I speak with countless customers and candidates, and the number one question I get is: What sets Decagon apart from all the other companies who say they build AI agents for customer service and customer experience?

The simple answer? Our product-centric approach.

When we think about customer service, most of us think of long wait times, unhelpful chatbots, or those frustrating moments when you press “0” repeatedly, trying to get to a real human to help with your issue. Why have businesses struggled to deliver great customer experience in the past, and why have these issues persisted even with some of the newer generative AI solutions?

• These systems are costly and slow to build: These solutions require technical resources or professional services to come in and mold the AI agent’s logic into some sort of framework. That framework usually looks like a clunky decision tree or people writing code to approximate what they want the AI agent to do. These systems break under pressure because they aren’t designed to quickly adapt to real-world situations, and they’re ultimately too brittle to scale and evolve with the needs of businesses and their customers.
• They quickly become black boxes: When an AI agent build is carried out by professional services, the AI agent becomes a black box. It’s impossible to know why an AI agent is taking the steps it's taking at any given time. When you can’t see how decisions are made, it’s extremely challenging to iterate on the workflow logic without playing a game of telephone, passing requests and requirements back and forth with your professional services team. 
• They produce poor quality results: And when you can’t iterate quickly, you can’t reliably deliver great quality results that are at or above what a human agent would deliver. When these AI agents are interacting with your end users, it’s critical that the process is transparent and secure—after all, these interactions are the face of your brand to your customers. 

At Decagon, we’ve developed a radically different approach to solve these problems. We call it Agent Operating Procedures (AOPs). AOPs combine the power and flexibility of natural language with the precision and rigor of code. This innovative combination allows Decagon to deliver AI agents that are not only smarter and faster, but also more secure, reliable, and scalable.

Agent Operating Procedures (AOPs)

Just like how human agents rely on Standard Operating Procedures (SOPs) to handle a wide range of situations, AOPs enable AI agents to dynamically respond to complex issues and execute multi-step workflows. AOPs empower non-technical business users—like your CX teams—to shape and mold the AI’s logic to meet their exact needs. Meanwhile, engineers retain control over the core systems, enforcing rules and ensuring the AI integrates seamlessly with your existing infrastructure.

This unique flexibility allows businesses to build and iterate on AI solutions quickly. Instead of spending months developing a solution, you can deploy AI agents into production within weeks, continually refining them based on real-world performance. The result is a more personalized and intuitive experience for your customers that continues to improve over time.

Faster time to value

At Decagon, speed to build and iterate is at the core of everything we do. Traditional AI solutions come with long development cycles and the added complexity of costly professional services. We’ve eliminated these hurdles with AOPs, empowering businesses to quickly build and deploy AI agents without waiting months. 

By allowing your CX team to become architects of your AI logic, AOPs enable your CX experts—who know your business and customers best—to shape the AI agent development process. Our advanced, iterative prompting platform ensures that businesses can deliver measurable ROI in weeks, not months, creating real-time value that drives business impact. And, with seamless integration with existing support tools and workflows, AOPs can pull in data, take actions, and trigger responses, all while ensuring your engineers retain full control over the core code.

Unparalleled transparency

Transparency is a critical aspect of building trust in AI. With AOPs, Decagon prioritizes full visibility into the logic and decision-making process of your AI agents. You can define your custom business logic using simple, natural language instructions that are easy to understand and modify. This means you can clearly follow every step the AI agent takes throughout a conversation. 

Our robust suite of tools also provides transparent observability, so you always know why an AI agent made a particular decision at any point in time. Coupled with rapid iteration capabilities through testing, versioning, and real-time alerts, Decagon ensures that you can continuously refine your AI agents, learning and adapting based on real-world data and insights.

Trusted results at scale

Decagon’s AI agents are built to scale with your business while delivering trusted, high-quality results. Whether you're verifying identities, processing refunds, or handling complex customer interactions, AOPs provide enterprise-grade guardrails to ensure that critical operations are executed securely and with strict validation. 

The combination of natural language flexibility and code-based precision allows AOPs to adapt to the unpredictable nature of customer inquiries. With turnkey scalability, businesses can deploy hundreds of AOPs, ensuring that each interaction is handled intelligently and efficiently. 

Decagon was purpose-built for the enterprise, with security, privacy, and observability as core principles. From day one, we’ve prioritized secure access, robust data controls, and seamless management, making Decagon the trusted choice for industry leaders, including those in highly regulated industries.

Customer experience is evolving, and AOPs will play a critical role in that transformation. At Decagon, we’re not just building AI agents; we’re building a new way for businesses to connect with their customers—smarter, faster, and more intuitively than ever before. We’d love to show you how.