What is an AI agent?
Over the past few years, we’ve enjoyed the gains in productivity and innovation unleashed by generative AI. While foundation models like ChatGPT are capable of creating content and insights when prompted—the next generation of AI is here: the AI agent.
AI agents can autonomously complete complex tasks that once required human effort. They’re an exciting advancement in AI technology, upgrading the largely reactive capabilities of GenAI with a powerful layer of proactive problem solving.
In short, AI agents can do more than assist—they can do the work for you. They promise to explode the ROI potential of AI, streamlining operations and serving as end-to-end solutions that can achieve business objectives without assistance.
So what exactly is an AI agent? And where do they fit into the rapidly evolving landscape of artificial intelligence?
What is an AI agent?
An AI agent is a software tool that can interact with its environment, gather data, and use that data to achieve predefined goals. Powered by advanced machine learning and natural language processing, AI agents can reason, plan, and adapt in real time to fulfill complex tasks.
Unlike traditional AI, AI agents can handle anything from simple customer queries to complex multi-step issues without human input. They do this by taking initiative within the broader digital ecosystem, making decisions, and integrating external data and tools to solve problems in creative ways. What's more, AI agents can continuously improve their performance through self-learning.
How do AI agents work?
At the core of AI agents are large language models (LLMs). This enables the AI agent to receive instructions from non-technical teams, interpret its environment, and generate meaningful responses to users.
AI agents take a four-step approach to problem solving:
Perception: AI agents gather data from various sources like customer interactions, databases, web searches, and social media. Capable of perceiving their environment, they can identify relevant objects and process data in real-time to provide timely, tailored interactions to users.
Reasoning: To make a decision, AI agents analyze the collected data using advanced machine learning models. For instance, based on past interactions and current context, they can make personalized product recommendations to customers. Using techniques like retrieval-augmented generation (RAG), the agent can access proprietary data sources and deliver accurate, relevant outputs
Action: AI agents integrate with external tools and data to execute tasks based on the plans it has generated. Boundaries can be set to ensure tasks are completed correctly. For instance, an AI agent for customer service may be able to handle tickets up to a certain priority, above which the AI agent will hand off the ticket to a human agent.
Learning: AI agents learn from each interaction, updating their knowledge base (or memory) to improve their accuracy and effectiveness. By feeding interaction data back into their model, AI agents can seamlessly adapt to evolving customer expectations and personalize interactions with returning users.
Unlike traditional AI that requires human input for certain tasks, AI agents can handle a range of tasks autonomously, such as providing customer support to recommending products to automating follow-up processes. This helps businesses to increase efficiency and productivity, while tailoring customer interactions based on real-time context to improve the customer experience.
Let’s take a deeper look at AI agents in action.
AI agents use reason to solve problems
Once humans set the goal, the AI agent chooses the best way to reach the business objective.
For example, a customer service AI agent would be assigned the goal to resolve customer queries. The agent will automatically ask the customer questions, refer to internal documents, and respond with a solution. Based on the customer’s responses, and predefined guardrails, it decides whether to try and resolve the query or escalate to a human agent.
When faced with a complex problem, the agent will create a plan that breaks the task into manageable sub-tasks. This allows it to support multi-step workflows that involve too many variables for previous versions of AI.
In the course of planning, the AI agent can also recognize gaps in its information and capabilities. This capacity for perception enables the agent to adapt to unforeseen challenges, and call on outside tools and data to solve unique problems.
Tool calling: From monolithic to compound AI
Like an evolutionary leap in computer intelligence, AI agents are capable of using existing tools and external data to achieve the goals set for them.
Historically, generative AI has been constrained by the limitations of its monolithic models—which can only operate using the static data it was trained on, as well as its predefined capabilities. In other words, despite the fact that ChatGPT is trained on vast swathes of internet data, if asked about your company’s customer service protocols, this monolithic LLM couldn’t access the required data unless pre-trained. It's a closed system.
AI agents change this. They’re compound AI systems, meaning they can dynamically retrieve outside data, process it, and integrate it into their model to solve a problem. They can also integrate a diverse set of tools to reach an objective, including APIs, software systems, and other AI agents.
For example, to troubleshoot a complex technical issue for a customer, an AI agent could:
Query the company's database for prior interaction history and device logs
Use an API to request real-time diagnostics from diverse sources
Analyze the collected data to identify potential causes and suggest solutions
This blend of tool calling, real-time analysis, and modularity allows AI agents to handle complex workflows far beyond the capabilities of traditional AI.
AI agents learn and improve continuously
AI agents learn from each interaction, refining their reasoning to continuously improve their accuracy and performance. They do this by storing interaction data in their knowledge base (memory), as well as receiving feedback from other agents or human managers.
For instance, an AI agent could be deployed on an ecommerce site to provide support and tailored product recommendations. As it interacts with customers, the agent tracks their behavior, preferences, obstacles, and successful solutions in search of trends, then stores this learned information in its memory.
This data feedback loop ensures that the AI agent remains effective and relevant as customer expectations or the business environment changes. It also enables it to respond to returning customers with full historical context—for deep personalization that improves conversions and customer experience.
The agent’s own iterative process of self-improvement can be enhanced with feedback from other AI agents, helping to reduce the time humans are needed to provide correction.
7 benefits of AI agents
AI agents are a scalable way to improve your business operations and customer experience.
24/7 availability: Available around the clock, AI agents ensure that customers get their inquiries addressed without delay, regardless of business hours. This always-on availability helps a business to meet customer expectations for self-service and improve loyalty.
Improved efficiency: Capable of handling multiple customer inquiries at once, AI agents reduce response times and increase the efficiency of customer service operations. This helps a business to handle higher ticket volumes without compromising service quality.
Reduced costs: By automating up to 80% of routine inquiries, AI agents reduce the need for large customer service teams. Integrating AI agents frees up human agents to focus on the most sensitive issues while helping to preventing employee burnout.
Enhanced customer experience: AI agents provide customers with immediate, tailored responses in the exact moment they want to improve engagement, satisfaction, and loyalty. Because they use stored data to personalize interactions and learn over time, AI agents reliably give accurate, contextual answers that elevate customer satisfaction scores.
Personalized solutions: AI agents can analyze both current behavior and past interactions to recommend tailored solutions, products, or services to each customer.
Data-driven insights: AI agents can gather, process, and integrate vast amounts of customer data in real-time. These insights help to enrich customer profiles, proactively identify issues, detect patterns in customer sentiment, and improve decision-making. AI agents also store these insights in their memory to drive continuous improvement.
Scalability: If you choose a provider with the right infrastructure, AI agents can be easily scaled to handle any number of customer inquiries. This makes them ideal for businesses that want to grow without compromising their quality of service.
"AI agents can automate repetitive tasks that previously required human intervention, such as customer service, supply chain management, and IT operations. What sets the technology apart is its ability to adapt to changing conditions and handle unexpected inputs without manual oversight."
Rodrigo Madanes, AI Officer at EY Consulting
Examples of AI agents
Here’s some examples of AI agents that show how flexible this technology can be.
AI agents for customer service
AI agents promise to revolutionize the customer service experience. By analyzing real-time context along with interaction history, AI agents can turn routine interactions into highly personalized experiences that require less manual escalation—improving agent efficiency and digital customer experience.
Say a telecommunications provider uses an AI agent to troubleshoot connectivity issues. The agent guides users through the steps, runs real-time diagnostics, and schedules a technician visit if necessary, all without human intervention.
Key benefits:
Proactive support: Based on behavioral patterns and historical interactions, agents can anticipate customer needs when implemented on websites, mobile apps, and support channels. By suggesting solutions before a problem arises, they can defuse issues before they impact the customer experience.
Context-rich escalation: Agents collect and synthesize all previous engagement data with real-time inputs before escalating, ensuring faster and more seamless resolutions.
AI agents for ecommerce
Ecommerce retailers could use AI agents to improve the customer experience and personalize recommendations while streamlining backend operations. For example, AI agents can make hyper-personalized recommendations on ecommerce sites. Since agents track user activity and preferences in their memory, they can recommend the perfect item to new and returning customers to reduce returns, drive conversion, and improve customer satisfaction.
Key benefits:
Dynamic inventory management: AI agents analyze sales trends and supplier data to optimize stock levels in real-time, preventing shortages or overstocking.
High-context interactions: Agents analyze customer data with real-time behavior signals to create targeted, tailored experiences wherever customers are for a truly seamless omnichannel experience.
AI agents for healthcare
In healthcare, AI agents go beyond the automated scheduling and triaging of current AI chatbots; they actively improve patient outcomes by personalizing care and streamlining operations. Unlike static AI systems, AI agents adapt to individual patient needs, offering a tailored experience that drives trust and engagement while also improving health outcomes.
For example, a hospital could deploy an AI agent on its mobile app to assist diabetic patients. The agent monitors real-time glucose levels through wearable devices, recommends diet and lifestyle adjustments, and triggers in-app notifications to both caregivers and patients when trends signal potential issues.
Key benefits:
Symptom checkers with dynamic follow-up: AI agents can guide patients through symptoms, pulling from up-to-date medical databases and adapting questions based on user responses.
Personalized chronic care management: Agents track patient data over time, providing reminders for medication, exercise, or dietary changes, and escalating cases to human professionals when necessary.
AI agents for finance
In finance, AI agents promise to elevate customer interactions by blending automation with nuanced decision-making. Unlike rule-based systems, they combine real-time data analysis with predictive modeling to enhance customer support and internal operations.
For example, an investment platform might use an AI agent to provide personalized portfolio recommendations based on live market conditions. The agent adjusts its advice dynamically based on market fluctuations and the user’s evolving financial situation based on a multiplicity of complex factors.
Key benefits:
Real-time fraud detection: AI agents analyze transaction patterns and flag anomalies for review, providing instant feedback to customers and fraud teams.
Hyper-personalized advice: Agents tailor recommendations based on a customer’s transaction history, savings goals, and risk appetite.
AI agents for marketing
AI agents can help connect their entire digital marketing ecosystem into a real-time engine for campaign targeting and deep personalization.
By leveraging customer insights alongside real-time behavior signals, and storing new inputs in memory, the agent can help brands to identify trends, optimize campaigns, and drive conversion with tailored targeting driven by the real-time omnichannel journey of each customer.
Key benefits:
Tailored content creation: Agents for content marketing, copywriting, and design could create tailored content, which a human evaluator would review for brand alignment. These agents would collaborate to iterate and refine outputs and align toward an approach that optimizes the campaign’s impact while minimizing brand risk.
Hyper-targeted campaigns: Agents optimize ad targeting and campaign creation based on market-level data, user engagement trends, platform analytics, and real-time user inputs, improving campaign ROI while integrating insights for data-driven decision-making in the future.
How to deploy an AI agent: 9 tips for success
As you think about deploying AI agents, here are some best practices to keep in mind:
Define your objectives: Start by defining what you want to achieve with your intelligent agent. For example, you might want to provide customer service, streamline operations, or refine code. You can create a specialized agent for each objective, so having a clear objective will ease implementation and help you measure success.
Prepare your data: To function properly, AI agents require access to huge amounts of high-quality business data. This includes customer data, transaction histories, social media, and other relevant information. Ensure you have data collection and management systems in place to feed your AI agents the data they need to provide accurate and relevant outputs.
Choose the right AI agent type: Choose the AI agent type that fits your specific use case. For example, if you want to provide customer support, a reflexive agent will work. However, for more complex tasks, a goal-oriented agent or learning agent will be better at solving problems and adapting to customer needs in the moment.
Integrate with systems and tools: Ensure that your AI agent integrates seamlessly with your CRM, databases, and third-party tools. This integration will allow the AI agent to access relevant data and leverage capabilities of tools in its environment, enabling robust problem solving and the best customer experience.
Train your agent: To be effective, AI agents must be trained on your relevant business data. You can feed your AI agent up-to-date information from customer interactions, call recordings, CRM, internal wikis, and other documentation to guide it in executing tasks and providing support that aligns with your business.
Plan for human oversight: Even though AI agents can handle tasks autonomously, it’s important to establish control mechanisms to validate their outputs for accuracy, relevance, and compliance. Plan for when human intervention (or humans in the loop) will need to step in, creating guardrails around complex problems or sensitive interactions.
Monitor and optimize: Regular monitoring the performance of your AI agents will ensure they give accurate responses that are aligned with business objectives. Gather feedback from users to inform any improvements, and track performance to ensure agents are adapting to customer expectations and performing at their best.
Protect user data and privacy: AI agents handle large volumes of sensitive user data, so be sure to implement robust security and data privacy measures to protect users. This includes compliance with data protection regulations and routine security audits that secure sensitive information and uphold the trust of customers.
Prioritize the user experience: Consider your end user as you build your AI agent. Test its responses thoroughly to ensure they’re timely, accurate, and deliver a positive customer experience.
AI agents are transforming business for the better
As we advance into 2025, AI agents promise to automate many tasks that once required human effort. They're a scalable way to improve operational efficiency and customer experience, delivering more productivity, better decision-making, and a competitive edge in a rapidly evolving market.
Ready to learn how this groundbreaking AI technology can help your business? Explore AI agents by Sendbird or contact sales.
