SEPTEMBER 2023 RELEASES
September 2023 Releases
Introducing the Multi-State Agent Builder
Enterprise support and sales conversations are anything but linear.
They branch in dozens of directions. They loop. They have multiple conditions and possible transitions at every turn.
In order to ensure that your AI agents deliver personalized, outcome-driven experiences at scale, they need to be built in a way that can match the non-linear structure and complexity of enterprise conversation flows.
How you manage the prompt, trigger data retrieval, and inform decision flow logic are all major points of control for AI agent performance. They inform how many types of conversations your agent can handle, if they handle them in a way that’s enjoyable for the customer, and whether they’re efficiently driving outcomes for your business at scale.
Enter, Regal’s Multi-State AI Agent Builder
Regal’s Multi-State AI Agent builder is a purpose-built way to design multi-turn, multi-decision conversation flows, while embedding your distinct business logic at every turn.
It’s a non-linear interface for non-linear conversation paths.
Regal’s Multi-State AI Agent Builder splits up prompting across a global prompt (to enforce consistency) and local prompts (for fine-grained actioning and customization).
Local prompts are linked in a drag-and-drop conversation flow builder, where conversation paths can be orchestrated logically.
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The result: AI Agents that not only perform at enterprise scale, but also align with your business logic while matching the depth, breadth, and complexity of real, non-linear conversations.
Multi-State AI Agents unlock control and scalability over conversations that are too complex for single-state agents.
When Single-State Isn’t Enough
A single global prompt can take an agent a long way.
It can provide persona and role guidance for the agent (i.e. who the agent is and what their goals are), tone and style, guardrails, and how to handle certain questions and customer objections.
It’s effective at covering the predictable bases (i.e. the 70-80% of straightforward cases across lead qualification, scheduling, and inbound support calls).
There’s only so much that can be packed into a single prompt, however.
As soon as conversations become multi-turn and multi-decision, they require deeper, more structured prompting, and more dynamic logic informing how the AI Agent should act (based on the context of every turn).
LLM Decisioning
LLMs generate responses by weighing all tokens in the conversation history.
Each conversation turn adds new information to the context window (an AI agent’s short term memory). As that window grows, the AI Agent continues to weigh all of it.
The problem: LLMs don’t have a built-in way to logically structure and parse tokens in a way that aligns with your customers and desired outcomes.
The result: Unpredictable behavior and performance. Your AI Agent’s ability to consistently and precisely follow directions weakens the longer and more complex the conversation and prompt become.
- Patterns over logic: LLMs act on language patterns, not on business logic. In a multi-decision flow, they don’t immediately know how to prioritize new incoming tokens. When your conversation flows need to acknowledge dozens of possible scenarios, the model won’t natively know which details should guide its behavior, forcing you to overload the prompt in an attempt to control it.
- Recency bias: It’s been shown that LLMs favor and interact with more recent tokens (being more recent inherently makes the information cleaner to recall). As the prompt and conversation context grow, the AI may cloud over prompt instructions provided early in-sequence, or important information a customer shared in an old conversation.
Prompt Maintenance and Scalability
From a prompting perspective, relying on a single giant prompt simply doesn’t scale. Massive prompts are:
- Hard to build and parse: A long block of text with 20+ different paths is nearly impossible to follow or organize. How do you type out detailed instructions for every branch, checkpoint, and exception? What if multiple people are editing the prompt? As the prompt grows, it’s nearly impossible to follow what the agent is doing, and you’re more likely to see inconsistent syntax and formatting across prompt editors.
- Difficult to test and debug: With everything in one block, you can’t isolate scenarios. QA becomes a matter of trial-and-error, because it’s unclear which part of the prompt is driving which outcome. Designing effective test scenarios is tricky, and even with careful planning, important cases are likely to slip through the cracks.
- Risky to update: When all logic is packed into one prompt, even small edits (like rewording an objection-handling step) can ripple across the flow, shifting how the model interprets unrelated scenarios and introducing regressions.
A single prompt isn’t equipped to guide decision-making across complex flows. As conversations unfold, you’re asking the model to somehow know:
- Which branch of logic to follow at any given moment.
- How to “jump” back and forth between steps if a customer interrupts, changes an answer, or shows confusion.
- How far to look back in the prompt for relevant instructions when the conversation pivots.
Multi-State AI Agents help make sure your prompts don’t become a liability.
Multi-State Matches the Complexity You Need
Multi-state allows you to make each turn, each decision, its own state, therefore decreasing the agent’s context window, leading to reliable performance at scale. You can set clearer guidance for:
- Complex branching: An inbound support flow might have 20+ different handling scenarios. A qualification flow might have 25+ steps, all with conditional splits, that must execute in order. Managing this in one prompt is unworkable at scale.
- Sequential checkpoints: Many enterprise flows require passing through checkpoints (like verification or eligibility) before advancing. Multi-state allows responses to be recorded as variables (instead of relying on the memory of the LLM) and next steps are only taken if a verification or other required action is completed.
- Dynamic scenarios: Customers may switch languages mid-conversation or pivot topics unexpectedly. With state-specific logic, the agent can adjust seamlessly without requiring a transfer.
It’s simple in nature but hard in practice: Multi-state breaks your AI Agent build down into more parts, leaving less decisions for your AI Agent to make per every conversation turn. This makes them more likely to deliver personalized, sharp, business-aligned responses, even during robust, multi-condition conversation flows.
Greater Certainty & Control Across Tasks at Scale
Regal’s Multi-State Agent Builder helps you control performance at scale by letting you manage a global prompt and state-level prompts separately.
That means you control who the agent is in a global prompt, and in a series of separate mini-prompts, instruct how they should act per individual steps in a conversation flow.
You can isolate every turn only to the script, knowledge base, voice, model, and custom variables the AI needs to complete that exact turn.
The Global Prompt
As you would in a single-state setting, in Agent Details, you’ll define the agent’s:
- Persona (Name, default voice, default AI model)
- Goals (general job description and metrics they perform to)
- How they should act (question and objection handling, guardrails, greeting behavior, style guidance)
You’ll also set general data retrieval pathways and on-call settings (fallback voices, voicemail settings, Custom Analysis)—here you can add custom actions and knowledge bases that would carry across all conversation paths.
The global prompt impacts every interaction the agent has (unless overridden by state-level prompts, which we’ll touch on below).
Consider this the guiding principle of who your agent is and the style in which they interact.
State-Level Prompts
When you move to the Conversation Flow tab, you can orchestrate the AI Agent’s decision-making patterns based on a real, visual conversational flow.
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This separation of global and state-level prompting minimizes the noise around decision-making for the LLM. Per every individual turn in the conversation flow, it gives you precise control over:
- How and when the AI applies knowledge base data
- What data needs to be validated or collected (as a variable) within that turn
- What events need to happen before the AI agent moves to the next step
- What the logical next step is based on the events that did occur
Richer Customizability & Personalization
With more points of control comes more points to customize interactions with customers. State-level nodes allow you to define RAG pathways, what custom variables to capture (to use later in the flow), and multi-layered logic that determines what happens next for the contact.
This layered prompting drives:
- Better decision-making: Ensuring the agent consistently applies business logic and drives callers to the outcome they need.
- Predictable retrieval: Since RAG is scoped per turn, the agent pulls only what’s relevant to the current state, improving accuracy and consistency.
- Adaptability at every turn: Dynamically adapting tone, next steps, and actions on a more granular level, which creates a more enjoyable, more human experience.
- Deeper visibility for future personalization: With turn-level observability into each step of the flow, you have more data points to react to and refine future interactions.
What Can You Do With State-Level Nodes?
- Prompt Nodes: Instruct the agent how to speak to the user, define conditions that determine next steps, add in an LLM or voice override (over the default, defined in the global prompt), define which knowledge bases to use (in addition to what’s defined in the global prompt), and what variables should be collected from the interaction.
- Action Nodes: Perform backend tasks (e.g. API calls to update your CRM), call an action (Transfer Call, End Call, Gather Date), and branch the flow based on the results of that action.
Transition Prompts are used within Prompt and Action nodes to define what needs to happen to determine next steps (i.e. if the contact says “yes” do A, if the contact says “no” do B).
Multi-State in Practice: Employer Health Insurance
Let’s consider an industry with a complex lead qualification flow—employer health insurance.
For this flow, Prompt Nodes would be used to instruct the AI agent to greet the lead warmly, explain the objective of the call, ask if it’s a good time, and then either schedule a callback or carry on with qualification questions. Prompt Nodes would also be used at each step to instruct the AI how to ask and respond to each of the qualifying questions.
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Transition prompts would be used throughout to guide the AI on how to react to different answers. For example, “is now a good time to talk?” Your transition prompt would define that “no” triggers a path to schedule a callback, and “yes” triggers the AI to continue on with qualification questions.
While qualifying the lead, the AI agent might have to solve for more complex math and reasoning (i.e. having to weigh the number of employees and projected new headcount across different plan options).
To address this, within the Prompt Node, you could switch from GPT 4.0 Mini (set in the global prompt) to GPT 4.0, which is better suited for more complex or open-ended reasoning. The agent will revert back to the global prompt after that turn is complete.
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Prompt Nodes are important for driving the conversation forward in a manner that’s pleasant and productive.
The Action Nodes then make sure tangible (backend) actions are taken to turn those conversations into outcomes. For this flow, you’d use Action Nodes like:
- Schedule callback (if contact is unavailable and agrees to a new time)
- End call (if the contact isn’t interested or does not qualify)
- Gather date and time (for a callback or needed follow up)
- Transfer call
- Custom actions for CRM updates or downstream info share (i.e. sending call summaries to transfer agents)
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Handle Deeper Complexity of Interactions
Enterprises need agents that can handle the full breadth and depth of possible call paths. Multi-State is built for that non-linearity.
It gives you the structure to orchestrate dozens of branches, enforce checkpoints, and adapt dynamically to customer inputs, without overloading a single prompt or relying on the LLM to hold it all in memory.
And since you’re prompting on a more granular level, that means there’s more (scenarios, data points, objections, “if-then” conditions) you can acknowledge per conversation flow.
Consider the following support flow:
- A contact asks to reset their password, and so the AI Agent carries down the relevant path.
- Before that path is complete, the contact asks another, unrelated question about billing.
- OR, perhaps the contact missed a detail halfway through the flow and the reset doesn’t work—so they have to restart the process.
In cases like this, being able to link nodes in a non-linear manner is crucial. The agent can cleanly loop back to the beginning of the flow, or enter a new state to handle unrelated questions (without having to transfer).
Part of the challenge with DIY solutions is that this complexity can quickly become unmanageable if you’re only working in text or code.
That’s why Regal centralizes orchestration in an accessible drag-and-drop builder. All the underlying logic—global prompts, state-level overrides, multi-condition decisions, variable capture—is there, built into the aforementioned nodes.
The interface keeps it intuitive to design, adjust, and manage.
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With the builder, you can:
- Visualize and stitch together dozens of node-based paths in a visual flow.
- Add conditional logic, data capture, and escalation steps.
- Scale complex workflows in a way that’s observable, editable, and easy to review and maintain—for technical or non-technical stakeholders.
Multi-State takes complexity that would otherwise be fragile and unmanageable and makes it structured, centralized, and accessible—in a way that translates to the customer-side as well.
Testing Before You Go Live
Building complex, multi-branch flows is only part of the equation—you also need confidence that those flows will perform as intended. With Regal’s Test Logic and Test Audio functions, you can preview both the decision pathways and the end-user experience before deploying.
You can validate conversation logic to make sure your agent follows branches correctly, like actions invoked and transition reasons.
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Test audio helps further test the conversational quality while also testing for voice quality—whether the AI delivers a natural, on-brand experience.
This way, you’ll spot potential drop-offs before customers ever pick up the phone—reducing risk, cutting iteration cycles, and ensuring your AI Agent is production-ready, no matter how complex the use case.
Purpose-Built for Your Complexity
Enterprises don’t need AI agents that simply “sound good,” they need agents that can reliably perform in the messy, non-linear reality of real customer conversations.
For sophisticated conversations with branching logic, that requires more than a single prompt and more than patched-together tools. It requires a purpose-built way to design, orchestrate, and measure conversation flows on a path-by-path level.
With Regal’s Multi-State Agent Builder, enterprises get exactly that: a centralized platform where global rules, state-level prompts, orchestration, and reporting live together.
Multi-State is about giving you the structure and visibility needed to scale AI agents with confidence across any level of unique complexity.
Ready to start building? Schedule a demo with us.
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Founded in 2020, Regal is an enterprise voice AI agent platform for contact centers. Regal helps businesses build, deploy, and manage autonomous AI agents across sales, support, and operations teams.
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