The Best AI Agent Builders: Comprehensive Guide

2025 is the year of AI agents. According to a study by LangChain, 51% of professionals were already using AI agents last year, with medium businesses leading in implementing the technology. Adoption is set to increase as companies look to augment their current teams, or even add an array of AI employees to collaborate with the human workforce.

But, despite the trend and media coverage, understanding what an AI agent is and identifying the best platforms to build one on can be challenging—especially as each has its unique capabilities and shortcomings. We compiled this comprehensive guide to empower you with everything you need to know to start creating AI agents, and a list of the best platforms to get started with.

What is an AI agent?

A Waymo self-driving car, an example of an AI agent. Photo by gibblesmash asdf on Unsplash.

An AI agent is an entity that can autonomously perceive its environment, make decisions, and take actions to achieve pre-determined goals. It can interact with human users, other AI agents and systems at large in the process.

But the AI agent definition is expanding. Powered by highly intelligent LLMs, generative AI agents can understand human language, interact with other systems, and generate responses. These actions can be triggered through a chat interface, by a software trigger, or as a result of a scheduled action. They can vary in levels of autonomy and in how much humans are involved in the entire process.

Here’s an AI Agents example: a customer service chatbot that responds to questions and proactively schedules appointments or sends follow-up emails. These autonomous AI agents can work without constant human supervision, automating routine tasks and complex processes.

AI agents come in many shapes and forms, from a self-driving car to a smart vacuum cleaner. In this guide, we’ll be focusing on generative AI agents.

Overview of AI agent builders

Setting up an AI agent with Stack AI’s advanced workflow builder.

AI Agent Builders are platforms that empower you to design and deploy AI agents tailored to your specific needs. Whether you’re interested in developing AI agents for business to streamline daily operations or creating sophisticated AI agents for enterprise, these builder platforms provide all the tools to do so. They let you create autonomous AI agents and experiment with generative AI agents capable of producing outputs on demand.

When choosing a platform, look for features such as no-code interfaces, drag-and-drop workflow designers, and customizable templates that illustrate AI agents use cases—from simple information retrieval to complex multi-step automation. Moreover, many of these platforms now offer modules for vertical AI agents, specialized solutions designed for niche industries like healthcare, finance, or retail.

General features and capabilities

Natural language processing (NLP): drawing on the capabilities of LLMs, AI agents can understand and generate human language, taking instructions and executing them as needed.

No-code/low-code interfaces: AI agent builders simplify the building process with visual interfaces, pre-made components and templates. This reduces the level of technical skill required to create, deploy, and manage a new agent.

Integration with external tools: AI agent builders integrate with external tools to read data, start actions, or write data. This helps keep the agent up to date with the latest information and automate its workflow.

Customization options: AI agent builders expose custom instruction inputs, security guardrails and other settings to help you configure behavior, functionality, and user experience.

Analytics and insights: to help you understand impact, AI agent builder platforms offer analytics and insights pages that show usage. When combined with user feedback, you can keep improving your agents over time.

Drawbacks, challenges, and limitations

High costs: AI agent builder platforms can be expensive due to the infrastructure costs of running LLMs and related tooling. Also, since this technology provides a lot of value to customers, platforms may increase pricing based on solving the pain point, not just on base costs.

Steep learning curve: despite being accessible for non-technical people, users still have to familiarize themselves with documentation, development mindset, and how each component works when combined with others.

Difficulty when scaling: while you can focus on efficiency and performance while building, AI agent platforms are each coded in a unique way and invest differently in infrastructure. Depending on these two factors, they may have an easy time scaling your AI agent to a thousand users, or struggle with performance beyond a few hundred.

Data privacy concerns: due to the observability challenges of AI—the fact it runs inside a black box—makes it complex to ensure that private data stays private, that compliance rules are respected and company property isn’t leaked or misused.

there are many vulnerability vectors around data privacy and AI. Exposure can happen in misconfigured user control and access, using AI models hosted on flawed multi-tenant platforms, and unsecured storage solutions.

Inconsistent accuracy: while AI model intelligence and capabilities has increased dramatically, it can still hallucinate important facts. When dealing with your company data, using retrieval-augmented generation (RAG) solutions can reduce the risk surface, but require proactive data maintenance to maintain quality.

AI agents use cases and vertical applications

AI agents use cases span a wide spectrum—from improving team collaboration to enhancing customer engagement. For instance, AI agents for business can manage internal workflows, support human decision-making, and automate repetitive tasks, freeing up employees for higher-value activities. In contrast, AI agents for enterprise might handle complex integrations with legacy systems, monitor data security, and ensure compliance across large organizations.

Exploring diverse AI agents use cases is critical. Some common examples include:

• Automated customer support that reduces response times.

• Real-time data analysis for better decision-making.

• Streamlined workflow management and task automation that optimizes daily operations.

• Processing vast amounts of data into digestible insights.

Moreover, specialized vertical AI agents are emerging, targeting specific industries with customized functionalities.

• In healthcare, vertical AI agents can assist with patient data management and diagnosis support.

• In finance, they streamline risk assessment and customer service, while in retail, generative AI agents create personalized shopping experiences and dynamic marketing content.

But no matter the sector, understanding what are AI Agents and their diverse AI Agents example scenarios can help you determine the best strategy for digital transformation.

Future trends and developments

AI agent builder platforms are evolving quickly, expanding toolkits to leverage LLM intelligence, adding more autonomy, observability and capabilities across the board. The future will likely see even more sophisticated autonomous AI agents that do more than simply respond to commands—they’ll proactively identify opportunities to optimize workflows

When thinking about ease of use, platforms may evolve in three directions depending on target audience:

• Consumer-grade and small business-oriented builders will invest in fully no-code and agent-driven creation experiences, completely removing the complexity from the process. This means building and customizing with natural language only. This can cover a wide range of use cases and provide effective solutions, albeit not extremely customized, efficient or performant.

• Mid-range platforms will still retain a no-code experience, but expose more controls to users. This is especially important for data integrations, LLM custom instructions and setting up external actions. This prioritizes accessibility and development speed while maintaining robust customization options.

• Low-code and developer-grade platforms will focus on adding shortcuts and frameworks to coding custom solutions. While user interfaces may look accessible to non-technical users, they will require engineering experience to operate. Most of the features are geared for code integration, specialized deployment, and extreme optimization.

While the market is still focusing on using groups of AI agents that don’t directly interact with each other, there are already implementations of multi-agent systems. These break down capabilities into groups or swarms of agents, each specialized in a particular task. Then, an AI control system monitored by a human user can plot an action plan, delegate actions, evaluate results and work towards an objective.

And, while most agents today start working on a trigger, based on a schedule, or a result as a user prompt—they are reactive—the future may bring proactive AI agents that take action based on their perception and previous experience.

How to build an AI agent

When building your AI agent, start by clarifying what are AI Agents and their intended role within your organization. Begin with a clear AI Agents example: imagine a scenario where an autonomous AI agent manages customer inquiries while also flagging potential issues for human review. Next, choose the right AI Agent Builder platform that supports both generative AI agents and external system connections.

Plan for diverse AI agents use cases by mapping out every interaction—from basic query handling to complex integrations for AI agents for business or even AI agents for enterprise. This holistic approach ensures your agent executes tasks and adapts to various scenarios, including specialized requirements that might benefit from vertical AI agents designed for your industry.

Understand the problem

Identify and understand the problem the agent will solve. Start with one small, easy to solve problem. Measure how many resources the problem is taking now—hours, team members, money, computing power—as a proof of pain. Document these factors to get buy-in from managers, executives and other team members.

Set objectives for the AI agent. What is it supposed to achieve? Define and track the metrics around the problem, so you can compare your AI agent’s performance to the previous solution.

Don’t forget to interview the teams involved in the workflow. Make sure that your AI agent conforms to expectations, conditions, and preferences. It’s meant to simplify work, not add an extra layer of complexity.

Choose the right platform

The next step is choosing the best AI agent platform. This is deeply connected with the use case you’re automating, the your technical skill level, and the results you’re looking for. For example:

• Stack AI offers a great balance between ease of use and power, with no-code interfaces, varied customization options, and performant RAG capabilities.

• Langflow is more developer-oriented, offering a framework to connect LLMs, agents, and actions fully extensible with code.

• OpenAI’s GPTs offer a fast approach to building simple agents that answer questions and run basic actions, a good match for quick experiments.

Later in this article, we’ll walk you through the best AI agent builder platforms so you can make your choice.

Gather and connect high-quality data

Data quality is fundamental for AI, both for training LLMs and also for improving AI-powered workflows, increasing answer relevance and accuracy.

Always choose the most up-to-date documents and data sources, keeping the data formatted and clean from errors. Most platforms offer sync features, making it easy to ensure that your agents always have the latest information.

Design the instructions and flow

Once you log into your platform of choice and start building, use the available tools to create the workflow. This is the path between input and output: what’s the agent’s purpose? What external systems does it need to contact? Where and how does it respond to the user? You can rely on existing templates, tutorials and documentation to understand how to set up this functionality.

Put yourself in the position of your users and understand what kind of instructions would simplify their interactions.

Incorporate external tools and systems

AI agent builder platforms include features such as tool calling, which lets you set up actions that the agent can run on external systems. To set these up, you have to set up an API connection to the system you want to use, and describe what the agent should look for to trigger it. Once configured, the agent will always run that action based on your instructions whenever it detects the intent.

Integrate other AI features

AI agents rely on LLMs to make decisions and complete tasks, but there are more specialized AI features you can add to improve results. For example, you can add sentiment analysis features to change the flow based on the user’s intent, not just the instruction text. You can also incorporate other machine learning features to calculate probabilities on outcomes, and use the result of that processing as part of the decision-making process.

Test your agent

As you build, develop a culture of testing as you build. This will allow you to correct mistakes or catch unintended functionality earlier. Once you reach a final version, try breaking your agent by asking questions outside the scope. Use your findings to fix issues and improve the agent.

Share your agent with a growing number of your team, and see how they use it. This can give you insights on additional changes you can make.

Launch and iterate

Finally, it’s time to launch. Share your AI agent with everyone that’s going to use it internally. Keep track of analytics in the development platform and invite feedback from users. Use these two elements to keep improving the agent and to build new ones.

The best AI agent builders

How we select the best AI agent builders

We tested the best AI agent builders based on the following criteria:

• Ease of use: we favored platforms leaning on a no-code or low-code approach, making it accessible to beginner to intermediate users.

• Feature set: these platforms can cover a wide variety of use cases by showing a versatile set of possibilities.

• Support: we looked for accessible knowledge bases, tutorials, and starting templates that make it easy to progress through the learning curve.

• Cost: we analyzed all of the above factors and stacked them against the price to see if you’re getting the best deal for your investment.

Stack AI

Pros:

• Intuitive, easy to learn

• End-to-end generative AI workflow builder

Cons:

• Not a good match for small businesses and startups

• Performance can vary depending on your location

Stack AI takes a fully no-code approach to building AI agents and workflows. You can create a new agent by determining instructions, LLM used and connecting your knowledge bases. This will cover a lot of use cases where information retrieval is key. If you want to make it even more powerful, you can create and connect tools to the agent, letting it take action across other systems.

If you’d like your agent to run more complex tasks, you can upgrade it to an AI workflow at any time. This will bring up a visual canvas, where you can drop nodes to connect data and LLMs, finalizing the process by choosing a user interface to interact with the project.

Langflow

Pros:

• Free development platform

• Advanced AI tools available

Cons:

• Not fully no-code

• External services and models paid separately

Langflow is a low-code developer platform that’s still friendly enough for non-technical (but tech-savvy) people. You can create agents that draw on data sources and take action, but the configuration process is complex. For example, for grounding responses on your data, you’ll have to connect a model to extract embeddings from your data, store them in a vector database, and then connect that to the LLM in charge. Still, it’s a robust choice due to the wide range of connectors and AI tools, with the option of extending native functionality with code.

OpenAI GPTs

Pros:

• Very easy to use

• Fixed-price subscription

Cons:

• Runs only within the ChatGPT app

• Not as many features as competitor platforms

Accessible by subscribing to the ChatGPT Plus plan, GPTs were one of the first AI agent builder platforms. With a range of simple controls, you can set system instructions, conversation starters, and documents used to ground answers. As you chat with your GPT, it will use the uploaded data to reply. Additionally, you can add functions to run actions on external systems, and the AI model will decide when to run them based on your settings.

Microsoft Copilot Studio

Pros:

• Seamless integration with Microsoft products

• Quick deployment

Cons:

• Slow indexing of documents and files

• Steep learning curve

As Copilot rolls out to all Microsoft products, you’re faced with a question: how do you configure the chatbot on the right-side tab, whether you want to use it in Word or Excel? Copilot Studio is part of this answer: you can use it to customize the chat experience for Copilot in any 365 apps, and even connect it to other apps and channels. It offers a wide range of data connectors to sync your data across systems, so you can have the most up-to-date answers anywhere while working in Microsoft 365.

Azure AI Foundry

Pros:

• Unified platform with a range of AI services and tools

• Integrates with development tools

Cons:

• Expensive services

• Complex for beginners

The Azure AI Foundry (formerly Azure AI Studio) is another Microsoft product for building AI conversational agents. It is a step up in power and complexity when compared with Microsoft Copilot Studio, and a better match for building agentic experiences for multiple channels. In addition to data connections, integration with AI models, and providing a chat interface, it includes useful extras: tools for testing outputs, safety and security controls, and endpoint management for integrating the agent with other platforms.

Google Vertex AI

Pros:

• Seamless integration with Google services

• Supports full range from no-code to code approaches

Cons:

• Limited model choice for AI Agent Builder

• Visual interface is too complex

At heart, Vertex AI is a platform for hosting and running AI models in a cloud, offering APIs to integrate it with your apps. One of its features, the AI Agent Builder, lets you build complex multi-step agents in a low-code interface. You can configure each step, adding labels and configuring what happens when certain conversation conditions are met. It integrates with Google Cloud Platform for extra services, such as Vertex AI Search and Conversation for grounding AI responses.

Amazon Q

Pros:

• Can support coding tasks

• Assists with building on AWS

Cons:

• Limited customizability

• Difficulty processing data types beyond text

Amazon Q is an AI chat agent part of the AWS offering, answering questions to any team member based on your company data. It does so by connecting to your data sources and automatically indexing all the data it finds. At the same time, it gathers semantic data about your business, understanding what you do so it can provide tailored help. Help will always be available in a dedicated chat window or as part of your integrated development environment if you’re a coder.

Langchain

Pros:

• High flexibility

• Wide range of integrations

Cons:

• Complex documentation

• Difficult debugging

LangChain is an open-source framework that streamlines the development process of LLM-powered applications. It offers a robust toolset to developers looking to build reasoning solutions, with full access to external data and APIs. While powerful, it requires deep technical knowledge, as most of the process involves coding.

Beyond this development framework, you can use LangGraph for orchestrating workflows, enabling agentic chatbots; and LangSmith for inspecting, monitoring, and evaluating the results of LLM applications.

Salesforce Agentforce

Pros:

• Native integration with Salesforce data and services

• Provides data-driven insights from usage

Cons:

• Complex implementation process

• Early-stage development

Agentforce is Salesforce’s answer to AI agents. Using existing data and actions in your account, you can build agents to help you with customer support on your website, qualify sales leads, and optimize marketing campaigns. While you can integrate these in external channels, Agentforce offers a dedicated chat interface where you can ask questions, start actions, and see results in a single place. This product integrates seamlessly with everything else inside your CRM, a good choice if you’re an existing Salesforce customer.

Relevance AI

Pros:

• Great AI-driven insights

• Scalable

Cons:

• Steep learning curve

• Inconsistent customer support

Relevance AI’s templates give names to each AI agent, putting a friendlier face on top of an utilitarian tool. You can get on top of your lead generation targets with Apla, the Prospect Researcher; get more info on new prospects with Elli, the Enrichment Agent; and answer all customer questions with Suni, the Intercom Support Agent. If you don’t see a virtual employee that matches your needs, you can create one yourself on the platform.

Stack AI vs The best AI agent builders

Stack AI is the most well-rounded platform in the AI agent category. It offers a combination of versatility and power, never cutting corners in security and compliance.

It’s a fully no-code tool, so anyone without technical skills can start building an AI agent. This way, your IT team won’t be pressured to deliver AI-powered tools, and the people on the front line will be able to build their own solutions with independence. This way, your IT team won’t be pressured to deliver, and your front-line workers can be independent when solving productivity issues.

How is the experience of building an AI agent in Stack AI? Once you start a new project, you can quickly write instructions, connect knowledge bases, and customize the user experience.

You can start with the name, description and by choosing the agent’s brain: its LLM. Stack AI integrates with all leading providers such as OpenAI, Anthropic, and Google, along with open-source stars such as Meta and Mistral. It’s easy to upgrade to a newer model: you can edit your agent and adjust the provider and model dropdowns to get the latest intelligence.

Specify the agent instructions in plain English: no need to code or configure complicated workflows. Add conversation starters to steer users when using the functionality or to save time for repetitive interactions.

Stack AI connects to a wide range of data sources. This includes platforms like Amazon S3, Microsoft Sharepoint, Google Drive, Notion and HubSpot, among many others. If you don’t see your platform on the list, you can bring your data in by connecting via API. Your data will be synced into the platform and remain up to date: no manual refreshing required.

Agents can do more than retrieve information. This is why you can add tools to your agent: run native actions such as a customized knowledge base search or extract a text pattern with Regex extract. You can also add API calls to the agent, allowing it to interact with external systems when it detects the conditions during the conversation.

It’s easy to deploy your agent. When you click the Publish Agent button at the top-right of the screen, it becomes available as a link instantly. Simply copy and paste that link into a text message, internal communication channel, or an email and invite feedback.

As your team uses the agent, take a look at the Analytics and Manager tabs. This gives you an overview of total runs, users and errors, along with a conversation history. These insights are fundamental to improving your agent in the long run.

You can create multiple agents for a variety of tasks or convert any of these into a Stack AI project: this enables the visual drag-and-drop canvas, where you can connect inputs, models, and knowledge bases to transform data using generative AI.

All the token costs are included in your subscription, so you don’t have to worry about conversation or document length. On top of that, when you graduate to the Enterprise plan, more than unlimited tokens, you gain a partner in generative AI automation. Stack AI’s team is ready to help you identify workflows you can automate and keeps track of your ROI.

Microsoft Copilot Studio vs Stack AI

Microsoft Copilot Studio focuses on its native integration with the Microsoft 365 and surrounding ecossystem, with a wide range of data integrations. Stack AI is a better option as it plays well with any infrastructure regardless of brand, variety of user interfaces, and a visual building experience.

Microsoft Copilot Chat vs Stack AI

Microsoft Copilot Chat is similar to the above product but is more deeply integrated within the Microsoft 365 experience, answering questions on connected data sources. Stack AI offers more data connectors, more user interfaces, and more overall customization.

Vertex AI vs Stack AI

Vertex AI’s biggest strength is its AI Model Garden, offering machine learning tools to developers—even if it doesn’t offer access to all leading models on the market. While it has a conversational agent builder, it’s requires developer-grade understanding to use. Stack AI is an easier, more powerful option with a faster setup time.

Azure AI Foundry vs Stack AI

Azure AI Foundry (formerly Azure AI Studio), is Microsoft’s agent builder solution that’s a step above Microsoft Copilot Studio. While it offers better scalability and customization, it’s still too complex to configure and hard to use. Stack AI is a faster, easier-to-use option, offering more functionality and wider integration with leading AI model providers.

The Future of AI agents - empowering tomorrow’s world

AI model intelligence exploded since 2023. Now, the tools to help them take action on external systems are improving. This will make it easier for users to interact with computer systems, with a layer of intelligence between humans and backend systems automating tasks. This makes a case for the death of SaaS, as some thought leaders are predicting.

Agents can serve as assistants to human workers or even act as AI employees, automating entire job descriptions, scaling productivity massively. These possibilities make the case for a future where companies are more productive and offer higher quality products and services—with the possibility of a single person running a company using a multi-agent system.

Since AI agents can be so flexible, they can tailor themselves to any task and personal preference, adapting to the human it’s assisting, not the other way around. More than that, people will be able to interact with technology without having to learn complex software interfaces: simply typing or speaking in any language, issuing commands, and describing problems will be enough. And while this is a story of user empowerment, it’s also a case of preventing them from making mistakes by putting adequate guardrails in place, with a positive impact in security and privacy.

While AI regulation isn’t mainstream, it’s expected that states may begin restricting what agents can do on behalf of users, or introduce caveats or warnings to help users navigate the functionality of each tool. This may require additional development time and investment in compliance.

And with platforms investing in ease of use and power, everyone will be able to create anything from personal assistants, work copilots and fully automated agents to complete tasks. This can add a layer of time freedom for users, as more automation can free up time to either do more high-value work.

As more autonomous AI agents are deployed, we can start seeing agent-to-agent interactions and multi-agent systems automating complex tasks and representing users in more contexts.

Get started with AI agents

It’s a great time to explore AI agents: all the platforms on the market are improving quickly, offering generous incentives to trial features. With Stack AI, you can build your first agent using a free plan account in less than an hour, and share it with your team right away. The sooner you start exploring, the better prepared you’ll be as it becomes mainstream, giving you a headstart in your AI automation strategy.

Frequently asked questions on AI agents and AI agent builders

What are the key challenges of developing AI agents?

When building AI agents, key challenges reside in:

• acquiring high-quality data that the agent can use to respond to questions and use as a base for reasoning

• ethical concerns regarding which kinds of power or functionality should an agent have, what kinds of systems it should access and how many privileges should it have

• transparency and explainability, building systems that can be used to audit the AI’s thought and decision-making process

• integration with existing systems, as to how the agent connects to each system, how it reads and writes into that system, and what kinds of actions it can run

• uptime and reliability, ensuring it works within the expected parameters for as long as possible, providing consistent results

• handling unexpected inputs, preventing crashing, performance issues, data leaks, or erroneous actions

Can AI agents integrate with existing business systems?

Yes. Many platforms offer data connectors natively that reduce the integration complexity. For custom systems or more niche platforms, almost all offer an API connector that you can configure to expose system capabilities to AI agents

What are the most common challenges faced during AI agent development?

Despite the ease of use of AI agent builder platforms, it’s hard for new users to understand all the parts that make an agent, how they should be connected and how they influence each other. This requires experimentation, experience, and making helpful mistakes.

The bigger pain points revolve around understanding how agents interact with integrated software platforms, how the instructions affect reasoning quality and sequence, and ensuring response consistency when faced with a diverse range of prompts.

What are the security considerations when deploying AI agents through builder platforms?

There are multiple aspects to ensuring privacy and security when building AI agents. First, it’s important that the platform’s data infrastructure is secure: look for certifications such as SOC or HIPAA to understand how it deals with these issues.

Access to the AI agent builder platform should also be tightly controlled, as anyone who has access to it could also access connected data sources. Make sure all logins have strong passwords and multi-factor authentication enabled.

If multiple stakeholders are building in the same workspace, set up role-based access control (RBAC) to data sources and advanced functionality. This enables admins to have full control over the platform, while still offering building features to other members of the team without exposing critical data.

When passing sensitive data to an AI model, make sure that your data is not exposed to provider companies, either for training or for debugging. You can do this by implementing PII safety measures, filtering personal information from prompts before they’re sent for processing.

Finally, you also need to protect the user interfaces to interact with AI agent platforms. Since they can be connected to your internal systems, they can retrieve data and start tasks for anyone who has access to them. Invest in password protection and SSO-based authentication to make sure that no unauthorized users can interact with the agent.

What are the costs to consider when using an AI agent builder platform?

There’s usually a platform fee for accessing all the builder tools, supporting databases and other related services. On top of that, running an LLM incurs costs in either tokens or compute time, depending on how you choose to host them.

When choosing the best platform for you, it’s important to understand how these costs are calculated and whether running LLMs is included in the pricing or not.

Additionally, having your agent use external services and APIs may incur additional costs with these platforms. Be sure to monitor any external calls and understand the way they’re priced.

What does human-in-the-loop mean?

Human-in-the-loop means that, along the entire action loop of an AI agent, there are points at which the system checks in with a user. It can share a report, expose a set of options and ask what to do next. This helps improve the accuracy of the agent, increase oversight and make sure no major errors occur during the process.

What is the difference between a copilot and an AI employee?

The term copilot is used to describe an AI system that acts as an assistant to a human. It can be used to fill the gaps, save time and increase productivity, never replacing the human. An AI employee refers to an AI agent that has enough autonomy and capabilities to automate an entire job description, potentially including proactive features instead of just reactive features like a copilot does.

Can AI agents take action in other systems?

Yes. When connected to external systems via an API, AI agents can detect action intents (either as a result of its internal reasoning or a user request) and run actions accordingly. Before doing so, you need to set up each action and what intent to look for.

How does an LLM change the way an AI agent works?

More intelligent models increase the reasoning capabilities of an AI agent, making it better at understanding requests and carrying out tasks. However, more intelligent models also require more compute to run—and, in the case of reasoning models such as OpenAI o3, more time to think. Taking into account these nuances is fundamental to ensure your AI agent is performant and accurate.

What happens if an AI agent can’t fulfill a request?

When an AI agent can’t fulfill a request—either because it doesn’t have the privileges or information to do so—you can configure ways to let the user know what happened and what to do next. These include fallback messages where the AI clearly states that it can’t carry out an action or doesn’t know anything about the topic—which is a major step up from producing a hallucination.