Decagon Product Foundations

Traditional chatbots are built on rigid decision trees that break whenever customers go off script. Decagon’s AI agents take a different approach. They’re designed to handle real conversations with both flexibility and precision, while giving teams full control over outcomes.

Core to every Decagon agent is Agent Operating Procedure (AOP), natural language instructions backed by code that are like a standard operating procedure for AI. An AOP guides the AI agent step-by-step through a workflow, such as processing a refund or resetting a password. CX teams can create and update AOPs directly, while technical teams can add guardrails, integrations, and advanced logic.

When a customer message comes in, Decagon’s query processing pipeline runs through several stages. First, the agent extracts the intent, referencing the full conversation history for context. Next, it selects the right AOP to run. If no AOP matches, a fallback mechanism uses retrieval-augmented generation to pull relevant knowledge before responding. This process repeats with each new message, allowing the AI agent to adapt if the customer changes topics mid-conversation.

Reliability at scale is built in at every stage. Decagon provides testing tools during development, a Supervisor Model to monitor live conversations for safety issues, and escalation intents for high-risk topics. Brand voice guidelines ensure the AI always stays on-brand, while Watchtower flags quality issues after conversations end.

Finally, Decagon’s Build, Optimize, Scale framework helps teams manage the full agent lifecycle. Build with AOPs and brand voice, optimize with testing and guardrails, and scale across hundreds of workflows with analytics to measure CSAT and deflection rates.

Now you have a mental model of how Decagon AI agents work under the hood for structure, safety, and scale.