What is an AI Agent?

Step 1: Perception

First, an AI agent receives input from various sources. Depending on its purpose, these could include:

• User interactions
• APIs pulling data from external systems
• Sensors or logs from connected applications
• Stored knowledge bases – like inventory sheets, HR policies, etc.

Step 2: Processing

Once it has the data, the AI agent needs to understand it.

The agent might use NLP, structured data, or real-time signals to process whatever input it’s built to use.

If it needs to fetch relevant knowledge from a database, it might use retrieval-augmented generation (RAG) to retrieve it.

Step 3: Decision-Making

The decision-making process will depend on how a builder structures an AI agent.

It might use bespoke business logic, like deciding whether a lead is qualified based on a formula devised by the sales team. 

It might also use machine learning predictions or reinforcement learning, like flagging a transaction as fraudulent based on past instances of fraud.

The best AI agent tools will account for AI explainability: how well an AI agent can clarify the reasoning behind its decisions.

Step 4: Taking Action

After perceiving, processing, and deciding, an AI agent is ready to take action.

There’s no limit to the actions an AI agent can take. It might follow up with a simple text response, like ‘These 3 accounts are showing signs of potential churn.’

It might trigger an API call, like fetching real-time inventory data from a warehouse system or initiating a password reset request.

Other AI agents take direct operational actions, like adjusting pricing in an e-commerce store, scheduling a sales call, rerouting a logistics shipment, or modifying system settings based on security policies.

Some AI agents even interact with external applications, like automating workflows in CRM systems, updating customer records, or issuing refunds based on predefined business rules.

These agents can career out entire agentic AI workflows from end to end.

No matter the action, the AI agent ensures that its response aligns with the decision-making process — and in many cases, it learns from the results to improve future actions.

1. LLM Routing

First and foremost, you’ll need to outsource the cognition of your AI agent to an LLM. In fact, sometimes you’ll hear the phrase ‘LLM agent’, a subset of AI agents. 

A good agent should be able to use different LLMs for different tasks.

There’s no single superior LLM, especially with the rapid rate of development. It might be beneficial for your AI agent to use one model when it’s generating long-form text, and another model when it’s analyzing your user’s input.

Are all AI agents LLM agents? Almost, but not quite.

The AI agents that don’t use LLMs include robotic process automation bots, multi-agent systems like traffic control systems or swarm intelligence, and reinforcement learning agents (like in robotics).

2. Identity and Instructions

Any AI agent needs an identity, a mission, and goals. Why does it exist? What is it going to accomplish and how is it going to achieve it?

Take an example: the first-line of defense for a customer service team at an IT support company. The goal of this AI agent might be to correctly solve as many customer issues as possible, while escalating complex cases to human agents. 

Instructions should define not only its role, but its decision-making threshold (i.e. when should it escalate or refer a user elsewhere?) and its KPIs.

3. Tools

Tools are how an AI agent gathers data and takes action. 

Because of its autonomous nature, an AI agent is able to choose which tools it should use to perform task. 

For example, a lead generation AI agent might have the task of creating qualified leads in Hubspot.

Based on the user interaction, the agent might choose to check the CRM for duplicates, suggest specific content for the user, or ask further questions until they can score the lead.

An AI agent’s arsenal of tools could include:

• External systems, like HubSpot, Linear, or Zendesk
• Code execution, in order to create ad hoc tools
• Built-in capabilities
• Other AI agents
• Humans (e.g. an AI agent needs human approval before carrying out a task)

4. Memory and Knowledge

An AI agent’s memory and knowledge define what it knows and how it retains information over time.

Unlike traditional software that simply retrieves information on demand, AI agents can store, recall, and build on past interactions to make smarter decisions.

For example, a customer support AI agent might remember past troubleshooting attempts with a user and avoid repeating ineffective solutions. A sales AI agent could recall previous interactions with a lead and adjust its messaging accordingly.

AI agents rely on two primary types of memory:

1. Short-term memory – Temporary context from an ongoing conversation or task, like a user’s language preference.
2. Long-term memory – Persistent knowledge that the agent can access over time, like recalling order volumes or supplier preferences.

Beyond memory, AI agents access structured and unstructured knowledge sources such as databases and APIs, company knowledge bases, or other relevant documentation.

5. Channels

Channels are how an AI agent interacts with users. It might use text, images, video, or voice, depending on the use case. It might reach them via a website widget, a webchat interface, 

AI agents can be deployed on webchat widgets, messaging apps (WhatsApp, Messenger, Telegram, Slack, etc.), or even embedded in email workflows.

For voice interactions, voice agents can integrate with phone systems or smart assistants, while text-based agents can operate in live chat, SMS, or internal enterprise tools.

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6. Governance 

AI laws are evolving worldwide, and building an AI agent without considering compliance is a wasted effort.

Governance ensures your AI agent operates ethically, transparently, and within legal boundaries.

A well-governed AI agent follows:

• Policy adherence – Aligns with brand guidelines, tone, and business rules.
• Reporting & KPI tracking – Monitors performance, bias, and decision accuracy.
• Approvals & Human-in-the-Loop (HITL) – Requires human validation for critical actions.
• Feedback mechanisms – Continuously improves based on user input and oversight.‍
• Compliance & audit trails – Logs decisions and actions to meet regulatory requirements.

Customer Service

One of the most common applications of AI agents is the humble customer support bot.

These virtual agents can point customers towards specific policies, provide personalized product suggestions, or even handle account tasks like resetting a password.

It’s become the norm for companies to offer customer service chatbots – but the rule-based chatbots of yesteryear often reflect negatively on a brand. These days, it’s dynamic LLM agents that are serving an organization’s users.

We're entering the death of AI chatbots and the rise of AI agents. Even (or especially) customer support bots need to level up.

Lead Generation

The majority of AI agents deployed on Botpress – at least at the time of writing – are some form of lead generation agents.

Lead gen agents are a subset of AI sales agents.

They often dispense critical information to users and collect qualified leads along the way, routing them to sales teams without manual intervention. 

Waiver Group, a healthcare consulting firm, was able to increase their leads by 25% after deploying a bot to replace their ‘contact us’ forms.

Waiverlyn would converse with website visitors, qualify leads, and book Google Calendar events – all without human intervention.

Knowledge Management

A use case that’s better handled by bots than humans, knowledge management can span from internal documentation to customer-facing self-service systems.

Employees can waste hours searching for critical information buried in wikis, PDFs, emails, or support tickets. An AI agent can respond to a natural language query with relevant account information, policies, or troubleshooting steps. 

On the customer-facing side, this might look like an insurance bot that helps users find the relevant forms and guidelines.

Workflow and Task Orchestration

Workflow and task orchestration AI agents don’t just execute single actions — they coordinate multiple steps across different systems. (This is sometimes known as AI orchestration.)

• A procurement AI agent might automatically generate purchase requests, cross-check them against budgets, and send them for managerial approval before placing an order. 
• In HR, an onboarding AI agent could schedule training, provision software access, and set up payroll for new hires without anyone needing to lift a finger. 
• AI agents in IT can triage support tickets, check system logs, and escalate unresolved issues to engineers.

Instead of businesses stitching together different automation tools for each process, AI agents act as centralized orchestrators — handling entire workflows dynamically, making real-time decisions, and adapting as conditions change.

This type of AI workflow automation is one of the most common use cases for AI agents.

Artificial intelligence is easily applied to the small day-to-day tasks that take time away from knowledge workers.

Developer Co-Pilots

AI agents are becoming essential for developers, speeding up coding, debugging, and documentation.

A co-pilot AI can autocomplete code, flag errors, and suggest optimizations in real time.

Beyond coding, these agents help with pull request reviews, security checks, and dependency tracking.

For engineering teams, AI co-pilots mean faster development cycles, fewer bugs, and less time spent on repetitive tasks.

Virtual Assistants

Sometimes, all you need is a bit of extra help.

Someone to conduct research, analyze metrics, or consolidate information. Maybe you need a personal scheduler to send reminders about upcoming tasks, or an assistant that can draft emails and summarize reports.

These gaps can be filled by AI agent assistants, software programs that execute tasks on your behalf.

The concept of an AI assistant is already familiar to us – like Siri and Alexa (the most famous voice assistants around).

AI agents allow for the next step of deeply personalized planning.

If you’re planning a vacation, an AI travel agent assistant can not only suggest locations for a new destination and identify hotels, but select the optimal flight and hotel – and then book them on your behalf.

1. Extensible and flexible

AI agents aren't limited to rigid workflows. They dynamically select tools, APIs, and models based on context, making them far more adaptable.

2. Autonomous decision-making 

Instead of predefining every flow, AI agents make real-time decisions and carry out end-to-end tasks. They're faster to build and far more efficient after they're deployed.

3. Scalable across use cases 

An AI agent built for customer support can be extended to handle sales, internal workflows, or HR automation without a complete rebuild.

4. Round-the-clock availability

AI agents operate continuously, handling tasks, responding to users, and executing workflows without downtime.

5. Cost-efficiency at scale

AI agents reduce the need for large manual teams in customer support, sales, and internal operations while maintaining high-quality service.

6. End-to-end automation

AI agents don’t just answer questions; they execute workflows, trigger actions in CRMs, manage approvals, and make real decisions, reducing operational bottlenecks.

7. Seamless system integration

AI agents connect with tools like Salesforce, HubSpot, Zendesk, Slack, and proprietary systems, ensuring a unified tech stack.

8. Faster time-to-value (TTV)

Unlike traditional automation projects, AI agents learn from interactions and continuously improve, accelerating deployment and ROI.

9. Improved accuracy and compliance 

AI agents can follow brand guidelines, legal frameworks, and decision logic, ensuring they operate within business policies.

Multi-Agent Systems

Multi-agent systems (MAS) consist of multiple AI agents that interact in order to achieve overarching goals. 

These systems are typically designed to address tasks that are too large, complex, or decentralized to be managed by a single AI agent. Proper AI agent routing ensures that the right task is assigned to the right agent.

Each agent in a multi-agent system can act independently, perceiving and interpreting the environment, making decisions, and then taking action to fulfill its goal.

The efficiency of a MAS is assessed via AI agent eval systems, which can include both quantitative and qualitative insights.

For example, a market research firm could use a MAS where one agent gathers industry reports, another extracts key insights, a third summarizes findings into client-ready briefs, and a fourth monitors data accuracy and refines outputs over time.

Simple Reflex Agents

Simple reflex agents operate based on a set of predefined condition-action rules. They react to the current percept and do not consider the history of previous percepts.

They’re suitable for tasks with limited complexity and a narrow range of capabilities. An example of a simple reflex agent would be a smart thermostat.

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Model-Based Reflex Agents

Model-based agents maintain an internal model of their environment and make decisions based on their model's understanding.

This allows them to handle more complex tasks. 

They’re used in the development of self-driving car technology, since they can collect data like the speed of the car, the distance between the car in front of it, and an approaching stop sign. The agent can make informed decisions about when to brake based on the car’s speed and braking capabilities.

Utility-Based Agents

Utility-based agents make decisions by considering the expected utility of each possible action.

They are often employed in situations where it's essential to weigh different options and select the one with the highest expected utility.

If you want an agent to recommend things – like a course of action or different types of computers for a certain task  – a utility-based agent can help.

Learning Agents

Learning agents are designed to operate in unknown environments. They learn from their experiences and adapt their actions over time.

Deep learning and neural networks are often used in the development of learning agents.

They’re often used in e-commerce and streaming platform technology to power personalized recommendation systems, since they learn what users prefer over time. 

Belief-Desire-Intention Agents

Belief-Desire-Intention agents model human-like behavior by maintaining beliefs about the environment, desires, and intentions. They can reason and plan their actions accordingly, making them suitable for complex systems.

Logic-Based Agents

Logic-based agents use deductive reasoning to make decisions, typically over logic rules. They are well-suited for tasks that require complex logical reasoning.

Goal-Based Agents

Goal-based agents act to achieve their goals and can adapt their actions accordingly. They have a more flexible approach to decision-making based on the future consequences of their current actions.

A common application for goal-based agents is robotics – like an agent that navigates a warehouse. It could analyze potential pathways and select the most efficient route to their goal destination.

Step 1: Identify a pilot use case

“Let’s get an AI agent!” If your boss tells you this after reading the latest headlines about ‘the year of AI agents’, then it’s up to you to identify what kind of AI agent you should pilot.

It’s easy to get lost in the hype, but the best approach is to start with a clear, high-impact use case.

Consider where an agent could reduce workload, improve accuracy, or enhance decision-making, such as lead qualification, customer support, or internal knowledge retrieval.

A strong pilot use case should be narrow enough to implement quickly but valuable enough to demonstrate impact.

The right choice will make it easier to secure buy-in, prove ROI, and lay the foundation for broader AI adoption.

Step 2: Find the right platform

The right tools will depend entirely on your circumstances – how much in-house development expertise do you have? How much time? What do you need your agent to accomplish (not just for your pilot use case, but long-term)?

In most cases, it makes sense to use an AI platform instead of starting from scratch.

The optimal choice will often be a vertical, flexible platform: a building software that allows you to build any use case and connect to any external tools. 

You can check out our list of the best AI agent building tools, the best chatbot platforms, or even the best open source platforms. But I’ll be real – I’m pretty biased towards ours.

Botpress is used by 35% of Fortune 500 companies and 500,000+ builders.

We’ve deployed AI agents for years, and it’s free to start using, so you don’t really have anything to lose.

Step 3: Integrate tools

If your AI agent will be creating Hubspot leads, you’ll start by integrating your AI platform with Hubspot. 

While a good platform will come with pre-built integrations, niche use cases will require further work to customize your agent’s connectors.

If your team integrates multiple systems – either internal tools or third-party software – your agent can act as AI orchestrator, ensuring smooth synchronization across platforms.

Step 4: Test and refine

The fourth step is to test your agent thoroughly using your platform’s built-in testing tools.

Adjust parameters, prompt phrasing, and workflows based on testing outcomes to ensure the agent performs well in real scenarios.

Step 5: Deploy and monitor

While the build and deploy stages often take center, don’t underestimate the importance of long-term monitoring with bot analytics. 

Your platform should come equipped with monitoring tools to track your agent’s interactions and performance after deployment.

Gather insights and refine the setup as needed, taking advantage of any feedback mechanisms provided by the platform. 

And remember: the best AI agents require updates. Some of the highest-performing AI agents out in the field have been updated hundreds of times since their initial release.

Your ROI will only go higher the more you tweak your agent.

Start small, then expand

We’re entering the era of AI-enhanced organizations – but no one will make the jump all at once. Start with a strong pilot use case that can gain a quick win before expanding your AI agent.

We refer to this as the Crawl-Walk-Run method. You can read more about it in our Blueprint for AI Agent Implementation.

Ensure high-quality data sources

As the old saying goes: garbage in, garbage out. If your AI agent isn’t pulling information from well-maintained databases, its impact is going to be limited.

If your agent is using Hubspot to track deal cycles and analyze closed-won and closed-lost predictors, then your sales reps need to be vigilant in tracking their prospects’ calls and data.

Set clear KPIs and success metrics

It’s hard to know how successful your AI agent is if you can’t properly measure its impact.

Define KPIs upfront — whether it’s response accuracy, time saved, conversion rates, or cost reductions. These benchmarks will help guide improvements and demonstrate ROI.

Use RAG

Using retrieval-augmented generation allows your AI agent to ground its answers in up-to-date data, like a company’s knowledge base, CRM, or documentation.

This reduces the chance of hallucinations, and it ensures that responses are accurate and contextually relevant.

Compliance Risks

AI agents must adhere to regulations like GDPR, HIPAA, SOC 2, and industry-specific policies. 

Compliance risks are one of the biggest reasons that builders choose to create AI agents on platforms, rather than build from scratch.

If your job isn’t AI compliance, your resources are better spent leaving it to the professionals.

Mishandling user data, failing to log decisions, or generating non-compliant responses can result in legal and financial consequences.

Hallucinations

Hallucinations are when conversational AI systems generate incorrect or misleading information.

These slip-ups have been the center of scandals like the Air Canada chatbot fiasco or the bot that sold a Chevy Tahoe for $1.

Cautiously-made AI agents rarely hallucinate. It’s possible to guardrail the quality of its responses with retrieval-augmented generation, human validation, or verification layers. In fact, there are several ways to keep AI agents hallucination-free.

Lack of Explainability

If an AI agent is making decisions, your team should be able to understand how and why.

A black-box system that delivers outputs without transparency can erode trust, making it difficult to diagnose errors, ensure compliance, or refine performance.

Explainability is especially important for regulated industries, where decisions need to be auditable.

Techniques like logging agent reasoning, surfacing sources, and incorporating human-in-the-loop validation can help keep AI-driven decisions clear and accountable. 

If explainability isn’t built in, your team will spend more time justifying the agent’s actions than benefiting from them.

Ongoing Resources

AI agents aren’t a ‘set and forget’ resource.

They’re a real software project that requires ongoing monitoring and improvements over time. Maintenance is a necessity that, if overlooked, will tank the success of an agent.

The good news is that this is only a downside if your team doesn’t plan for it. If you’re prepared to embark on an AI investment, the ongoing resources required for an AI agent can easily be seen in the returns.

1. Autonomy

AI agents can operate without human intervention, making decisions and acting on them independently.

Their autonomy allows AI agents to handle complex tasks and make real-time decisions on how to best complete a process, but without a human coding the specific steps for a given task.

While the idea of an autonomous agent may conjure images of HAL 9000, the talking computer from 2001: A Space Odyssey, AI agents still rely on human instructions.

A user or developer will need to spend time telling the agent what to do – but the agent will problem-solve how to best complete the task.

2. Continuous learning

Feedback is essential for the AI agent's improvement over time.

This feedback can come from two sources: a critic or the environment itself.

The critic can be a human operator or another AI system that evaluates the agent's performance. The AI agent’s environment can provide feedback in the form of outcomes resulting from the agent's actions.

This feedback loop allows the agent to adapt, learn from its experiences, and make better decisions in the future.

It will learn to create better outcomes as it experiences more tasks. Because of their ability to learn and improve, AI agents can adapt to rapidly changing environments.‍

3. Reactive and proactive

AI agents are both reactive and proactive in their environments.

Since they take sensory input, they’re able to change the course of action based on changes in the environment.

For example, a smart thermostat can sense the temperature of the room getting colder as an unexpected thunderstorm begins. As a result, it’ll decrease the intensity of the air conditioning. 

But it’s also proactive – if the sun shines into a room at approximately the same time each day, it will proactively increase the air conditioning to coincide with the emergence of the sun’s warmth.

What are common mistakes businesses make when deploying their first AI agent?

A common mistake businesses make when deploying their first AI agent is launching without a clearly defined use case or measurable success criteria, which often results in low engagement and limited impact. Many also treat the AI agent as a one-time deployment rather than a system that requires regular updates and refinement to remain effective over time.

How much time and budget should I plan for an AI agent pilot?

A focused pilot project can typically be launched within 2 to 6 weeks, using $300 to $700, especially with no-code or low-code platforms.

What kinds of logging or audit trails should I implement?

You should log every user input, the agent’s decisions, actions taken, and all API calls, including timestamps and user identifiers to enable traceability. For greater transparency and diagnostics, it’s helpful to also include reasoning steps or confidence scores when available.

What does human-in-the-loop (HITL) look like in practice?

In practice, HITL means the AI agent pauses for human input on specific decisions, such as approvals, escalations, or ambiguous cases, before proceeding. This creates a safeguard, ensuring that uncertain actions are overseen by a person when needed.

Can a single AI agent support multiple departments (e.g., HR and Sales)?

Yes, a single AI agent can serve multiple departments, provided it is designed with proper context separation, clear instructions for role-based behavior, and intelligent routing of queries. Many organizations begin with one department and expand the agent’s capabilities incrementally to avoid overlap or confusion.