treat workflows as systems with signals, inputs, states, functions and outputs. Today, that shows up in how I approach reliability, evaluation, and long-term maintainability for AI-driven automation.

Core Strengths

  • Systems-level automation design – Designs end-to-end workflows that integrate RPA, APIs, human-in-the-loop, and LLM/agent components into coherent systems rather than one-off bots.

  • Agentic AI applied to real work – Comfortable orchestrating tools, search, and knowledge over LLMs to deliver tangible business value (not just chat experiences).

  • Enterprise RPA depth – Years of hands-on work with UiPath, Blue Prism, and Automation Anywhere in complex environments (VDIs, thin clients, on-prem orchestrators).

  • Servant leadership & mentoring – Grows developers into consultants, creates clarity, and removes friction in distributed teams.

  • Calm in ambiguity – Comfortable with messy real-world constraints; able to stabilize programs, triage fragile automations, and introduce structure without over-engineering.

  • Long-term thinking – Designs solutions so today’s PoCs can evolve into durable systems that are observable, governable, and maintainable.

Focus Areas

  • Agentic AI & LLM systems

    • Multi-agent workflows, tool calling, orchestration frameworks.

    • Use of web search, RAG, and structured tools to drive decisions.

    • Evaluation and guardrails for reliability and failure modes.

  • RPA & workflow automation

    • UiPath (REFramework, Orchestrator, queues, attended/unattended bots).

    • Blue Prism & Automation Anywhere for legacy estates.

    • Pattern libraries and standards for automation teams.

  • Infrastructure & delivery

    • On-prem Orchestrator, upgrades, and modern folders.

    • Working with VMs/VDIs, thin-client constraints, and security requirements.

    • CI/CD-ish approaches to bots and AI workflows.

  • Program health & governance

    • RPA/AI program health checks and risk/maturity assessments.

    • Pipeline design, intake, and prioritization with business stakeholders.

Professional Experience

Senior Consultant – RPA & Agentic AI
Consulting Firm — 2024–Present

  • Serve as RPA Solutions Architect and Agentic AI specialist across multiple clients.

  • Design and build agentic AI proofs of concept and demos, including some of the company’s first paid agentic AI engagements.

  • Act as pre-sales engineer: shape solution designs, estimates, and scopes that are realistic for both delivery and value.

  • Mentor automation developers into more client-facing, consulting-oriented roles.

  • Support and design UiPath infrastructure, including on-prem Orchestrator upgrades and modern folder migrations.

Software Engineer / Data Analyst – RPA
Fortune 500 Health Insurance — 2021–2024

  • Build and maintain attended and unattended UiPath automations across multiple business units.

  • Work with UiPath Orchestrator, VDIs, thin-client environments, and upgrades in a large enterprise context.

  • Develop internal best practices and training for modular, maintainable bots.

  • Lead thin-client RPA efforts to navigate technical constraints while preserving reliability.

RPA Engineer & Senior Consultant
Consulting Firm — 2017–2021

  • Led RPA solution design and delivery for global clients using Blue Prism, Automation Anywhere, and UiPath.

  • Ran process assessments to identify high-value automation candidates and shape client roadmaps.

  • Conducted program health checks and advised on governance, COE design, and standards.

  • Trained and mentored RPA developers, helping teams converge on common patterns and practices.

Quality Engineer – Medical Devices
Medical Device Manufacturing — 2015–2017

  • Supported quality systems for medical devices, including complaint handling, root cause analysis, and validation.

  • Used data from field performance and returns to drive corrective and preventive actions.

  • Led improvement projects aimed at reducing variation and defects in manufacturing and post-market performance.

Automation Specialist – Industrial Controls
Industrial Automation — 2015–2015

  • Deployed PLC programs, HMI screens, and industrial networks at client sites.

  • Developed HMI screens (e.g., FactoryTalk View) and tested PLC programs on CompactLogix platforms.

  • Implemented EtherNet/IP networks in industrial environments.

Selected Agentic AI & Automation Projects

Agentic Decisioning & Data-Quality Prototypes
UiPath + LLMs + Web Search / RAG

  • Led design and development of multi-step, agent-like workflows that use external data, web content, and LLM reasoning to classify and validate records.
  • Demonstrated that agentic patterns can reliably reduce manual review effort and improve consistency in complex decision flows.

Automated Research & Reporting Demo

  • Designed an agentic system where specialized components research key topics, generate visualizations, and assemble a structured HTML report.
  • Showcased how agentic orchestration can deliver end-to-end, fully automated research, analysis, and reporting.

Automation Platform Modernization

  • Served as lead architect/engineer for a major on-prem automation platform upgrade, including migration to modern capabilities and structures.
  • Reduced operational friction, improved governance, and created a more scalable foundation for future automation growth.

RPA Program Health & Pipeline Design

  • Acted as lead consultant reviewing existing automation estates, identifying fragility and risk, and recommending governance and design improvements.
  • Defined intake and pipeline models that better align business demand with realistic delivery and support capacity.

Early Engineering & Technical Foundations

While my current focus is AI and automation, I started my career closer to physical systems and modeling:

  • Biomedical engineering:

    • Worked on medical-device quality and on-the-ground equipment repair (including work in low-resource hospital environments).

    • Learned to design for reliability and local constraints, not just theoretical specs.

  • Math and modeling mindset:

    • Undergraduate minor in mathematics and early exposure to modeling and data analysis.

    • Later formalized some of that through graduate-level data science coursework (statistics, classical ML methods, reproducible research), even though I ultimately pivoted fully into industry.

These experiences are background context now: they don’t define my current role, but they shape how I reason about systems, risk, and real-world constraints when building AI-driven automation. They shed a light of understanding for how LLM’s and AI works through a lens of complex biological systems and multi-variate and dimensional mathematics.

Personal Projects & Working Style

  • Maintain and operate an computer lab environment (virtualization, services, LLMOps, Agentic Automations, etc.) to test ideas before bringing them into client contexts.
  • Enjoy exploring “edge of capability” use cases for LLMs and agents, then distilling what works into practical patterns others can adopt.
  • Prefer to deeply understand a problem space, design a system that balances ambition and risk, and then iterate in tight feedback loops with stakeholders.