What great self-serve actually looks like: An Agent Product Manager’s perspective

“Self-serve” in customer service software means giving businesses the tools to design and shape their own customer support experiences. 

But not all self-serve is created equal. At its worst, it is rigid, brittle, and difficult to maintain. At its best, it is powerful, flexible, and intuitive. Great self-serve means teams can build exactly what they need, and more importantly, do it with ease.

At Decagon, Agent Product Managers (Agent PMs) are at the core of making that possible. We work at the intersection of customer needs, product design, and AI behavior, bringing a product mindset to how businesses shape their AI agents. Because we deeply understand both the logic teams want to express and how they want to express it, we’re uniquely positioned to design products that feel as intuitive as they are capable.

The 3 levels of self-serve: what works and why

An effective AI agent must be self-serve because it depends on two critical components:

• Intelligence that can take action, personalize responses, and integrate with tools and workflows without relying on brittle decision trees.
  
  
• Deep, domain-specific business logic that shapes how the agent should sound, apply policies, and handle complex scenarios.

To bring these two pieces together, you need a self-serve platform that allows CX teams to directly train their AI agents, much like they would train their best human agents.

But what makes a self-serve platform great? We guide customers to think of self-serve in three levels:

Level 1: Usable, but rigid and hard to maintain

• Early self-serve systems emerged as simple decision trees and flowcharts, allowing businesses to automate basic workflows with minimal setup. Yet as organizations expanded, maintaining these sprawling decision trees with thousands of interconnected nodes quickly became an overwhelming technical burden.

Level 2: Flexible, but difficult to operationalize

• Generative AI was expected to make it easier to build magical support experiences. Instead of manually configuring every step, teams could now guide AI using natural language and, in theory, build anything. But in practice, it often felt like handing customers a blank canvas with no guidance, leaving them stuck and unsure where to start. 

Level 3: Flexible and intuitive

• Great self-serve platforms combine flexibility with structure. They give teams the scaffolding to build, iterate, and maintain AI systems with confidence and clarity. 

While most solutions remain stuck somewhere between Level 1 and Level 2, Decagon is operating at Level 3, with a self-serve platform that combines the power of AI and the structure teams need to confidently build, iterate, and maintain high-quality experiences.

How Agent PMs have shaped Decagon’s self-serve product

At Decagon, Agent PMs are central to shaping our Level 3 self-serve platform. We work directly with customers to design and refine AI agents, giving us a clear view into what businesses are trying to build, where they get stuck, and what success looks like. This puts us in a unique position to bridge customer intent with product design.

One area where this impact is especially clear is in the evolution of Agent Operating Procedures (AOPs), our natural language framework for defining business logic that AI agents use. Through close collaboration with customers, we’ve made several key improvements in AOPs, including:

Balancing flexibility vs. precision: 

Customers love the ease and speed of natural language, but certain scenarios like compliance requirements demand precision. 

→ We developed mechanisms that allow teams to guide agent behavior with greater specificity, improving accuracy while preserving the ease of natural language.

Understanding AI agent behavior: 

Businesses need visibility into not just what the AI is doing, but why it’s doing it. 

→ We built tools into AOPs that expose the full decision-making trail: the rationale behind each choice, the step-by-step path the agent took to reach a conclusion, and the knowledge sources it referenced along the way.

Enabling diverse workflows: 

Engineering, support, and operations teams all work differently.

→ We built natural language, low-code, and full-code interfaces into AOPs to match each team’s preferred way of building and interacting with AI.

Instilling confidence: 

Teams need to trust that changes won’t break existing logic, especially when iterating quickly. 

→ We introduced path-level testing and full conversation simulations to validate updates before deployment.

Each of these shifts reflects how Agent PMs translate customer needs into product decisions. More importantly, they illustrate what sets Decagon apart: we’re not just building self-serve features, we’re building a Level 3 self-serve platform.

Great self-serve empowers teams—and it’s a competitive advantage

The difference between good and great self-serve isn't just in the AI. It’s how well the platform enables both non-technical and technical teams to shape it. At Decagon, we believe self-serve should mirror how people actually think, build, and improve. When powerful AI is paired with thoughtful structure, teams move faster, build better, and stay in control. That’s what makes great self-serve not just a product philosophy, but a lasting competitive edge. 

Curious what Level 3 self-serve looks like in action? We’d love to show you. 

Inspired to help shape the future of AI-driven support? Explore careers at Decagon.