AI is already a game-changing tool for companies across all sectors, particularly in the areas of customer service and process automation. According to Forrester Research, 75% of clients expect coherent and fast responses from their favorite brands. In this regard, AI agent systems can become the bridge between companies and their audiences. Near-instant first contact, high-quality resolution, and other perks can be within your reach and ensure users will prefer your services to others.
What is left is to learn how AI agents work and acquire the best deployment tactics. Let’s roll into the topic.
What are AI agents?
These act as autonomous agent systems that perceive different types of context and data to achieve the target objectives with minimal friction and downtime. Classic use cases include code creation and debugging in the IT industry, data analytics, and customer support. The main peculiarities of AI agents are as follows:
- Autonomy — independent performance with versatile performance strategies.
- Memory — keeping records of past interactions across chats.
- Action-forward — access to APIs and other systems to interact with in-house and outsourced databases for high-quality task resolution.
- Goal-driven — created for complex troubleshooting based on the company’s data and context interpretation.
Difference from simple rule-based bots
The basic technology for customer support automation is rule-based, while AI agent systems for the company’s goals are the latest know-how. Understanding “What is an AI agent vs. a rule-based bot?” can clarify your vision and help you select the right workflow optimization instrument for your business processes.
Core architecture
Rule-based bots are pretty self-explanatory: their performance depends on predetermined scripts. This makes their work outside of the comfort zone (anything that isn’t defined by if-then logic and decision trees) Mission Impossible to deal with. On the other hand, AI agents are better suited to today’s ever-changing customer support challenges. They operate based on natural language understanding (NLU) and large language models (LLMs).
Language processing
Keyword-forward text analysis is typical for rule-based bots. In comparison, AI agent systems are taught to understand the real user intent behind messages. Their communication style is contextually relevant, which allows them to handle multi-intent inquiries, ambiguous phrasing, etc., with greater effectiveness in mind.
Task completion
Rule-based bots are rigid in what they do. That’s where another tactical perk of using AI agents lies. Such systems support autonomous task execution that follows the company’s policies and meets end-user needs at the same time.
In customer support, AI agents act, not just reply. They can, for example, cancel order upon customer request.
How AI agents work
Although the choice of LLM and custom commands and scripts will define the functional capacity of AI agent systems, the general workflow commonly stays the same. Here is how they handle business processes in practice.
| Phase | What happens | Processing |
| Input reception | Receiving a message from the user | Speech-for-text, NLP, and other technologies to capture the raw input |
| Intent decoding | Text or voice message interpretation, with a focus on the chat’s context and intent | Embeddings, LLMs, etc., to clearly define the message’s meaning |
| Reasoning & response planning | Deciding on the best actions and response methods, with relevant data sourced from reliable knowledge sources | RAG, planning algorithms, and other steps to gather the related details and create a step-by-step action plan |
| Tool selection | Selecting the right instrument for the best troubleshooting (access to the inventory management system to check the product’s availability, for example) | Completing the requested task backend |
| Response generation & delivery | Crafting a natural language response | LLM generation to send the reply to the message recipient on the chosen communication channel (voice, email, live chat) |
AI agent types for businesses
You can categorize AI agent systems based on the business process they are engineered for (Finance, IT, Customer Support, etc.) or considering how they work with data for decision-making goals. The latter includes such classes as:
- Autonomous — designed for prolonged, independent operation without human supervision.
- Hierarchical — with greater rights, tools, and responsibilities for splitting complex tasks into smaller, more manageable pieces for agents that hold lower ranks.
- Conversational — created to mimic natural language interactions and be as versatile and context-relevant as possible.
- Multi-agent — collaboration-forward systems for performing different tasks that will lead to a single desired outcome.
Examples of AI agents in businesses
The best approach is to select AI agent systems that can be tailored to your industry’s specifications — native software for your needs. It’s advantageous for clients (maximum relevance and professionalism accomplished) and businesses, too. According to Deloitte, companies that improve the personalization of AI-empowered solutions are 70% more likely to enhance customer loyalty and around 50% more likely to skyrocket their ROIs.
Customer support
This form of automated helpdesk handles support workflows end-to-end. The range of tasks that can be completed by AI agents shouldn’t be underestimated, from ticket creation to refund processing.
Sales & marketing
In this scenario, such assistants can boost lead qualification and conversion. Aside from instant responses, they can offer demo product check-ups, individual solution recommendations, and more.
Call center
As AI voice bots, such software can drastically reduce workload while ensuring your company’s accessibility around the clock. Aside from FAQs and call management, they can create service bookings and follow up on ticket-related matters.
AI agent in customer support
Customer service AI agents are created to handle the issues that end users may encounter from A to Z, all without direct human involvement. They are configured to independently comprehend a client’s request, decide what needs to be done to address it, and carry out the right actions across business systems for a satisfactory outcome.
Accurate responses
Unlike rule-based bots that fully rely on prerecorded static scripts for communication goals, these assistants can interpret the end-user intent across multiple contexts. This efficiency is possible thanks to a balanced pairing of the brand’s data and LLM reasoning.
From a technical standpoint, one of the key mechanisms is retrieval-augmented generation (RAG). This framework works as a source of directives for AI agents:
- It locates a relevant and credible data source to set up snippets.
- It creates a prompt that combines details from the original inquiry and the retrieved company data.
- It delivers a prompt, which leads to the creation of a highly personalized response.
This way, your business can build trust through data-driven and relevant answers while elevating first-contact resolution rates and minimizing the risk of error.
Timely assistance, even outside of business hours
AI agents are available around the clock, which takes your support service’s performance standards to the next level. Not only does it ensure your accessibility to audiences across multiple time zones, but it also boosts customer satisfaction through near-zero-delay responses. This strategy lets you reduce abandonment rates and maintain the desired service consistency.
Task-forward instruments for business goals
That’s the leading operational difference between AI chatbots and AI agents, their next-gen analog. These instruments can execute a wide range of tasks with a minimal error rate. Their performance doesn’t necessarily have to be restricted to the conversational layer of support service.
While a lot depends on the prescribed workflows and settings behind your AI agents’ technological chassis, the variety of tasks that can be handled by them is more extensive than it might seem at first:
- automated ticket status update and routine;
- order status check-ups for clients;
- real-time order delivery tracking;
- user identity verification, based on KYC protocols;
- proactive messaging, given the target user’s behavior patterns (special bonuses, issue-related notifications, and so on);
- analytical dashboard setup to analyze the team’s performance within a selected timeframe;
- workflow coordination across departments;
- content editing and creation for unique responses or Knowledge Base updates;
- automated feedback collection and analysis;
- custom operations in billing, inventory management, logistics, and other fields, predetermined by the chosen API and CRM integrations.
How to choose an AI agent for customer support
Given that AI agents can be customized to suit your business needs, your journey should begin with defining your primary goals and expectations from this technology. Settings for tools to streamline support, workflow automation, and onboarding won’t be 100% the same. Here are the main parameters to take into account:
- automation depth assessment;
- LLM quality, latency, and other characteristics;
- proven efficiency for omnichannel and multilingual support, both text and AI chatbots if required;
- resolution rate benchmarks;
- context handling capacity (whether it remembers past interactions with end users and across which channels, if yes);
- available customization level;
- real-time latency and response speed;
- deployment and learning curve difficulty;
- continuous learning potential;
- error handling routes, etc.
By comparing a few AI agent systems across key metrics and markets, it will be easier to define which performance ranges are the bare minimum and state-of-the-art assistance.
How to create AI agents in Intelswift
The Intelswift system allows for custom AI agent creation and performance optimization. The experience is straightforward and doesn’t depend on your skill set — no coding knowledge is required to make things work flawlessly on your end.
1. Register and access the dashboard
Start by setting up a profile on the Intelswift website and accessing the main dashboard. This modern AI system is designed for fast onboarding. In comparison with alternatives, its implementation timelines have dropped from months to just a few days.
2. Navigate to AI agents
In the menu, go to the “AI Agents” section and click on the “Create New Agent” command. This is where you initialize your automation layer. Unlike rule-based bots and AI chatbots, this solution enables independent task execution — up to 80% of routine tasks can be solved without human intervention and supervision. Besides, they can be trained to split complex inquiries into micro-assignments and redirect their troubleshooting to the responsible human agents across departments. If desired, you are welcome to build separate AI agents for different functions. It will make this experience clear and purpose-driven.
3. Assign a name and select an LLM
Name your agent and choose a large language model (LLM) to fuel its performance on your site. The model selection determines how the newly created AI agent system will work:
- scalability potential;
- integration compatibility;
- language comprehension skills;
- post-failure operational protocols;
- consistency and coherence in responses;
- instruction following paradigm;
- response speed;
- multilingual capabilities;
- which types of problems can be solved and their maximum difficulty level;
- memory and contextual relevance;
- response quality and relevance, based on the latest brand’s data;
- safety, etc.
The list includes high-performance LLMs from Google, OpenAI, Moonshot, and others. Compare their perks and limitations to gain a well-balanced framework to cater to your goals in terms of quality, latency, etc.
Choose more advanced solutions if your team handles complex workflows on a regular basis. The right call is to balance the solution’s cost and performance, depending on the expected use cases.
4. Choose the AI agent type
You can select from two available categories: text and voice AI bots. This will define the assistant’s main communication style. Click on the multilingual toggle to adhere to your brand’s global reach through AI-empowered, human-like, and fluent communication via your support service.
5. Train the AI agent on company data
Upload your business’s internal documents to “feed” your agent. Sufficient categories include website content, FAQs, PDFs, and so on. This step is of great importance, as it predetermines the real-time quality of your AI agent.
6. Deploy across your website and messaging channels
Once trained, deploy your creation to the target platform and social media like Telegram or WhatsApp. The omnichannel communication strategy will be your competitive perk. It ensures consistent assistance across various touchpoints. That’s how you follow the latest trends in the industry and tailor to end-user expectations. 97% of service consumers await seamless transitions across communication channels, and that’s exactly what Intelswift offers, thanks to its optimization and the shared inbox system, in particular.
7. Optimize the agent’s work and scale it
Configure the project’s default behavior patterns and prompts in the settings. If you aren’t satisfied with the initial outcome, Intelswift lets you change the AI agent’s profile — go to the general settings in the menu. In “AI Actions,” you can specify conditions when the assistant has to transfer the ticket to a human agent and so on.
Final word
Whether you require AI finance agents or AI knowledge management agents, Intelswift can help you create the right software for your business processes. This data-driven analysis proves that such modern software has already moved beyond simple conversations and can accomplish a lot with the right settings and learning protocols.
