What are AI agents?

What is an AI agent?

AI agents are systems that can independently observe a situation, make decisions based on goals and context, and take action — without needing constant human input. They’re designed to go beyond simple automation, combining reasoning, memory, and the ability to interact with other systems or tools to achieve a defined outcome.

Unlike traditional bots or scripts that follow fixed instructions, AI agents are capable of adapting their behaviour based on what’s happening around them. They can plan multi-step tasks, respond to changing conditions, and even decide when to escalate or pause. This makes them especially useful for dynamic, high-context tasks across operations, customer support, internal workflows, and more.

Benefits of AI agents

Let’s break down the core elements behind the scenes — the ones that turn an AI-powered tool into an actual agent capable of working independently:

Perception

AI agents need a way to take in data from their environment. That could be natural language (via text or voice), sensor data, images, clicks, logs, or any other input that gives the agent context. This is where NLP, computer vision or speech recognition often come into play. Perception is what lets the agent “see” or “hear” what’s happening before it decides what to do.

Reasoning & decision-making

This is the core logic. Once the agent understands what’s happening, it needs to figure out what to do next. Some agents rely on rule-based systems or decision trees. Others use more complex methods like reinforcement learning, planning algorithms or probabilistic models to weigh different options, predict outcomes and choose actions that align with goals.

Memory

Smart agents remember. They track previous interactions, learn user preferences, store historical outcomes, and continuously adapt their behaviour over time. Memory can be short-term (session-based) or long-term (user profiles, past decisions). This allows the agent to get better — and more relevant — the more it’s used.

Learning loop

Unlike traditional automation, AI agents improve with experience. They collect data from every interaction, compare outcomes, and adjust future decisions. Learning is a key part of what makes them more than just static systems – through supervised learning, reinforcement learning or fine-tuned LLM prompts.

Actuation

Finally, the agent needs to act on decisions. That might mean responding to a user, triggering a process, sending an alert, updating a database or coordinating with another system. Actuation can be as simple as a chatbot reply or as complex as orchestrating workflows across platforms.

Types of AI agents

AI agents vary widely in how they operate and what they’re capable of. Some follow fixed instructions, while others learn, adapt, and act with increasing independence. Here are the most common types:

Reactive agents

These are the simplest kind. They don’t have memory or the ability to learn — they just respond to the current input. Think of them as highly efficient responders, useful for repetitive tasks with predictable conditions.

Model-based reflex agents

A step up from reactive agents, these can store information about the world and use it to make better decisions. They maintain a basic internal state, helping them react more accurately in dynamic environments.

Goal-based agents

These agents evaluate different actions based on desired outcomes. They can plan ahead, weigh trade-offs, and choose the best path toward a defined goal. This makes them ideal for more strategic tasks.

Utility-based agents

When there’s more than one way to reach a goal, utility-based agents shine. They not only aim for a result, but also try to maximise usefulness — picking the option with the best expected value. These agents handle complexity and uncertainty well.

Learning agents

These are the most adaptable. They learn from experience, improve performance over time, and adjust their strategies based on feedback. They’re often used in environments that change or where outcomes can’t always be predicted in advance.

Multi-agent systems

Sometimes, the best results come from teamwork. In a multi-agent system, several agents collaborate — or even compete — to achieve broader goals. This setup is common in logistics, simulations, and distributed problem-solving environments.

AI agents, AI assistants, and bots: what’s the difference?

These terms are often used interchangeably, but they refer to different levels of complexity and capability.

• Bots are automated programs designed to perform specific, often simple, repetitive tasks. They can answer basic questions, send messages, or follow predefined commands, but usually lack the ability to adapt or learn from interactions. For example, a website chatbot that only replies to “What are your opening hours?” is a bot.

• AI Assistants are a step up from bots. They use artificial intelligence to better understand user intent and context, allowing them to handle more complex tasks like scheduling meetings, offering personalized suggestions, or guiding users through services. AI assistants improve over time by learning from interactions. Examples include Siri, Alexa, and Google Assistant.

• AI Agents are broader and more autonomous systems designed to perceive their environment, make decisions, and take actions to achieve specific goals. They often combine several AI capabilities — like natural language processing, planning, and problem-solving — and can operate across multiple domains or tasks without constant human input. AI agents might manage workflows, optimize processes, or handle dynamic situations with minimal supervision.

Real-world applications for AI agents

Here are some practical examples where they deliver real value:

Customer support

AI agents handle routine inquiries instantly. They help book appointments, answer product queries, and fix common problems anytime, day or night. Over time, they learn what each customer prefers, making conversations smoother and quicker — which keeps customers happier without extra staff.

Smart home automation

These agents can even learn your habits. They adjust heating, lighting, and security automatically to fit your routine. For example, they can turn off the lights when you leave or warm up the house just before you get home. It’s like having a system that pays attention, so you don’t have to.

Supply chain management

AI agents keep an eye on stock levels, predict when demand will rise or fall, and spot issues before they happen. They help businesses order just the right amount, avoid delays, and cut down on waste. This means smoother deliveries, lower costs, and happier customers.

Healthcare assistance

In healthcare, AI agents collect and analyse data from patient records and wearables to catch warning signs early. They suggest personalised treatments and monitor recovery progress, giving doctors a powerful tool to improve care while letting them focus on patients, not paperwork.

Financial advisory

AI agents track spending, spot suspicious activity, and offer tailored advice based on market changes and personal goals. They help people manage money smarter and faster, while banks can provide more relevant support without waiting for a human agent to step in.

How to implement AI agents?

Introducing AI agents into your workplace needs to be done thoughtfully.

1. Identify key tasks and pain points

Start by pinpointing the repetitive or time-consuming tasks your team faces daily. For example, if your customer service reps spend hours answering common questions, that’s a prime spot for AI. Or maybe the scheduling or data entry processes are slow and prone to errors. Choose tasks where AI can bring clear improvements in speed or accuracy.

Tip: Talk to your frontline teams. They know best what slows them down.

2. Involve your team early

Bring in the employees who will use or be affected by the AI agent. Ask what they would find helpful and what worries they might have. If your sales team feels the AI could help by prioritising leads, get their input. This makes the AI more useful and builds trust.

Tip: Run workshops or informal chats to gather honest feedback before building or buying the solution.

3. Start small and pilot

Don’t roll out AI across everything at once. Begin with a simple, well-defined use case, like automating meeting scheduling or answering FAQs on your website. This allows you to see how the AI performs and fix issues early without disrupting the whole business.

Tip: Choose a department or team open to trying new tools and ready to give feedback.

4. Train and fine-tune continuously

Use real user interactions to improve your AI agent’s responses. If it misunderstands customer questions or misses certain details, adjust its training. Over time, it will become more accurate and aligned with your specific needs.

Tip: Set up regular review sessions with your team to assess performance and suggest improvements.

5. Integrate with existing systems

Connect your AI agents to your CRM, calendar tools, databases, or other platforms. For example, an AI agent integrated with your CRM can provide sales reps with up-to-date customer info without manual searches.

Tip: Involve your IT department early to smooth out integration challenges.

6. Maintain human oversight

Define clear escalation paths so the AI hands off tricky issues to human experts. This keeps customers happy and reduces frustration. For instance, if an AI agent can’t resolve a complaint, it should immediately connect the customer with a human advisor.

Tip: Train your team to work alongside AI, not against it, seeing it as a helpful assistant.

7. Scale up thoughtfully

Once your pilot shows positive results, expand your AI agents’ role step by step. Maybe next they handle more complex queries or support additional teams. Always keep measuring how well the AI is performing and how users feel about it.

Tip: Avoid rushing; growth with continuous feedback keeps your AI agents effective and trusted.

Agentforce: powering AI agents the smart way

Salesforce Agentforce is the powerhouse platform that brings AI agents out of isolation and into the heart of your business. Instead of AI working blindly, Agentforce hooks them directly into your Salesforce ecosystem — tapping into live customer data, ongoing cases, and real-time business processes. This means AI agents deliver context-aware, instantly relevant responses that actually matter.

What sets Agentforce apart is its deep integration with Salesforce’s CRM, Service Cloud, and other clouds, letting AI agents act with full insight and authority. It’s like giving your AI a backstage pass to every customer interaction and business rule, so they can solve problems faster and more accurately.

But the real game-changer? Agentforce is designed to work alongside your teams, blending automation and human intelligence seamlessly. It routes complex issues to the right expert, automates repetitive tasks, and keeps your operations running smoother — all while improving the customer experience.

If you want AI that understands your customers as well as your best team members do, this is the platform to watch.

Ready to make AI Agents work for you?

AI agents hold incredible promise, but turning that potential into real, everyday value takes the right partner. At Near Partner, we design and implement AI solutions that fit into your workflow, making technology work in your favour. Using platforms like Salesforce Agentforce alongside custom AI tools, we deliver practical, effective systems that support your teams and elevate the customer experience.

If you want AI that delivers results and makes daily work easier, we’re here to help. Get in touch!