Everything You Need to Know About AI Agents in 2026

Growing demand rarely comes with extra headcount. Teams are expected to move faster and still offer that personalised touch, often with the same tools and budgets they had last year.

That pressure is pushing many businesses to rethink how work gets done. We found in our State of IT: AI and App Development report (surveying more than 2,000 IT and development leaders worldwide) that 83% of developers say AI agents are fundamentally changing how organisations operate, while 78% worry their business will fall behind if they don’t move to adopt them.

This is where AI agents like Agentforce come into play. In this article, we’ll explain what AI agents are, how they work inside Salesforce, and what you need to consider to successfully implement them across your business.

• What is an AI agent?
• AI agents compared to other types of AI
• How AI agents work: The four-step cycle
• The main types of AI agents
• What are the benefits of AI agents?
• AI agent use cases for business
• Real-world examples of AI agents in action
• How to build an AI agent
• AI agent risks, challenges, and guardrails
• 8 tips for successfully implementing AI agents
• Deploying AI agents with Agentforce
• The next generation of business technology
• FAQs

An AI agent is an intelligent system that can understand context and take action on behalf of a business, without needing continuous human input.

Unlike tools that respond to a single prompt, AI agents can manage ongoing work across systems and adapt their behaviour based on outcomes. This is a big reason why 85% of developers say AI agents are now central to digital transformation.

Across organisations, AI agents are already being used to qualify leads, update CRM records, personalise campaigns, forecast demand, resolve account issues, and trigger workflows when conditions change.

They rely on machine learning and natural language processing (NLP) to handle a wide range of tasks, from answering simple questions to complex multi-tasking.

While often confused, AI agents and chatbots aren’t the same. Chatbots follow predefined scripts and handle narrow requests. AI agents work within a broader context and can take action in real time.

AI is an umbrella term for a number of different systems that all use data and algorithms to analyse information and generate outputs.

Some of these AIs generate content, some assist humans, and others are designed to act independently. Understanding the difference between the models helps set realistic expectations and can help you avoid deploying the wrong tool for your job.

Here’s an overview of the common types of AI and how they compare to agentic AI.

AI agents work in a cycle. They take in information, decide what the next action should be, act on it, and then learn from its results. Here’s how that looks in practice.

1. Perception: They collect data from CRM records, like customer interactions and transaction history, to understand context, often in real time.
2. Decision-making: Based on everything it’s picked up, the agent figures out the best way to respond. It does this by spotting patterns and weighing up the context.
3. Action: The agent then steps in to get the task done, which could be answering a question, helping with a request, or getting a person involved if the request requires a human touch.
4. Learning: Each interaction helps refine their models. Over time, they adapt to new behaviours, improving speed, accuracy, and relevance.

This cycle allows agents to operate consistently across sales, marketing, and service, helping teams keep up as customer expectations evolve.

AI agents come in many different forms, depending on the agentic architecture that they use as a foundation. Here are eight different types you should know about.

As of 2026, today's agentic systems are typically designed to operate inside live business environments, where they need to interpret information and carry out actions.

Here are the characteristics you can expect from a state-of-the-art AI agent:

• Tool-using agents: Can call APIs, run code, or query databases to retrieve live information and complete actions outside of having conversations.
• Retrieval-centric agents (RAG-powered): Look up information from your company’s own documents and systems so responses are accurate.
• Planner or orchestrator agents: Take a large or unclear request, break it into manageable steps, and decide the order in which those steps should happen.
• Supervisor or watchdog agents: Keep an eye on what other agents are doing, check that results make sense, and step in when something needs human review.
• Multi-agent collaboration: Allows different agents, each with a specific role (for example, HR and finance), to work together on a shared task by passing information between them.

Adopting AI agents offers numerous benefits, transforming how businesses interact with customers and manage their service operations. Here are five benefits you can expect when using an AI agent.

AI agents are available around the clock, ensuring customer inquiries are addressed promptly, regardless of time zones or business hours. This continuous availability helps businesses keep up with their work and meet customer expectations for self-service.

AI agents deliver fast, accurate, and personalised responses, helping your team save time and boost customer satisfaction, all while improving with every interaction. In fact, 42% of organisations using AI say it’s already had a positive impact on customer experience.

One of the biggest challenges for growing businesses is suddenly having a lot more customers to support, without more staff to do so. AI agents can be easily adjusted to handle increased volumes of customer interactions, making them ideal for businesses looking to grow without compromising service quality. As case volume increases, AI agents can be easily adjusted to ensure consistent and reliable support.

AI agents generate valuable data on customer interactions, preferences, and behaviours. Businesses can use this data to gain insights into customer needs and trends, enabling them to make informed decisions and improve their service offerings.

AI agents absorb routine work that would normally require new hires or outsourcing, helping your business manage its costs as demand increases.

AI agents are increasingly used to handle everyday work that would otherwise sit across different software and in inboxes. They are designed to reduce day-to-day admin while fitting into existing ways of working.

Here are some of the popular ways different professions use AI agents.

AI agents are now commonly used to review incoming IT requests and handle simple issues without needing human involvement. When a problem does need escalation, the request is then passed on to a human with relevant context, which helps reduce delays and unnecessary back-and-forth.

In HR teams, AI agents are used to answer common questions about policies and help new staff get set up. However, when something unusual or sensitive comes up, the AI agent will pass it on to a human.

Finance teams use AI agents to deal with high volumes of routine queries, including invoices, expenses, and data validation. Agents help keep records up to date and surface missing or inconsistent information as it appears.

AI agents are used to support lead management and keep CRM data accurate as prospects engage. In marketing, they assist with campaign execution and reporting, which reduces the need for manual coordination between tools.

Across your organisation, AI agents can help employees access information and prepare summaries without jumping between systems. This reduces time spent on administrative work and keeps their attention on high-value work that requires human input.

If you want to see what this looks like in a real product, watch the Agentforce demo to see how an AI agent can take on routine work across different teams.

Companies in several different industries are seeing the benefits of integrating agentic systems. Let’s dig into some AI agent examples by industry, with specific use cases, that show how versatile this technology can be.

Open Universities Australia (OUA) is focused on making education more accessible for Australians. Reaching a broad audience, however, makes it harder to create personal experiences. OUA saw AI as a way to change that.

OUA partnered with LivePerson to create an agentic tool that can engage with new students, handle inquiries with empathy, and provide warm, humanised conversations. Here’s what they achieved:

• Student engagement: OUA’s AI agent mirrors the tone of student advisors, delivering thoughtful, empathetic responses to support students through the emotions of starting university.
• Personalised support: It gathers insights and shares custom summaries with advisors, helping them tailor conversations to each student.
• Efficiency: The agent handles early-stage queries, giving educators more time for high-quality, one-on-one support.

Despite the benefits, OUA had valid concerns about AI bias, hallucinations, and safety. To mitigate this, they put safeguards in place, including local data handling and rigorous testing, to make sure the system was safe for students.

With those concerns alleviated, OUA integrated a chatbot and freed up educator time to focus on higher-value tasks.

One of New Zealand’s largest telecommunications providers, One NZ, is using Agentforce and Data 360 to power its transition to an AI-first telco.

One NZ has already deployed Agentforce to handle enterprise FAQs, log service cases, and respond to common enquiries like international roaming fees.

The long-term vision is even more ambitious. One plans to implement AI agents that proactively manage plan upgrades, offer device upsells, and book in-store appointments, all while surfacing customer insights to support personalised, end-to-end service.

You might be surprised to hear that building an AI agent doesn’t require a computer science background. All it requires is some basic AI literacy. If you don’t feel comfortable using AI yet, now is a great time to start, as we found that 82% of developers say AI literacy is becoming a non-negotiable skill.

If you’re ready to start experimenting with building your own agent, try following this simple process.

Choose a routine task that happens often and has a clear “done” state.

For example: Open a document and write one sentence that starts with “I want the AI agent to…”

Keep it narrow so you can test its results quickly and avoid hard-to-measure metrics of success.

In the same document, list what the agent is and isn’t allowed to do.

For example: “Use our brand guidelines as the source of truth. Answer only questions about tone, colours, and core messages. If a request falls outside the guidelines, say you can’t answer and provide an email for the marketing team.”

At this point, you need to choose the tool you will use to turn your written instructions into a working AI agent.

The simplest way to get started is to create a custom GPT. This lets you paste in your instructions and any documents you want the GPT to reference, and test the agent's response.

If you want the agent to do more than answer questions (this is slightly more advanced), like updating systems or triggering actions, tools like n8n can be used to connect the agent to other software.

If you want the easiest way to build a business-ready agent, Agentforce is designed exactly for this purpose. It lets you create AI agents inside Salesforce, with built-in access to your CRM data and guardrails, so you get a powerful business AI agent, without needing to connect everything yourself.

An AI agent can only answer correctly if it has access to the right information. If you are using a custom GPT, upload the document or paste the content directly into the agent.

If you are using Agentforce, the agent will be able to connect to the relevant business data or knowledge base within your CRM.

So far, the agent can answer questions. This step is about deciding whether it can take any action.

Start by listing one simple action the agent is allowed to perform. For example, this could be drafting a reply for an email or passing a request to a specific team.

Ask the agent the same questions your team or customers would normally ask. Include unclear or incomplete questions, not just the perfectly phrased or information-dense ones.

Pay attention to three things:

1. When the agent answers correctly
2. When it answers, but it shouldn't have
3. When it escalates too quickly

From these findings, make notes on what needs adjusting.

Update the agent’s instructions when you see patterns in its mistakes. Replace outdated documents and clarify rules that are being misunderstood.

Ready to build your own business-ready AI agent? Take our free Build an AI Agent with Agentforce Trailhead course to learn how to create and test an AI agent using Salesforce data and tools.

AI agents can be incredibly helpful, but they don’t come without risk. In fact, 70% of security leaders say they are concerned about the accuracy and explainability of AI outputs.

With these concerns in mind, there are four areas teams need to address before deploying AI agents.

AI agents can produce incorrect or misleading responses when information is incomplete. Clear instructions, trusted data sources, and limits on what the agent can answer help reduce this risk.

AI agents often interact with sensitive business data. With 64% of customers believing companies are reckless with data, having strong access controls, audit logs, and strict data boundaries should be a non-negotiable for your AI agent.

AI agents should not operate unchecked. Guardrails should define when an agent must stop, escalate, or defer to a human, especially for high-impact decisions.

In regulated industries, AI agents must follow existing rules around data use, transparency, and accountability, with controls in place to support compliance.

If you’re getting ready to deploy AI agents, here are some best practices to keep in mind.

Start by defining what you want to achieve with AI agents. Whether it's reducing response times, enhancing customer satisfaction, or cutting operational costs, having clear objectives will guide your implementation process and help you measure success.

AI agents rely on high-quality data to function effectively. Ensure that you have robust data collection and management systems in place. This includes customer interaction data, transaction histories, and other relevant information.

Select the type of AI agent that best fits your needs. For instance, a reactive agent might suffice if you need an agent to handle routine customer queries. Consider a goal-oriented or learning agent that can adapt to changing customer needs and provide more sophisticated support for more complex tasks.

Seventy per cent of ecommerce leaders say poor data integration and harmonisation is a major or moderate challenge when adopting AI.

To mitigate this, ensure your AI agents integrate with your existing CRM and customer service tools. This integration will enable a smooth flow of information and enhance the capabilities of your AI agents, allowing them to access relevant data and provide more effective support.

Design your AI agents with the end user in mind. Ensure that interactions are intuitive and responses are timely and accurate, providing a positive customer experience. Test your AI agents thoroughly to identify and address potential issues before deployment, ensuring they meet customer expectations.

Regularly monitor your AI agents' performance elements and gather user feedback. Use this information to continuously improve your AI agents, ensuring they remain effective and relevant. This ongoing optimisation will help you adapt to changing customer needs and improve the overall performance of your AI agents.

While AI agents can handle many tasks autonomously, it is important to plan for human intervention when necessary. Ensure clear guidelines for when and how human agents can assist, providing a safety net for more complex or sensitive interactions.

Implement robust data privacy and security measures to protect customer information handled by your AI agents. This includes compliance with data protection regulations and regular security audits to safeguard sensitive data and maintain customer trust.

If you're looking to integrate AI agents into your business, Agentforce can help. Here’s how:

• Intelligent AI agents: With Agentforce, you can automate tasks, offer 24/7 support, and resolve cases faster using real-time, trusted data.
• Data-driven personalisation: Integrated with Data 360, agents can access complete customer data to deliver accurate and personalised responses.
• Scalable and secure: The Agentforce 360 Platform supports high volumes while maintaining performance and reliability.
• Customised to your needs: Use MuleSoft to tailor your workflows and connect them with your existing systems.
• Easy integration: MuleSoft's open architecture makes it simple to plug into your tech stack.