The Evolution of AI Agents: From Chatbots to Multi-State

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October 27, 2025

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5 min read

Customer expectations evolve faster than most contact centers can keep up with. At the same time, generative AI is advancing voice intelligence at a breakneck pace. It’s constantly reshaping what’s possible in every interaction.

For contact center leaders, it’s a lot to take on.

You’re definitely feeling the AI transformation evolution in more ways than one. You hear it from the market. You hear it from your CEO.

But deploying AI effectively across enterprise-scale workflows is anything but simple.

To understand where AI can take you next, it helps to look back at where it started, why it had to evolve, and how that led us to today’s most recent advancement—multi-state generative AI agents.

The introduction of multiple “states” to AI agents is what’s now separating AI that’s “good enough” from AI that transforms the customer experience at scale.

In this article, we’ll break down multi-state agents as the next evolutionary step in voice AI. We’ll look at the technical and business implications of multi-state, providing a clearer view on why it’s so important for meeting, and exceeding, rapidly evolving customer expectations.

From Rigid Scripts to Context-Aware Agents

Voice AI has always had its limitations, though. Chatbots and IVR with rigid scripting and pre-defined paths—even early GenAI agents struggled with multi-turn complexity.

Multi-state AI agents represent the next evolutionary step in this progression, representing the first instance of AI truly being able to handle end-to-end customer needs.

The landscape of voice and chat-based intelligence has evolved quite a bit in just the past two decades:

Chatbots (early 2010s): Scripted, FAQ-based web or app assistants. These followed rigid decision trees with no ability to adapt beyond predefined paths.
IVR/IVA (2010s–early 2020s): Interactive Voice Response and Interactive Virtual Assistants added voice interfaces, but remained static—callers still navigated menus or rigidly scripted logic. Useful for routing or basic self-service, but not dynamic.
Single-State GenAI Agents (2022–present): With the rise of large language models, a single global prompt gave agents flexibility to handle multi-turn conversations. They could sound natural and cover predictable workflows, though branching and backtracking were limited by the constraints of one massive prompt.
Multi-State GenAI Agents (2024–present): The next evolutionary step. Scoped prompts and explicit orchestration per state allow agents to carry enterprise-grade workflows end-to-end, reliably handling dozens of branches, loops, and checkpoints with precision and scalability.

Looking at this progression, it’s clear why multi-state agents became necessary.

Digital support workflows became more multi-channel, more complex. Customer expectations become more dynamic. Enterprises needed a way to drive greater coverage and improve customer experiences without ballooning cost to serve.

Multi-state agents solve the fundamental limitations that single-state models faced.

By isolating states, today’s AI agents overcome these challenges, directly driving the technical and operational benefits we’ll explore next.

Multi-State Changes the Prompting Paradigm

The move from single-state to multi-state directly improves how AI agents interact with LLMs and enterprise systems.

Drive Predictable Behavior with Controlled Context Windows

In a single-state build, both the AI agent and the human builder of that agent are forced to work off one massive prompt.

That means every instruction—objection handling, escalation paths, possible branches, conditional logic—sits in the same block of text.

As the context window grows, the LLM weighs all tokens simultaneously when generating a response (in other words, it weighs every unit of text it’s interacted with previously). This amplifies recency bias (favoring the most recent tokens) and creates priority ambiguity (no hierarchy for which instructions matter most). This increases the risk of instruction drift over long multi-turn flows.

For example:

A personal injury law firm handles cases for car accidents, dog bites, and employee rights. To qualify for each of these separately, a single-state agent would have all qualifying questions in one prompt (for dog bites, car accidents, and employee rights). While it’s possible to provide instruction in the prompt for each of these paths, it’s much more reliable to break them out into their own prompts. If each path includes 8-10 qualifying questions, using only one prompt opens the door for the AI to accidentally ask a question related to a car accident, even after the caller has mentioned a dog bite.

For builders, maintaining a single prompt becomes its own problem:

Editing a line for one scenario can ripple unpredictably across 20 others
QA becomes trial-and-error (because it’s very hard to find where you need to edit the prompt)
The literal task of writing and organizing such a massive block of text becomes error-prone at scale (especially as multiple people make updates)

Multi-state scopes prompts to the current state only:

With multi-state builds, each state isolates only the inputs required for that step—relevant KBs, variables, conditional logic, or LLM and voice overrides—so the LLM isn’t forced to use its built-in logic.

It’s only using your logic, to act on a task prompt, custom backend action, or conditional logic.

So now, the same law firm mentioned above can have one state-level prompt clarifying the contact’s reason for calling (i.e. dog bite versus car accident), and then branch off into a unique path only to qualify for that specific case type. The agent moves to the next step, which only includes questions about dog bites, and proceeds from there.

And from a workflow perspective, you’ll be managing smaller, modular prompts instead of one sprawling document.

So while the orchestration has more parts, it also introduces more isolated entry points to edit, test, and monitor performance without regression.

Reduce Failure Points with More Structured Data and Orchestration

Scoped prompting also cleans up the “plumbing” that sits underneath the prompt. The technical pathways that keep the AI connected to your CCaaS, telephony, CRM, and proprietary knowledge bases.

In single-state:

You connect knowledge bases and hope the model picks the right context when needed.
Data capture is implicit. The model “remembers” variables inside its context window, but doesn’t formally structure them for reuse.
Backend actions (like an API call to your CRM) have to be embedded in the same block of prompt instructions. As your prompt instructions grow, the model becomes less reliable at invoking actions at all, and even less reliable at invoking them at the right time.

With multi-state:

Knowledge retrieval is scoped to a specific state, so the agent only queries the KBs needed for that step in their flow. This ensures the model applies the right context at the right turn.
Data capture happens as structured variables, so eligibility checks, profile attributes, or form entries are carried downstream in predictable formats. That means cleaner handoffs to CRMs, claims systems, or scheduling tools.
Backend orchestration is explicit—action nodes trigger transfers, API calls, or scheduled callbacks. You don’t have to bury technical instructions in prose; the orchestration layer enforces them.
Reporting becomes path-level. Because every state is distinct, you can measure path by path, where conversations drop off, sentiment across nodes, and where customers are looping back.

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The net effect is a tighter, modular system architecture that reduces failure points, keeps data consistent, and makes the AI’s decisioning both observable and auditable.

The Bottom Line is the Bottom Line

For enterprises, the evolution to multi-state also directly impacts performance, cost, and customer outcomes.

Outcome alignment: Multi-state ensures every branch of a workflow reflects enterprise rules, compliance, and brand voice. The agent isn’t just responding, it’s driving conversations toward defined business outcomes in cleaner, more trackable steps.
Cost efficiency: Scoped prompting allows you to use lightweight models for routine steps and reserve advanced LLMs only for high-complexity tasks, optimizing operating costs. Take a loan service lead qualification flow for example. There will be a series of straightforward questions (name, DOB, product interest), followed by steps where more complex reasoning or math is required (projecting qualified loan amounts and monthly payments based on income, intended debt consolidated, and more). In this, you can use an LLM with higher reasoning for the later states.
Compartmentalized implementation and maintenance: Modular states make updates surgical. You can comfortably change one node without disrupting the entire flow, making iteration safer, cleaner, and potentially even faster at scale.
Adaptive experiences: Multi-state enables pivots mid-conversation. Agents can loop back, branch off, or escalate without losing context, improving containment, reducing handle times, and driving better CSAT.

The Enterprise Payoff: Better Outcomes, Lower Costs, Happier Customers

The rapid pace of AI evolution—accelerated by generative AI—means enterprises can no longer afford incremental improvements in customer experience.

Multi-state AI agents aren’t just a technical upgrade, they’re now a strategic necessity.

By modularizing prompts, scoping context, and orchestrating workflows end-to-end, enterprises can deploy AI that’s reliable, compliant, and truly outcome-driven at scale.

For CX leaders, this means two things: Move quickly to adopt purpose-built AI, and ensure your deployment is designed for enterprise-scale reliability. Those who embrace multi-state sooner rather than later will redefine what great customer experience looks like in a GenAI world.

Ready to deploy multi-state AI agents at scale? See how Regal makes multi-state deployment workable across your enterprise use case.

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