Using AI And AI Agents to Handle the Difficult Tasks in Your Business
A quick recap for those who have attended my recent talks.
I recorded this lecture, and then transcribed, extracted, and drafted this blog post using AI.
The emergence of AI agents represents a fundamental shift in how businesses operate, design processes, and serve users. This paper synthesizes the principles and real-world practices derived from years of hands-on experience across government services, AI startups, and open-source communities. It covers the evolution from traditional software to AI-augmented solutions, offers a roadmap to effective implementation, and outlines the ethical frameworks necessary for responsible AI deployment.
Check out the slides from today's presentation.
Introduction
AI tools, particularly AI agents, are rapidly transforming how we build, deliver, and manage digital systems. My journey into AI began through years of product management, business development, and systems design. Today, through my work at Fearless and Virgent AI, I help organizations use AI not just to innovate but to solve real problems in finance, supply chain, marketing, and beyond.
1. The Rule of Three: Business, User, and Feasibility
At the heart of any successful AI initiative lies balance:
Business Need – Can it keep the lights on or create new revenue?
User Need – Will it improve the user experience or introduce new friction?
Feasibility – Is it technically and operationally achievable?
Ignoring one leads to failure. Prioritizing only the user or business outcome without feasibility is unsustainable. Conversely, focusing on feasibility alone can lead to ethically problematic or financially ineffective solutions.
2. From Prompting to Agents: A Spectrum of Capabilities
Understanding AI today means understanding three components:
Prompt Engineering – The art of crafting instructions for AI.
Model Selection & Fine-Tuning – Choosing or customizing the right LLM.
Agents – Task-oriented systems that can reason, fetch data (RAG), and act.
These components work best together. An effective AI agent leverages good prompting and a suitable model to automate workflows and decision-making processes.
3. Real-World Use Cases
At Fearless, I have used prompt engineering to accelerate proposal generation and solutioning. At Virgent AI, we help companies automate internal requests, triage emails, and even analyze procurement patterns. For example:
Proposal Parsing Agent – Extracts key requirements, win themes, and past performances.
Email Triage Agent – Prioritizes and drafts responses, schedules meetings.
Customer Service Agent – Handles password resets and document retrieval.
All of these are powered by modular systems using open-source tools like LangChain, Pinecone, and Hugging Face.
4. Tools and Technologies
We made this animation together! Here is the midjourney prompt: a dog accountant, 1980s style newspaper ad
Language Models & Vector Stores:
OpenAI GPT-4/4.5 (powerful but costly)
Mistral, LLaMA (open-source, fine-tunable)
Pinecone, Weaviate (for fine-tuning and embeddings)
Tools & Frameworks:
LangChain: Language Model Application Development Framework
Replit / Cursor / V0.dev: Prototyping and dev environments
Hugging Face: Model exploration
Runway & MidJourney: Image and video content creation
Suno AI: Music creation
Notebook LM & Cadderly: Tools for organizing AI-augmented thinking and project memory.
5. Implementation Strategy: Build vs. Buy
Many companies rush to buy enterprise tools like Microsoft Copilot or ChatGPT Enterprise but see low adoption. The reasons:
Employees fear job loss
Lack of training
Poor integration into workflows
Instead, follow this process:
Map the Process – Service blueprinting to identify bottlenecks
Start with Small Wins – Identify low-effort, high-impact automation targets
Pilot Internally – Use tools like Claude or ChatGPT to test internally
Train & Scale – Educate staff on responsible, productive AI usage
6. Ethical & Ecological Considerations
AI is not neutral. Every implementation must consider:
Data Privacy – Self-hosted models offer control, but carry overhead
Bias and Accuracy – Hallucinations stem from the creativity-accuracy tradeoff
Environmental Impact – Training and deploying large models consumes energy
It is essential to prioritize user impact alongside business goals and technical feasibility.
7. The Human Factor: Creativity and Ownership
Despite automation, humans bring the last 20%—the nuance, the vision, the responsibility. AI can:
Get you to 80%
Help you finish faster
Accelerate repetitive or high-cognitive-load tasks
But it is not a replacement for lived experience, accountability, and ethical judgment.
8. What Comes Next: The Rise of Personalized Models
We are entering an age where everyone will have:
Their own agent
Their own fine-tuned model
Their own mini-utility for cognitive work
Licensable models—trained on your voice, style, decision-making, even values—will be the future of personal and organizational productivity.
Conclusion: Building Responsibly, Acting Now
AI agents aren’t a buzzword. They’re here. And those who learn to use them thoughtfully will lead the next generation of business, creativity, and governance. Whether you're starting with Replit, building in LangChain, or just writing better prompts—get started now.
Because AI won't take your job. But someone using it might.
Check out the slides from today's presentation.
Note: This paper was generated and refined through AI-assisted processes using prompt chaining, RAG, and custom tooling like Cadderly.
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