Think about the last time you trained a new customer service agent. You probably handed them a stack of documents, like your standard operating procedures, policy guides, and support scripts, and hoped they'd quickly figure out how and when to apply which rule. Now imagine giving that same documentation to an AI agent and having it actually understand, follow, and execute those procedures from day one.

That's precisely what Agent Operating Procedures (AOPs) make possible.

Agent Operating Procedures are natural language instructions that compile into validated workflows for AI agents. Unlike traditional chatbots that simply answer questions, AOPs enable AI to take real actions, such as processing refunds, verifying identities, updating subscriptions, and resolving issues end-to-end. This resolves the tension between natural-language flexibility and code-level control that has long frustrated customer-service automation.

What are Decagon's agent operating procedures (AOPs)?

Agent Operating Procedures are natural language instructions that compile into structured logic for AI agents to reliably execute workflows. 

When you train a human agent, you give them Standard Operating Procedures (SOPs). These documents explain what to do when a customer wants a refund, how to verify someone's identity, or when to escalate a situation. AOPs work the same way for AI agents. They translate your existing business rules into a format that AI can understand, follow, and act on consistently.

What makes Decagon's approach different is the hybrid architecture. CX teams can write agent logic using everyday language. Meanwhile, engineers maintain control over the underlying systems, implementing procedural thinking, setting up guardrails, and managing integrations with tools like Zendesk or Salesforce. This balance means business experts shape how the AI behaves while technical teams ensure everything runs securely.

How AOPs power LLM agentic workflows

An LLM agentic workflow describes AI that doesn't just respond to questions but actively completes tasks across multiple steps. Traditional chatbots follow rigid scripts. Ask something unexpected, and they fall apart. AOPs enable far more nuanced conversations.

With AOPs, an AI agent can handle a customer saying "I need to return this broken item and get a replacement shipped to my new address" by breaking that request into logical steps: 

1. Verify the order.
2. Confirm the return policy applies.
3. Process the return.
4. Update the shipping address, and 
5. Arrange the replacement. 

Each step adapts based on what the customer says and what the systems reveal.

This flexibility comes from combining natural language understanding with deterministic execution. The AI interprets messy, real-world customer input. The code-based logic ensures that critical actions are executed correctly every time. Refunds get processed according to policy. Identity checks follow security protocols. Account changes go through proper validation.

The result is AI that handles the unpredictable nature of customer conversations while maintaining the consistency your business requires.

How do AOPs for AI differ from SOPs for humans?

Standard Operating Procedures written for humans tend to make a lot of assumptions. They expect the reader to fill in gaps, use judgment, and draw on experience. A human SOP might say "verify the customer's identity" without spelling out every possible way to do that. Human agents naturally understand context, read between the lines, and adapt when situations get weird.

AI doesn't work that way. It needs explicit instructions for every scenario it might encounter.

AOPs take the intent behind human SOPs and translate it into structured logic that AI can execute reliably. They preserve the flexibility to handle varied customer inputs while adding the precision required for consistent outcomes.

Here's how the two approaches compare:

Benefits of using Agent Operating Procedures

The shift from traditional automation to AOPs has advantages for both the customer experience and the bottom line. Here's what businesses actually gain when they make this transition.

Speed and agility

Traditional AI implementations can drag on for months. You hire consultants, build decision trees, test endlessly, and hope everything works when you finally launch. AOPs compress this timeline dramatically.

Initial AOP implementation typically takes 3-6 weeks from kickoff to production. This includes drafting procedures, integration work, and testing and refinement. Because CX teams author workflows in natural language rather than waiting for developers to write code, iteration happens in days instead of weeks.

When your refund policy changes or you launch a new product, updating the AI doesn't require a development sprint. Your team can adjust the procedure, test it, and push it live. This agility means your AI agent stays current with your business rather than lagging behind.

Increased omnichannel efficiency and reliability

AOPs enable AI agents to deliver instant responses at any hour, on any channel. As a result, the efficiency gains stack up fast:

• Response times drop from minutes or hours to seconds.
• Support coverage can be extended 24/7 without overnight staffing costs.
• A single AOP definition works across chat, email, and voice simultaneously.
• Seasonal spikes no longer require scrambling to hire temporary staff.

In our experience, businesses using this approach can consistently achieve 70-80% deflection rates while maintaining or improving customer satisfaction scores. The AI handles routine workflows, freeing human agents to focus on conversations that genuinely need their expertise.

For instance, when NG.CASH switched from using a decision-tree support platform to Decagon’s AI agents for customer support, their deflection rate increased to 70%, substantially reducing manual escalations.

“Decagon has been incredibly impactful for us. Through our recent acquisition, we scaled our customer base dramatically without having to add to our support team or waste time on complex preparation. Their AI agents have made our transition seamless and efficient while increasing customer satisfaction.”

• Petrus Ballhausen Arruda | Co-Founder & COO, NG.CASH

Continuous improvement built in

AOPs aren't a set-it-and-forget-it solution. The real power comes from the infrastructure that supports ongoing optimization.

Robust testing tools let teams validate changes before they reach customers. You can:

• Run simulations at scale to see how updates perform across thousands of scenarios. 
• Create unit tests that verify responses stay on-brand and policy-compliant. 
• Schedule automated regression testing to catch problems before they cause issues.

Versioning tracks every change to your procedures. When something goes wrong, you can trace exactly what changed and roll back instantly. When something works well, you can identify why and replicate that success elsewhere.

Alerting and insights highlight patterns you'd otherwise miss. You can spot emerging customer issues before they become widespread, identify workflows that need refinement based on actual performance data, and measure the impact of every adjustment you make.

All of this happens within enterprise-grade security guardrails. Critical operations such as identity verification and payment processing are conducted under strict validation protocols, providing regulated industries with the compliance controls they require.

How to get up and running with AOPs

Here's how to turn your existing documentation into production-ready AI workflows.

1. Audit your existing procedures

Before building anything, take stock of what your team already uses. Pull together your SOPs, support scripts, knowledge base articles, and policy documents. These resources contain the logic your AI agent needs, all you need to do is fix the format.

Look for procedures that share these characteristics:

• High volume and repetitive, consuming significant agent time.
• Clear rules with defined outcomes rather than situations requiring subjective judgment.
• Multiple steps that follow a predictable sequence.
• Frequent enough to justify the setup investment.

Skip procedures that rely heavily on emotional intelligence or require complex negotiation. Those should stay with your human agents for now.

2. Translate documentation into AOP format

Once you've identified your target workflows, the translation process begins.

1. Break each procedure into discrete steps with specific triggers and actions.
2. Identify the data points required at each stage, such as customer ID, order number, or account status.
3. Define the decision points where the workflow might branch based on different conditions.
4. Specify what success looks like and when the AI should escalate to a human.
5. Add validation requirements for sensitive actions like refunds or account changes.

CX teams handle most of this work using natural language. You're essentially writing instructions the way you'd explain the process to a new hire, but with more explicit detail about edge cases. Engineers then add the code-level controls that ensure critical operations execute securely.

3. Connect with your existing tools

AOPs are designed to sit on top of your existing tech stack, to ensure continued efficiency. Decagon’s AI agents integrate with the systems your team already uses, enabling them to pull information and take actions across your entire infrastructure.

You can connect AOPs to helpdesk platforms for ticket management and customer history, e-commerce systems for order data and transaction processing, internal databases for account information, subscription details, and custom business logic, and knowledge bases for accurate product documentation and policy information.

These connections let your AI agent make complex multi-step decisions and take relevant actions, such as checking order status in real time, processing a return per your policies, updating a shipping address in your system, and confirming the change back to the customer, and so on, all within a single conversation.

Pre-built connectors can easily integrate many common platforms, but custom databases, legacy systems, and tech stack compatibility issues require more engineering effort. Plan accordingly during your initial implementation scope.

Are there any alternatives to Decagon's AOPs?

The market for AI customer service automation has grown crowded. Several vendors now offer platforms that promise to automate support workflows, each taking a slightly different approach to the challenge.

Some platforms focus primarily on conversational AI, excelling at natural dialogue but offering limited ability to take real actions within your systems. Others emphasize pre-built templates and industry-specific solutions that work well for standard scenarios but struggle when your business has unique requirements. A few take a heavily services-dependent approach, requiring ongoing professional support to make changes or add new workflows.

You'll also find established helpdesk providers adding AI features to their existing platforms. These bolt-on solutions benefit from tight integration with their core product but lack the depth and flexibility of purpose-built AI agent platforms.

What sets Decagon apart

Decagon takes a product-centric approach rather than a services-heavy model. This distinction matters more than it might seem at first glance.

With Decagon, non-technical CX teams can build and refine AI agent logic directly. You're not waiting on consultants to implement every change or paying professional services fees each time your policies update. Your team shapes how the AI behaves using natural language while engineers maintain control over security and integrations.

This balance has several practical advantages:

• Speed to production. Initial implementations reach live deployment in weeks rather than months.
• Rapid iteration. Policy changes and workflow updates happen in days, not development cycles.
• Lower ongoing costs. Your team manages routine adjustments without external dependencies.
• Enterprise-grade security. Built-in guardrails protect sensitive operations, such as refunds and identity verification, from the start.

Our platform also provides comprehensive testing, versioning, and monitoring tools that many alternatives lack. These capabilities enable teams to validate changes before deployment and continuously improve performance based on actual interaction data.

This is one of the major reasons why our clients like working with us. As Emma Auscher, the Global Head of Customer Experience at Notion puts it,

"We conducted a rigorous RFP process, evaluating everything from interaction quality and user interface to the depth of integrations, product roadmap, and the caliber of engagement and partnership offered. Decagon stood out across the board - not just in these core areas, but also through their close collaboration with our technical team and their ability to meet our stringent security and compliance standards."

In a market full of compromises, Decagon offers a unique combination of accessibility and rigor for businesses that want control over their AI agent's behavior without sacrificing security or scalability.

Take your customer service to the next level with Decagon

The gap between what customers expect and what traditional support teams can deliver continues to widen. People want instant answers at any hour, but traditional chatbots can’t cope with nuance, making users bounce between departments.

Decagon’s Agent Operating Procedures close this gap.

Your team already has the knowledge needed to deliver exceptional service in your SOPs, training documents, and support scripts. With Decagon, you can transfer that existing expertise into AI agents that address all customer concerns with consistency and speed.

Schedule a demo to see how Decagon can help you move past generic chatbots and deploy smart agents that actually work.