This episode breaks down what agents really are, moving past the "Matrix guy with sunglasses" imagery. Lutz and Jasper explain how agents are pieces of code that change how we interact with computers, taking defined tasks and breaking them down step by step.

A key insight emerges: agents aren't entirely new. Agents build on concepts from Robotic Process Automation (RPA), but with crucial differences. Where RPA was limited to rigid rule-based decisions, agents can handle uncertainty and unstructured data.

The discussion takes an interesting turn when Lutz brings up Microsoft's Clippy - the infamous paperclip assistant. While Clippy was widely mocked, it represents an early attempt at what we're now trying to achieve with agents. The key difference? Modern agents are more sophisticated in how they can be guided and controlled.

Cherry Ventures share have insights from their investment portfolio, including:

• Software testing companies using "self-healing" mechanisms
• E-commerce applications that improve search and product discovery
• Document analysis and processing systems

In this episode of The Edge, we sit down with Eric Siegel, a 30-year machine learning veteran and founder of Gooder AI, to discuss the critical challenges enterprises face in deploying predictive AI models.

Episode Highlights:

The Deployment Problem

• Introduction to the "Value Translation Gap" in enterprise AI
• Why only 15-20% of predictive models reach production
• The four critical predictions businesses rely on: who will click, buy, lie, or die

Why Models Fail

• The "metrics mirage" problem in AI deployment
• Understanding the workflow-reality gap
• Scale challenges in moving from pilot to production
• Implementation costs (26%) and ROI translation (18%) as key barriers

BizML Framework

• Three essential concepts for business stakeholders:
  • What's being predicted
  • How well it predicts
  • What actions those predictions drive
• Translating technical metrics into business outcomes

The Future of AI Products

• Evolution from consulting to product-based solutions
• The importance of domain-specific architectures
• How successful companies embed business logic into ML pipelines

Investment Opportunities

• Value Translation Tools
• Vertical Solutions
• Deployment Frameworks
• The shift from model development to value realization

Featured Guest: Eric Siegel, Founder of Gooder AI and machine learning veteran

Guest: Ferdinand Terme, Lasqo AI

Key Topics

• Why vertical AI startups are winning against tech giants
• Building B2B AI products vs features
• The role of human-in-the-loop in scaling AI solutions
• Transitioning from consulting to AI entrepreneurship

Main Insights

Path to Entrepreneurship

• Started at McKinsey Casablanca (French-speaking Africa focus)
• Joined Alma during hypergrowth (50 to 400 employees)
• Explored Fintech before pivoting to AI/creative space

Building Lasqo AI

• AI designers for marketing teams in SMBs
• Focused on brand-consistent visual asset creation
• Accessible through Slack integration
• Uses custom-trained models (LoRA) for each brand

Why Focus Wins

• Large companies struggle with maintaining quality across broad offerings
• Startups can:
  • Build deeper rather than broader
  • Maintain tight feedback loops
  • Take calculated risks
  • Focus on specific use cases

Human-in-the-Loop Strategy

• Initial model training with brand guidelines
• Cost-effective for B2B recurring revenue model
• Client feedback drives continuous improvement
• Balance between automation and quality control