Agentic AI Workflows: How They Work, Examples & Benefits

A customer contacts support because their order is going to the wrong address. The chatbot understands the problem and explains the shipping policy, but it can’t check whether the delivery address can still be changed. The customer ends up waiting for a human agent to resolve something that could have been handled automatically.

Agentic AI workflows help businesses move beyond these limitations. Instead of simply responding to questions, AI agents can evaluate customer requests and take approved actions across connected systems. They can adapt to changing circumstances while knowing when human intervention is necessary. In this guide, we’ll explain how agentic AI workflows operate, where they deliver the most value, and how to implement them effectively.

TL;DR

Agentic AI workflows allow AI agents to make decisions and complete tasks autonomously. They use customer context and connected business systems to resolve requests without following rigid scripts.
Unlike traditional automation, agentic workflows adapt to changing situations. Agents can select appropriate tools, adjust their actions based on new information, and handle unexpected customer requests.
Agent orchestration connects AI agents with business systems. Through APIs and shared context, agents can retrieve information, execute approved actions, and coordinate complex customer interactions.
The biggest benefits include higher resolution rates and faster customer support. Businesses can reduce unnecessary escalations, handle larger conversation volumes, and free human representatives to focus on complex issues.
Quiq customers have achieved measurable results with agentic AI. Brinks Home reported a 67% reduction in cost per customer contact, while a major airline achieved a 40% automated resolution rate.
Agentic workflows require safeguards and human oversight. Inaccurate data, unreliable integrations, and unauthorized actions can create problems. Businesses need clear permissions, regular testing, and escalation rules.
Successful implementation starts with a specific use case and measurable goals. With Quiq’s AI Studio, businesses can build and orchestrate AI agents, connect existing systems, and expand automation as performance improves.

What are agentic AI workflows?

Agentic AI workflows are processes in which AI agents independently make decisions, use tools, and complete tasks to achieve a specific goal. Unlike traditional automation, which follows predefined rules, agentic workflows can adapt their actions based on real-time information, changing conditions, and the results of previous steps.

These workflows typically use large language models (LLMs), APIs, and external data sources to understand requests and determine what actions to take next.

For example, in customer service, an agentic AI workflow can handle a refund request by checking the customer’s order history, verifying refund eligibility, processing the refund through an integrated system, and confirming the outcome with the customer.

If the request falls outside the company’s policies or requires additional approval, the AI agent can escalate it to a human representative.

How agentic AI workflows Work

Agentic AI workflows represent a new frontier in how generative AI is applied. Rather than focusing solely on content creation, these systems are designed to reason, make decisions, and take action based on goals and context, what the Forbes Tech Council describes as “the next frontier: in applying GenAI to real-world problem-solving.

Agentic AI workflows refer to a new generation of AI systems that go beyond pre-programmed logic. These workflows involve AI agents that perform complex tasks by:

Breaking goals into sub-tasks
Making decisions based on context
Iterating through tasks using real-time feedback

With traditional automation, you define what should happen and build the system to follow that exact path. Agentic workflows, on the other hand, allow AI agents to choose from a set of available tools or actions and determine the best next step based on the situation using statistical analysis. That ability to choose and to adapt is what makes them truly agentic.

That ability to make context-driven decisions enables AI agents to engage more naturally with customers, even when the conversation goes off-script. It’s about giving them a curated set of tools and letting them determine how best to move forward within defined boundaries.

Agentic AI workflows vs. traditional automation

Traditional automation follows predefined rules to complete tasks. It works well for predictable processes, such as sending order confirmations or routing support tickets based on specific keywords. However, when a situation falls outside the programmed rules, the workflow typically requires human intervention.

Agentic AI workflows can handle more complex situations because AI agents can interpret context, select appropriate actions, and adjust their decisions as new information becomes available. Rather than following a fixed sequence of steps, agents can determine which tools and processes are needed to achieve a specific goal.

Here’s how the two approaches compare:

	Traditional automation	Agentic AI workflows
Decision making	Follows predefined rules and conditions	Makes contextual decisions within established boundaries
Workflow structure	Uses fixed sequences and predetermined paths	Adjusts task sequences based on context and outcomes
Handling exceptions	Requires predefined exception rules or human intervention	Can evaluate unexpected situations and determine appropriate next steps
Tool usage	Executes predefined actions through connected systems	Selects and uses approved tools based on the task
Adaptability	Requires changes to rules or configurations	Can adapt to new situations without requiring a predefined path for every scenario
Human involvement	Often requires intervention when existing rules cannot handle a request	Can complete complex tasks independently and escalate when necessary
Best use cases	Repetitive tasks with predictable outcomes	Complex processes involving multiple decisions and changing information

For example, a traditional customer service workflow might automatically send a return policy when a customer requests a refund. If the customer has an unusual situation, such as a damaged item purchased during a promotion, the system may need to transfer the conversation to a human agent.

An agentic AI workflow could retrieve the customer’s purchase history, check the applicable return policy, evaluate the circumstances, and determine whether the refund can be approved. If authorized, it could process the refund through an integrated system without human assistance.

Traditional automation still has an important role in business operations. In fact, agentic workflows often rely on conventional automation to execute predictable actions. The main advantage of agentic AI is its ability to coordinate those actions and handle situations that require contextual reasoning.

Example of an Agentic AI Workflow

Quiq implemented an agentic AI workflow for a cosmetic services provider. The challenge? Customers entering the chat primarily wanted pricing, while the brand prioritized collecting contact information.

Traditional bots struggled with this tug-of-war. Quiq’s agentic workflow offered a better solution:

Customer asks for pricing
AI agent provides a broad estimate and requests contact details
Customer repeats request
AI Agent gives more specific pricing and again requests information

Each time, the AI agent reevaluates the context, determines how much to disclose, and how to nudge the conversation toward scheduling a consultation, all while honoring business rules. Another example comes from a major airline using Quiq to manage both voice and digital conversations. Their AI agent can dynamically hand off to humans, escalate when needed, and make decisions on whether to collect additional details or move forward. This reduces frustrating loops and enables smoother experiences.

Key components of agentic AI workflows

Agentic AI workflows rely on several components working together to understand goals, make decisions, and execute tasks. Each component serves a specific purpose, from interpreting customer requests to connecting with external systems and determining when human intervention is necessary.

Here are the core components and how they work in practice.

1. Autonomous AI agents

Autonomous agents are responsible for interpreting information, making decisions, and taking actions within established boundaries. Unlike traditional chatbots that follow scripted conversations, these intelligent agents can decide which steps to take based on the customer’s request and available information.

For example, an AI agent handling a delivery complaint might check the order status, review shipping policies, and determine whether the customer qualifies for a replacement. It can complete approved actions without requiring a support representative to intervene.

Businesses can also deploy agents with specific responsibilities, such as order management or technical support, depending on the complexity of their operations.

2. Goal setting and task decomposition

Every agentic workflow needs a clearly defined objective. The system then breaks that objective into smaller tasks that agents can complete individually or coordinate across multiple systems.

Consider a customer who wants to change a flight reservation. The workflow might involve:

Identifying the customer and retrieving their booking.
Checking available flights and applicable fare rules.
Calculating any additional charges.
Requesting customer confirmation before making changes.
Updating the reservation and sending confirmation.

The agent can adjust this sequence when necessary. If the requested flight is unavailable, for example, it can search for alternatives instead of ending the conversation.

3. AI orchestration and coordination

An orchestration layer manages how agents interact with one another and with external tools. It determines which agent should handle a task, what information needs to be shared, and when the workflow should move to the next step.

This becomes especially important in systems with multiple specialized agents working toward the same objective.

For example, an ecommerce company might use one agent to answer product questions and another to handle shipping requests. If a customer asks whether an existing order can be changed, the orchestration layer can coordinate the necessary actions without forcing the customer to start a new conversation.

At Quiq, our AI Studio provides orchestration capabilities for building and managing agentic experiences across customer interactions.

4. Tool calling and system integrations

AI agents need access to business systems to complete tasks beyond answering questions. Tool calling allows agents to interact with approved APIs, retrieve information, and execute actions in connected applications.

Depending on the workflow, agents might access:

CRM platforms to retrieve customer information and interaction history.
Order management systems to check purchases, shipping updates, and returns.
Payment systems to initiate authorized refunds or billing adjustments.
Knowledge bases to retrieve approved policies and product information.

For example, an agent handling a refund request can use an API to verify the purchase, check eligibility, and initiate the transaction if company policies permit it.

Access permissions should restrict which information agents can retrieve and which actions they can perform.

5. Memory and contextual understanding

Agentic workflows use context to interpret customer requests and maintain continuity throughout an interaction. This includes information provided earlier in the conversation, relevant customer records, and results returned by connected tools.

Suppose a customer contacts an airline about a delayed flight and later asks, “Can you move me to the next available one?”

An agent with access to the conversation history can understand that the customer wants to rebook the delayed flight. It can retrieve the original reservation and search for available alternatives without asking the customer to repeat information.

Some systems also support persistent memory across interactions, subject to privacy policies and data retention settings.

6. Feedback loops and continuous improvement

Feedback loops allow agents to evaluate the results of their actions and adjust their next steps during a workflow.

For example, if an agent attempts to retrieve an order but the system returns no matching records, it can request additional information from the customer and try again.

Longer-term improvement requires evaluation and oversight. Agents don’t automatically become more accurate simply because they handle more conversations.

At Quiq, we review real customer interactions to identify problems, update Process Guides, refine agent instructions, and improve orchestration. This helps us address recurring failures and improve performance based on observed results.

7. Guardrails and human oversight

Autonomous decision-making requires clear limits. Guardrails define which actions agents are authorized to perform, what information they can access, and which situations require human approval.

For example, an AI agent might be permitted to process refunds up to a specified amount but must request approval for larger transactions.

Other safeguards can include:

Customer identity verification before accessing sensitive account information.
Restrictions on modifying payment details or account permissions.
Escalation rules for disputes and requests outside approved policies.
Testing and monitoring to identify incorrect responses or unauthorized actions.

These controls allow businesses to give agents more responsibility while retaining oversight of consequential decisions.

With Quiq’s AI Studio, businesses can configure agent instructions, connect approved tools, and establish human oversight within their customer service workflows.

Types of AI agent workflows

Agentic workflows aren’t one-size-fits-all. They come in various forms, depending on the use case, business goals, and the level of decision-making required. Some common types include:

Conversational AI Agents: These agents handle interactions in voice or chat. Unlike scripted bots, they understand context, manage multi-turn conversations, and adjust based on what the customer says, even if they go off-topic.
Multi-Agent Systems: In more complex settings, multiple AI agents—each an expert in a specific business domain—work together to solve a problem. For example, a “Retail Agent” could handle a customer’s initial product questions, then seamlessly hand off the conversation to a “Logistics Agent” to track the package and adjust the delivery. Together, they collaborate in real time to reach a shared goal that spans different business functions. However, for a Logistics Agent to provide truly reliable, minute-by-minute updates, it must be fed a continuous stream of ‘ground truth’ data from sophisticated asset tracking software. This ensures the AI’s autonomous decisions, such as rerouting or notifying a customer of a delay, are grounded in the actual, real-time status of the fleet.
Robotic Process Automation with Agency: RPA has traditionally followed static rules, but when infused with agentic logic, it becomes much more responsive. These agents determine when and how to execute processes based on the current context, rather than just a script.

Benefits of agentic AI workflows

Agentic AI refers to systems that can independently evaluate situations, make decisions, and take action to achieve defined goals. In customer service, this means AI agents can resolve requests with minimal human intervention, even when conversations involve multiple steps or unexpected questions.

Here are the main benefits businesses can expect from agentic AI workflows.

1. Higher customer resolution rates

Traditional chatbots often answer basic questions but struggle when customers need something done. An agentic workflow can go further by accessing business systems and completing authorized actions.

For example, when a customer contacts an ecommerce retailer about a missing package, an AI agent can:

Retrieve the customer’s order and shipping information.
Check the carrier’s tracking status.
Determine whether the order qualifies for a replacement.
Submit a replacement request through the order management system.
Confirm the next steps with the customer.

The customer gets a resolution without being transferred between departments.

Businesses can measure this benefit through autonomous resolution rates, repeat contact rates, and the percentage of conversations requiring human escalation.

2. Faster resolution of complex customer issues

Agent reasoning allows AI agents to choose appropriate actions based on the customer’s situation rather than following a fixed sequence.

Consider an airline customer whose connecting flight has been canceled. The agent can check available alternatives, review the customer’s fare restrictions, and offer eligible rebooking options.

If the customer rejects the first option, the agent can evaluate alternatives without restarting the conversation.

This reduces unnecessary exchanges and helps customers complete requests faster.

To measure improvements, track average resolution time and the number of interactions required to resolve each issue.

3. More consistent customer service across channels

Customers frequently switch between messaging platforms, websites, and phone calls. When these channels operate independently, customers may have to repeat information or explain their problems multiple times.

Agentic AI workflows can use shared customer records and conversation context to maintain continuity across supported channels.

For example, a customer might begin troubleshooting a device through website chat and later call customer support. If both channels have access to the same interaction history, the AI agent can continue from the previous troubleshooting steps.

This can improve first contact resolution and reduce repeat questions, provided the underlying systems share accurate information.

4. Better use of human support teams

Customer service representatives often spend substantial time handling routine tasks such as checking order statuses, updating contact information, and explaining account policies.

Agentic workflows can complete many of these requests independently, allowing human representatives to focus on situations requiring judgment or customer reassurance.

Businesses can configure custom agents for specific responsibilities. One might handle order modifications, while other agents manage technical troubleshooting or billing requests.

A single agent may be sufficient for simpler processes. More complicated customer journeys may benefit from coordinated agents with separate responsibilities.

The goal is to reduce unnecessary escalations without preventing customers from reaching a human when they need one.

5. More efficient operations during demand spikes

Customer demand rarely stays consistent. Retailers experience seasonal shopping peaks, airlines handle sudden increases in requests during travel disruptions, and service providers receive large volumes of similar inquiries after outages.

Agentic AI workflows can help businesses accommodate these changes without creating a new scripted workflow for every possible situation.

For example, when hundreds of customers contact an airline about canceled flights, AI agents can retrieve booking information and present available alternatives simultaneously.

Businesses still need sufficient infrastructure capacity and reliable system integrations. However, they can expand the number of supported conversations without increasing staffing at the same rate.

Useful metrics include cost per resolved contact, autonomous resolution rate, and the percentage of requests completed during peak periods.

6. More personalized customer interactions

Agentic workflows can use information from customer profiles and previous conversations to provide responses relevant to each person’s circumstances.

Suppose a customer contacts a subscription business about an unexpected charge. Instead of providing a generic billing explanation, the agent can retrieve the customer’s subscription details and identify the transaction in question.

If the customer recently changed plans, the agent can explain how the change affected their bill and offer appropriate next steps.

This allows businesses to deliver more relevant support while keeping decisions within established policies.

The quality of these interactions can be measured through customer satisfaction scores, repeat contacts, and successful issue resolution.

Challenges of agentic AI workflows

Giving AI agents more autonomy introduces operational risks. Agents need accurate information, reliable integrations, and clear boundaries to accomplish tasks without creating new problems for customers.

Understanding these challenges before deployment helps businesses build workflows that remain dependable as their complexity increases.

1. Inaccurate or incomplete customer data

AI agents depend on the information available through connected systems. If customer records are outdated or important details are missing, an agent may make incorrect decisions even when its reasoning process appears logical.

For example, an agent might approve a replacement based on an outdated delivery status, even though the package was delivered earlier that day.

To reduce this risk, businesses should:

Connect agents to authoritative data sources rather than relying on outdated copies.
Verify important information immediately before executing consequential actions.
Define what the agent should do when records conflict or required information is unavailable.
Test workflows against incomplete records and unexpected system responses.

Agents should be able to acknowledge uncertainty and request clarification rather than making assumptions.

2. Integrating agents with existing business systems

An agent can understand a customer’s request perfectly and still fail to resolve it if it cannot access the necessary tools.

For example, an AI agent might identify that a customer qualifies for a refund but be unable to submit the transaction because the payment system doesn’t provide the required API access.

Integration challenges become more complicated when agents need information from multiple systems.

Before deployment, businesses should identify which systems each workflow requires and verify that the necessary operations are supported.

Start with read only access where possible, then introduce permissions for actions such as updating orders or processing refunds after appropriate testing.

Also, define how agents should respond when an API becomes unavailable or returns an error. Retrying an unsuccessful request may be appropriate, but repeating a payment transaction without checking its status could create duplicate charges.

3. Unpredictable AI decisions and model behavior

Unlike traditional software built around deterministic rules, large language models can produce different responses to similar inputs.

This variability becomes more consequential when agents can take action.

For example, an agent might interpret an ambiguous customer message as permission to cancel an order. Another agent might ask for confirmation before proceeding.

Businesses need explicit instructions governing sensitive actions, supported by technical restrictions that prevent unauthorized operations.

Testing should include ambiguous requests, conflicting customer information, and attempts to persuade agents to ignore business policies.

Teams should also evaluate workflow performance after changing models, prompts, or connected tools.

4. Maintaining control over autonomous actions

As agents gain access to more business systems, organizations need to determine which decisions they can make independently.

An agent that can answer product questions presents relatively little operational risk. One that can issue refunds or modify customer accounts requires stronger controls.

A practical governance framework should define:

Which tools and customer records each agent can access.
Maximum transaction amounts and other limits on authorized actions.
Situations that require explicit customer confirmation.
Conditions that trigger human approval or escalation.

For example, an ecommerce agent could be authorized to issue refunds below a specified threshold while routing larger requests to a supervisor.

At Quiq, we use Process Guides and layered safeguards to control agent behavior. These help agents follow business policies while allowing them to respond flexibly within approved boundaries.

5. Coordinating multiple agents

Multi-agent workflows introduce additional complexity because specialized agents need to exchange information and coordinate their actions.

Imagine a customer asking to change a delivery address after placing an order. An order management agent might verify whether the change is permitted, while a logistics agent checks whether the package has already shipped.

If those agents operate on outdated information or execute actions independently, they could provide conflicting answers or attempt incompatible changes.

Businesses should define ownership of each task and specify how information moves between agents.

A central orchestration layer can coordinate these interactions, but the workflow still needs rules for handling failed handoffs and conflicting results.

Avoid introducing multiple agents when a single agent with approved tools can complete the same process reliably.

6. Testing and monitoring agent performance

Traditional automation can often be tested against a predictable set of inputs and outputs. Agentic workflows require broader testing because customers can express similar requests in countless ways.

A refund workflow, for example, should be tested against situations such as:

Customers requesting refunds for orders they cannot verify.
Purchases that fall outside the return window.
Partial refunds involving promotional discounts.
Requests that combine a refund with an address or account change.

Testing should confirm that agents choose appropriate actions and execute them correctly.

After deployment, businesses should monitor autonomous resolution rates, escalation frequency, incorrect actions, and customer satisfaction.

Our approach at Quiq includes reviewing real customer interactions and refining Process Guides based on observed failures. Regular evaluation helps identify problems that might not appear during initial testing.

7. Balancing automation with human oversight

Not every customer interaction should be handled autonomously.

Sensitive account disputes and unusual financial requests may require human judgment, even when an agent has enough information to recommend an action.

An effective workflow needs clear escalation criteria and a reliable way to transfer the conversation without losing context.

For example, an agent handling an insurance claim might collect the customer’s information and explain the next steps but refer coverage disputes to a qualified representative.

Human oversight should also extend beyond individual escalations. Teams need to review unsuccessful conversations and identify recurring issues that require changes to agent instructions or system permissions.

The objective is to allow agents to operate independently where they have sufficient information and authority, while maintaining human control over decisions that carry greater risk.

How to implement agentic AI workflows

Implementing agentic AI workflows starts with identifying a process where autonomous decision making can improve customer outcomes. Rather than deploying agents across every department immediately, businesses should begin with a specific use case, establish clear performance targets, and gradually expand the agent’s responsibilities.

Here’s how we recommend approaching implementation at Quiq.

1. Identify a specific problem to solve

Start by identifying a customer interaction that requires multiple steps or frequently results in human escalation.

For example, an ecommerce retailer might receive thousands of requests about delayed orders. Instead of building an agent that handles every possible customer question, the business could start with a workflow dedicated to resolving delivery issues.

Document how the process currently works, including the systems employees access and the decisions they make.

Prioritize workflows with sufficient request volume, clear business rules, and measurable outcomes.

2. Define goals and success metrics

Before deploying an agent, establish what a successful interaction should look like.

For a delivery support workflow, the primary objective might be to resolve customer inquiries without transferring them to a human representative.

Relevant performance indicators include:

Autonomous resolution rate, measuring the percentage of requests completed without human assistance.
Average resolution time, showing how quickly customers receive a final answer or completed action.
Escalation rate, identifying how often agents require human intervention.
Customer satisfaction, measuring whether customers are satisfied with the outcome.

Establish baseline measurements before introducing AI solutions so you can compare performance after deployment.

You should also define acceptable error rates and conditions that require the workflow to be paused or reviewed.

3. Design the workflow and establish human oversight

Map out the decisions an agent needs to make, the actions it can perform, and the circumstances that require human involvement.

At Quiq, we use Process Guides to define how agents should approach customer interactions. These provide instructions that guide agent behavior without requiring a rigid script for every possible conversation.

For example, a refund workflow could follow these rules:

Verify the customer’s identity and retrieve the relevant purchase.
Check the order against the company’s refund policy.
Process eligible refunds within an approved transaction limit.
Request human approval when the amount exceeds that limit.
Escalate disputes or requests that fall outside established policies.

Human oversight should be built into the workflow from the beginning. Define escalation triggers, approval requirements, and what information the agent must pass to a human representative.

This prevents customers from having to repeat their requests when an escalation occurs.

4. Connect agents to reliable data sources and business tools

Agents need accurate information and appropriate tool access to complete tasks.

Start by identifying which systems the agent must interact with. These might include your CRM, order management platform, payment processor, or internal knowledge base.

Use APIs to connect agents to business applications and authorize specific operations.

For example, a retailer could connect its customer service agent to supply chain management systems to check inventory availability, retrieve shipment updates, and determine whether replacement products can be dispatched.

For knowledge retrieval, consider retrieval augmented generation (RAG). This approach allows agents to retrieve relevant information from approved company documents before generating responses.

An agent could use RAG to retrieve the latest return policy while relying on an API to verify an actual customer’s purchase.

However, information retrieval and transaction execution should have separate permissions. An agent that can read refund policies shouldn’t automatically receive permission to issue refunds.

Restrict tool access according to each agent’s responsibilities, and define how the workflow should respond when information is missing or a connected system becomes unavailable.

5. Test workflows against realistic customer scenarios

Before deploying agents into production, test how they behave when conversations deviate from the expected path.

Create simulations based on actual customer interactions, including situations that have historically required human intervention.

For example, test what happens when:

A customer requests a refund without providing sufficient identifying information.
The order management system returns conflicting information.
A customer attempts to persuade the agent to ignore refund restrictions.
An external API stops responding during a transaction.
A customer changes their request after the agent has already started processing it.

Each test should have a defined expected outcome. Use assertions to verify that the agent selects appropriate tools, respects authorization limits, and escalates when necessary.

Combine automated testing with human reviews of conversation quality and decision accuracy.

At Quiq, our AI Studio supports conversation simulations and testing so teams can identify failures before customers encounter them.

6. Deploy gradually and improve based on actual performance

Start with a limited deployment rather than immediately routing every eligible customer interaction to an AI agent.

You might initially allow the agent to handle order status inquiries while requiring human approval for refunds and account modifications.

Monitor the performance metrics established earlier and review unsuccessful conversations to identify recurring problems.

Pay particular attention to situations where agents:

Select incorrect tools or retrieve outdated information.
Escalate requests they should have been able to resolve.
Execute actions that don’t match the customer’s intent.
Fail to recognize when human intervention is necessary.

Use these findings to refine Process Guides, adjust permissions, and improve the workflow’s decision logic.

As performance improves, gradually expand the agent’s responsibilities and introduce additional use cases.

With our AI Studio, businesses can build and orchestrate agentic workflows while maintaining control over the models and systems they use. The platform supports any LLM or custom AI model, allowing teams to select the technology that meets their requirements without rebuilding their customer service operations around a single model provider.

Agentic workflow examples across industries

Agentic AI workflows are already helping businesses resolve customer requests and complete transactions that previously required human assistance. The applications vary by industry, but the underlying principle remains the same: AI agents use customer context and connected systems to determine what needs to happen next.

Here are five examples from businesses using Quiq.

1. Customer service: Brinks Home

Home security provider Brinks Home uses Quiq’s agentic AI to help customers make payments, troubleshoot security devices, and schedule service appointments without calling a support representative.

For example, when a customer reports that a security camera has gone offline, the AI agent can retrieve information about the customer’s device, consult troubleshooting documentation, and guide them through the appropriate diagnostic steps. If the issue cannot be resolved remotely, the workflow can arrange a service visit.

The results have been substantial:

67% reduction in cost per customer contact.
Digital transactions increased from 12% to 60%.
Customer contacts requiring live agents decreased by two thirds.
Digital NPS improved from negative 55 to positive 50.

These workflows allow customers to resolve common problems through messaging while human representatives handle more complex issues.

2. Retail and ecommerce: National furniture retailer

Agentic workflows can help retailers manage customer requests throughout the purchasing journey, from product discovery to delivery scheduling.

One national furniture retailer replaced its traditional chatbot with a Quiq AI agent connected to its product catalog and order management systems.

The agent can recommend products based on customer preferences, retrieve order information, and assist with delivery scheduling. It also identifies situations where a customer would benefit from speaking with a sales representative.

After deployment, the retailer reported:

33% fewer escalations to human support representatives.
76% of support conversations handled through self service.
10% of daily sales attributed to chat on a record sales day.

The retailer now uses the same AI agent across web chat, SMS, and Apple Messages for Business, maintaining customer context throughout the interaction.

3. Airlines: National airline

Airlines handle complicated customer requests involving reservations, flight disruptions, and changing travel plans. Traditional chatbots often struggle because customers rarely follow a predictable conversation path.

A major North American airline partnered with Quiq to replace its rigid chatbot with an agentic AI system that supports voice and digital customer interactions.

The AI agent can interpret complex requests, retrieve reservation information, and determine whether it can complete the next step or needs human assistance.

When escalation is necessary, it transfers the conversation context so the representative can continue without asking the customer to explain everything again.

The airline’s results include:

More than 7 million conversations handled annually.
A 40% automated resolution rate across supported channels.
16% shorter average conversation times.
20% shorter conversations for escalated inquiries.

The workflow illustrates how agentic AI can coordinate customer support across multiple channels while keeping human representatives involved when necessary.

4. Healthcare: Medical device manufacturer

Healthcare companies can use agentic workflows to handle administrative inquiries while maintaining human oversight over requests that require additional attention.

A medical device manufacturer implemented Quiq’s Email AI agent to manage customer questions about orders and shipments.

Previously, employees had to research every inquiry and write individual responses, which created support backlogs and delayed responses.

The new workflow connects directly to the company’s Oracle environment. When an order inquiry arrives, the AI agent retrieves the relevant information and determines how to proceed.

If it has enough information, it responds to the customer and closes the incident. When the request requires human review, the agent prepares a draft response or transfers the inquiry to a representative.

The manufacturer achieved an 85% resolution rate among emails answered directly by the AI agent, with no follow up required from the customer. It also improved customer satisfaction and response times.

5. Financial services: Lease to own financing provider

Financial services workflows often involve sensitive customer information and transactions that must follow established authorization rules.

A national lease to own financing company implemented Quiq’s agentic AI to help customers complete financing applications, answer account questions, and make payments through chat.

For example, when a customer wants to make a lease payment, the agent can authenticate the customer, retrieve account information, present available payment options, and process the transaction.

The agent also assists applicants who encounter difficulties during the financing process, explaining requirements and answering questions that might otherwise lead them to abandon their applications.

The company reported:

Approximately 60% of customer conversations resolved automatically.
Between 3,500 and 4,000 payments processed through chat monthly.
Approximately $56,000 in monthly savings from avoided support calls.
More than $1 million in additional annualized revenue attributed to improved application completion.

By connecting AI agents to existing financial systems, the company can complete routine transactions while directing unusual disputes and requests requiring human judgment to its support team.

Build agentic AI workflows that resolve customer issues with Quiq

Agentic AI workflows have the potential to transform customer service, but their value depends on what they can actually accomplish. Answering questions is useful. Resolving customer issues, completing transactions, and reducing unnecessary escalations are where businesses can see measurable improvements.

With Quiq’s AI Studio, you can build, deploy, and orchestrate AI agents that handle complex customer interactions across channels. Connect agents to your existing business systems, define the actions they’re authorized to take, and maintain control through Process Guides and human oversight.

Whether you’re looking to automate routine support requests or manage complex customer journeys, Quiq gives you the tools to move from basic conversational AI to autonomous customer service.

Book a demo with Quiq to see how agentic AI workflows can improve resolution rates and deliver better customer experiences.

Frequently asked questions about agentic AI workflows

What is an example of an agentic AI workflow?

A common example is an AI agent handling a customer refund request. The agent retrieves the customer’s order history, checks the applicable return policy, verifies eligibility, and processes the refund through an integrated payment system. If the request requires additional approval, it transfers the conversation to a human representative with the relevant context.

What is the difference between an AI agent and an AI agentic workflow?

An AI agent is an individual system capable of reasoning, making decisions, and taking actions to accomplish a goal. An agentic workflow is the broader process that coordinates those actions, including tool usage, decision points, and interactions with other agents or human employees. A workflow can involve a single agent or multiple specialized agents.

Can agentic AI workflows operate without human supervision?

Agentic AI workflows can complete many tasks with minimal human intervention, provided they have access to the necessary information and approved tools. However, businesses should establish guardrails that restrict sensitive actions and define when human approval is required. High risk decisions, such as large financial transactions or changes to sensitive customer records, may require additional oversight.

What are the limitations of agentic AI workflows?

Agentic AI workflows depend on accurate data, reliable integrations, and clearly defined permissions. They can make incorrect decisions when customer information is incomplete, misunderstand ambiguous requests, or fail when connected systems become unavailable. Regular testing, performance monitoring, and human oversight help businesses identify these issues and prevent errors from affecting customers.