Everything You Ever Wanted to Know About AI Agents (But Were Afraid to Ask)

Everyone is talking about AI agents, but few people truly understand them. Businesses are in a race to use agents for a competitive edge, but the real question is: How do you separate the hype from reality?

This no-nonsense blog post cuts through the noise. It answers basic questions about what agents are, what they can realistically do, and how they might fit into your business by deploying them responsibly and securely to achieve a tangible return on AI investments.

• What Are AI Agents?
• How Do Agents Work?
• What Makes Agents Different From Traditional Automation?
• What Does Agent Autonomy Mean?
• What Is Agentic Transformation?
• What’s the Difference Between Generative AI and Agentic AI
• How Did AI Agents Come About?
• What Do I Need to Get Started With Agents
• Why Is Trustworthy Data Essential for Agents?
• Why Is Integration Important for Agents?
• Why Are APIs Important for Agents?
• How Are Agents Built?
• What Are the Risks and Limitations of Agents?
• What Are Agent Guardrails?
• How Are Agents Governed and Secured?
• What Is An Agentic Workflow?
• What Is Agentic Orchestration?
• What Are Agent Tools?
• What Is MCP?
• What Are Tokens and Context Windows?
• What Are Prompts?
• What Processes Are Right for Agents?
• How Can Agents Drive Business Value?
• What are AI Agent platforms?

What Are AI Agents?

Agents are autonomous or semi-autonomous AI-powered software programs that take action and accomplish tasks for specific use cases based on their instructions. Agents have clearly defined goals and rely on built-in contextual reasoning. They can respond to prompts, use digital tools, and access databases. They have memory, follow instructions, and have “guardrails” limiting what they can and cannot do. They can make decisions, interact with systems, and perform work with minimal or no human intervention. Critically, they can perform analysis work and automate tasks – and even roles – that previously could only be done manually. Agents can collaborate in sophisticated workflows that involve multiple applications, databases, access controls, and domains of expertise. They can serve as trusted assistants and collaborators for people in their day-to-day work.

How Do Agents Work?

Traditional software operates on deterministic code branches of if-then-else statements. Automating business processes required encoding all the possibilities the software needed to consider when making decisions. The complexity of this decision-making limited automation to only the most predictable processes that could be anticipated. Automation breaks down when changes or unanticipated exceptions occur. Agents change the game of automation because they don’t need endless labyrinthine forks of pre-coded logic. They can utilize natural language instead of detailed code, and they use goals instead of detailed requirements to accomplish their tasks. They simply act within the context of their instructions and guardrails. Rather than waiting for explicit direction, they can act with varying degrees of independence to achieve their objectives. This means that they can solve problems quickly, coordinating their work with other agents and with people.

What Makes Agents Different From Traditional Automation?

Because agents don’t require every one of their possible actions to rely on predefined rules and scripts, they can take action independently (but within established guidelines), dramatically broadening the scope of what can be automated. And because agents aren’t as rigid as traditional automation, they can solve problems when they arise, instead of simply freezing or throwing an error. Another key difference: users can interact with agents through natural language prompts, eliminating the need for programmers to create new software for analysis and decision-making. This accessibility is why this powerful technology doesn’t just belong in the realm of IT. Agents’ simplicity and ease of use explain why every part of the organization is adopting them. Companies move faster without the typical IT bottlenecks and take greater advantage of the creativity of people within the lines of business who are trying to solve real-world problems that show quantifiable ROI. Users within the line of business can create their own agentic workflows, while IT maintains centralized governance to prevent security and compliance issues.

What Does Agent Autonomy Mean?

This refers to the degree of independence from human oversight and direction with which an agent operates. While the ability to execute some tasks with little or no human intervention is a core feature of all agents, they can be programmed with varying degrees of independence. Perhaps you have straightforward, rote tasks where you’re comfortable with agents doing the work without supervision. These can operate with complete autonomy. Or you can have agents performing more business-critical roles that require much greater oversight by people – a design principle known as “human in the loop.” Agents should keep humans apprised when performing high-stakes work that involves observation and double-checking by people. Agent autonomy is a spectrum, not a yes/no decision. A well-designed agent will operate with the autonomy appropriate to its assigned tasks.

What Is Agentic Transformation?

For years, digital transformation has enabled organizations to transition from manual, paper-based processes to digital ones, improving efficiency, accuracy, and productivity. Still, most automation remained task-based, rigid, and unable to handle the unexpected. As a result, the gap between the promise of digital technology and the business impact continued to widen. Agentic transformation is the next evolution that solves many of those issues. It embeds AI agents directly into business operations, so fixed scripts and predefined workflows no longer constrain work. Embedding agents can happen in one of two ways. Businesses can deploy an agent into an existing workflow, automating tasks that would previously have required human intervention – accelerating the workflow overall. Alternatively, they can rethink workflows entirely to take advantage of agents, not merely replacing manual steps with agent-based automation, but enabling them to lead and manage processes. This second approach yields the greatest ROI. Both types of agentic transformation rely on agents that can understand goals, reason in real-time, and take adaptable, meaningful actions across systems with minimal human intervention. This shift – finally – closes the gap between digital promise and business reality while amplifying human capability.

What’s the Difference Between Generative AI and Agentic AI?

Most widely deployed AI use cases involve generative AI, such as chatbots that answer questions, provide customer service support, and perform tasks like writing emails or summarizing documents. But generative AI systems, which depend on large language models (LLMs), have an inherent limitation. They’re passive and rely on input from people. They need to be prompted into action by a human command before generating content, such as text, images, or database tables. In contrast, agentic AI systems can take many forms of action based on their instructions. They also have long-term memory, enabling them to recall facts relevant to their work, as well as long-term “state,” which allows them to keep track of where they are in a specific process. By using memory and state, agents can remember, plan, execute, improve, and adapt as part of their proactive work. A simple way to think of the difference is that an LLM generates text while an agent uses an LLM as its “brain” to orchestrate complex tasks.

How Did AI Agents Come About?

Machine learning – an AI field that uses statistical learning to generalize about data and take action without explicit instructions – has been a field of study since the 1950s. In late 2022, it rocketed out of the computer lab and into the mainstream with the release of OpenAI’s ChatGPT. Adopted faster than any other consumer technology in history, ChatGPT marked the beginning of the age of generative AI. Other chatbots soon followed, also using pattern recognition to answer any question and perform specific tasks with near-instantaneous speed, thanks to the computing power of LLMs. In mid-2023, the advancement of agentic AI introduced new software that could not only generate text and images but also set goals, plan tasks, and perform actions autonomously – agents. Perhaps what’s most impressive about AI agents is their rate of improvement. Barely two years old, organizations are already trusting them with business-critical functions, such as quote-to-cash processes and invoice reconciliation.

What Do I Need to Get Started with Agents?

Some businesses have struggled to see ROI in these early days of agentic AI because agents themselves are only part of what makes agentic systems successful. It’s not a matter of purchasing the latest and greatest AI models, spot-welding agents in your digital architecture, and expecting success. Businesses must prepare their digital foundations by ensuring agents can work with high-quality, timely data. A significant part of that is connecting systems and databases. That connectivity layer, via integration and APIs (application programming interfaces), makes digital assets interoperable and provides AI models and agents with the necessary data. To get started with agents, you need:

• Trusted Data: It might come from a single application or source, or multiple applications and data sources. Accessing that data likely involves APIs that make data available to other software programs.
• Tools: These can include web browsers, online forms, productivity and collaboration applications, file systems, databases, software repositories such as GitHub, and public data sources. The Model Context Protocol (MCP) – which we’ll discuss in more detail later in this post – helps agents find and work with the necessary tools.
• Development Environment: The place for building agents doesn’t need to be a complex integrated development environment (IDE). It might offer a natural language interface, enabling developers and even business users to design the agent they want using everyday language.
• Control: A way of monitoring agents and ensuring they’re operating correctly (not accessing things they shouldn’t, not succumbing to cyber attacks, hallucinating, and so on). Agents accessing business data must comply with all security policies, just like people do.

Why Is Trustworthy Data Essential for Agents?

The wisdom of “garbage in, garbage out” carries even more weight with AI systems because everything they do – generating content, analyzing data, taking actions – depends on high-quality data. If AI systems rely on poor or incomplete data, it will skew their analysis. AI hallucinations (producing wrong answers) will proliferate, and decision-making will be flawed as a result. Clean, accurate, and trusted data is the fuel that empowers agents by providing the context they need to take appropriate actions. If you don’t get the data right, AI agents will get it wrong at scale because they’re only as good as the information they’re using.

Why Is Integration Important for Agents?

AI agents are only as powerful as the systems they can see, understand, and act upon. Over the years of digital transformation, organizations have modernized their operations by adopting ERPs, SaaS applications, APIs, and automation. While this created enormous capability, it also introduced fragmentation—data silos, duplicate records, and systems that don’t share meaning. This disconnect is a major contributor to the Impact Gap, where the promise of technology outpaces real business results. Integration is what gives agents context, which is only as powerful as the systems they can see, understand, and act upon. A robust integration strategy ensures that data can move freely across systems, is governed, and reflects a consistent source of truth. This allows agents to reason accurately, make informed decisions, and adapt in real time. Without integration, agents face the same limitations humans do today—partial visibility and manual workarounds. With integration, businesses can synchronize “single source of truth” records throughout systems, creating an “agentic” layer for agents to work faster and more accurately. Integration also enables agents to take action within enterprise systems and applications. By connecting to applications and other IT resources, integration extends the reach of agents, increasing their operational benefits and ROI.

Why Are APIs Important for Agents?

APIs are crucial to technology interoperability, serving as intermediaries that enable systems to exchange information in a standardized and automated manner. That connectivity is even more essential with AI. APIs are what give agents their agency by acting as a bridge, allowing agents to securely communicate, access real-time data, and interact with the outside world. APIs serve as standardized interfaces that allow agents to securely access real-time data, interact with applications, and take action to drive outcomes. Through APIs, agents can update records, trigger workflows, coordinate across systems, and respond to changing conditions. Without APIs, agents would be isolated and unable to take action. Think of APIs as giving them agency to act on your behalf.

How Are Agents Built?

To build an agent, you need to bring together four elements:

• Instructions: Agents require clear prompts that define what to do and how to respond to achieve their goals. These prompts serve as directions for agents. Prompts might run several hundred words, providing background data and guidance about the tone and manner in which the agent should respond.
• Models: AI models provide the intelligence and context needed to understand language, interpret context, and generate output.
• Tools: Agents access APIs, web services, events, data pipelines, files, and master data to complete tasks. They might access these tools through MCP servers or by calling APIs directly.
• Guardrails: Defined rules, permissions, and policies keep agent behavior safe, secure, and aligned with business intent.

It’s possible to hand-code software for all these things. Increasingly, however, businesses are utilizing agentic AI platforms to rapidly design, build, and deploy agents, leveraging natural language commands to automate the majority of the work. Building an agent this way might take as little as a few hours or less. Then, they can be tested, first in a test environment and then in a closely monitored business environment. Once released to production, agents need to be managed, orchestrated, and governed continuously to ensure they function correctly and don’t compromise data privacy or security.

What Are the Risks and Limitations of Agents

Agents are powerful, but like any transformation technology, they must be implemented thoughtfully. The primary risks fall into three general areas:

• Transparency and Ethical Concerns: Because they lack transparency in how they accomplish tasks, there’s a risk of bias, difficulty identifying the source of errors, and unintended consequences of their actions. Because agents use probabilistic reasoning rather than deterministic logic, it can be difficult to fully understand how a specific decision was made or discover the root cause of an issue. Without proper oversight, clear goals, and auditability, this can introduce bias and lead to unintended outcomes.
• Security Vulnerabilities and Data Exposure: Agents expand the operational footprint of an enterprise by interacting with more systems, APIs, and data sources. This increases the potential attack surface and raises concerns around access control and sensitive data, including personally identifiable information (PII). Strong identity management, scoped permissions, and data governance are critical to ensuring agents stay within their scope.
• Reliability and Data Quality: Agents are only as reliable as the data and context they receive. Poor data quality or incomplete information can lead to incorrect actions or hallucinations. Also, the non-deterministic nature of agents means that similar inputs can sometimes yield different outputs. This makes testing, monitoring, and continuous feedback loops essential for production use.

What Are Agent Guardrails?

Like guardrails along a road, agent guardrails are rules that prevent agents from swerving off their assigned path and inadvertently creating havoc. They consist of safety protocols and controls governing agent behavior to prevent them from performing harmful, unethical, or non-compliant actions. These constraints are crucial because of the autonomous nature of agents. That independence introduces a new element of risk. Guardrails ensure agents don’t take actions such as deleting or exposing sensitive data, including personally identifiable information (PII), acting inappropriately in ways like injecting bias into decision-making, or creating “infinite loops” that unexpectedly incur costs. Most of all, guardrails raise the level of confidence that everyone has in agents to do their work accurately and appropriately.

How Are Agents Governed and Secured?

Because of agents’ autonomous nature, organizations need a multi-pronged approach to governance and security to ensure they behave as intended. Agent governance and security should include:

• Identity: Begin by recognizing that agents are not generic, cookie-cutter pieces of software. Each agent is unique. Agents need to be managed similarly to human employees with individual, traceable identities that allow the business to audit their behavior, monitor their privileges and access rights, and apply accountability standards.
• Layered Guardrails: Establish rules for what each agent can and cannot do. Then monitor the agent’s internal reasoning, actions, and outcomes to ensure it’s acting appropriately. This means enforcing the guardrails we discussed earlier.
• Secure Ecosystem: Agents must operate in a secure environment to minimize any damage if they are compromised, fail to meet compliance standards, or go rogue. In the proof-of-concept stage, agents should run in an isolated environment such as a sandbox or container. Once released to production, they can run in a business environment equipped with real-time security safeguards such as proactive anomaly detection.
• Oversight Framework: In addition to technical controls, organizations require centralized oversight by a dedicated AI governance board, which is responsible for business policies, risk assessments, determining when human intervention is necessary, and ensuring compliance with organizational policies and applicable regulations. Each organization should establish a Center of Excellence (CoE) responsible for showing that every agent deployed is producing a positive business impact.

What Is an Agentic Workflow?

An agentic workflow defines how an agent gets its job done. Rather than being a fixed, step-by-step process, an agentic workflow describes the agent’s ability to determine what needs to happen and how to make it happen based on the goal it has been given. The agent plans its own sequence of steps, decides which tools or systems to use at each point, takes action, and adjusts its approach as conditions change. This means the workflow is not hard-coded in advance. It emerges at runtime as the agent gathers context, reasons about the situation, and responds to new information. If something unexpected occurs – missing data, a system change, or an exception – the agent adapts instead of failing. An agentic workflow often extends beyond the agent itself, involving integrations, APIs, data pipelines, automations, approval steps, or other software as needed. In short, an agentic workflow shifts process design from “define every step up front” to “define the goal, instructions, tasks, and guardrails” – allowing work to be carried out in a way that is more flexible, resilient, and aligned to how the business actually operates.

What Is Agentic Orchestration?

Agentic orchestration refers to the comprehensive coordination and management of multiple agentic workflows. As work becomes more complex, a single agent often isn’t enough. Complicated processes often require multiple agents working together with humans to achieve broader goals. That requires sophisticated levels of coordination, observability, governance, and oversight – similar to when teams of people collaborate on a major project. One agent might be dedicated to orchestrating the activities of other agents. Or some other software system might take on this role, ensuring that complicated workflows involving multiple agents run smoothly. At its core, agentic orchestration provides coordination of activities across agentic workflows, visibility into what agents are doing and why, oversight to ensure agents operate as intended, and control points for human review or intervention when needed.

What Are Agent Tools?

Tools are what allow an AI agent to be useful. These are the systems, applications, and data sources that an agent can access to achieve its desired outcomes. Typically, the agent will use API calls as the interface to obtain what it needs from those endpoints. The Model Context Protocol (MCP) has emerged as an industry-standard interface that agents can use to find and access tools.

What Is MCP?

The Model Context Protocol (MCP) is a widely accepted, open-source standard for connecting AI applications, such as agents (clients), to tools they might use in their work. These tools could include business applications, databases, file servers, and command-line interfaces. MCP is designed to provide a standard interface that agents can rely on to find and connect to the tools they may need to perform their functions. Just as a USB-C port provides a standard way for hardware devices to connect, MCP provides a standard interface for AI applications and tools. It facilitates easier integration for agents to interact with various components in an environment, allowing them to perform their work more efficiently. While MCP was designed for ease of use, it lacks security and governance features that agents operating in most commercial and government environments require. Fortunately, agentic platforms (like the Boomi Enterprise Platform) allow businesses to take advantage of MCP without jeopardizing data security, privacy, or regulatory compliance.

What Are Tokens and Context Windows?

Tokens are the building blocks of generative AI input and output, comprising words, word fragments, and punctuation. When an AI process receives input, it breaks it down into tokens and uses those tokens to analyze the request in the context of the process’s language model. The  AI process then generates a predictively plausible response, also in the form of tokens, and converts these tokens into human-readable language. A model’s “context window” is its working memory for handling input and output tokens. The larger the context window, the more tokens the model can track simultaneously. When AI agents have the benefit of LLMs with large context windows, they can track more details about business records, transactions, and other data, improving their ability to automate sophisticated tasks. Because they serve as the building blocks of AI operations, tokens also often serve as the basis for AI usage fees.

What Are Prompts?

A prompt is a detailed set of instructions, context, and rules that define the AI agent’s persona, goals, tools, and desired output. The prompt serves as a comprehensive “instruction manual” for the agent, enabling it to use its tools (such as databases, APIs, and command lines) to achieve a broader objective, rather than just answering a single query like a chatbot would. Guided by prompts, an AI agent can autonomously perform complex tasks by breaking them down into small, well-defined tasks and tailoring its results for a specific context or audience. Effective agentic AI prompts define what is requested (the task), how it should look (its format and style), and optionally who the AI agent should be (a role).

What Processes Are Right for Agents?

Your business has dozens, hundreds, or even thousands of processes. Here’s one way to determine which tasks are the best candidates for automation with agents. Consider your repetitive, time-consuming processes. Chances are, they’ll involve data from systems of record (CRMs or ERPs) and systems of engagement (mobile apps or web browsers). Map out all the steps in one of those processes and identify those requiring human reasoning today. Is there a repeatable chain of thought involved? In other words, is there a repeatable process you can automate, especially now that agentic AI can help with some of the decision-making? Take expense reports. In any approval process, there’s a long list of rules to follow and questions to ask. Do the expenses align with company policy? Are receipts attached? Does the text on the receipts match the line items in the expense report? It’s possible to train an agent to determine the answers. Note that there may be some points in the process where you want to include a “human-in-the-loop” handoff, ensuring that employees continue to oversee critical decisions and interactions. For example, you might want a manager to review and approve any expense report with a total above a specific dollar amount. You can build these handoffs directly into your agentic process, creating a mix of digital automation and human intervention that best suits your business use case. Automating processes in this way saves time and money (and, for onerous processes like expense report approvals, likely improves morale). But agents offer more than simply task automation with or without human guidance. They also support role automation, enabling an agent to act as a “digital colleague,” performing work, making decisions, and coordinating activities with other tools and users. Role-based automation offers organizations the opportunity to reimagine workflows, accelerating not just a few steps in a process but the overall handling of processes themselves, leading to greater ROI.

How Can Agents Drive Business Value?

AI agents drive business value by closing the gap between insight and action. They don’t just surface information. They reason across data, coordinate work across systems, and take action to deliver outcomes. This allows organizations to move faster, reduce manual effort, and operate with greater accuracy and resilience. Here are concrete ways agents produce measurable ROI:

• End-to-End Business Processes: In complex, cross-functional processes such as order-to-cash and source-to-pay, agents can operate across every stage, from order validation and supplier sourcing through invoicing, payment, reconciliation, and analysis. Agents enrich data, validate transactions, resolve exceptions, communicate with customers or suppliers, and proactively surface risks, reducing cycle times, revenue leakage, and manual intervention across the entire process.
• Supply Chain and Operations: Agents continuously monitor suppliers, inventory levels, delivery status, and external signals to ensure optimal operations. They can identify risks early, recommend alternatives, and take action to prevent disruptions – improving service levels while reducing the need for costly firefighting.
• Finance and Invoice Reconciliation: Agents validate invoices, reconcile payments, identify discrepancies, and automatically resolve routine exceptions. This improves financial accuracy, accelerates close cycles, strengthens supplier relationships, and frees finance teams from manual reconciliation work.
• Forecasting and Decision Support: Agents analyze real-time data to identify anomalies in forecasts, dynamically adjust models, and trigger reviews when human oversight is required. This keeps leaders informed and confident while ensuring accountability remains in the loop.
• Employee Lifecycle Management: Agents can orchestrate employee journeys from onboarding to role changes to offboarding, automatically coordinating systems, policies, and communications. The result is faster execution, better employee experiences, and reduced operational overhead.
• Claims and Exception-Heavy Processes: In areas such as claims processing, agents identify incomplete, inconsistent, or anomalous data, resolve routine issues, and escalate only those cases that truly require human judgment and expertise. This improves data quality, reduces errors, and speeds resolution times.

What Are AI Agent Platforms?

AI agent platforms provide everything an organization needs to build, run, secure, and monitor AI agents. That means they provide a way to:

• Build agents with instructions, models, tools, and guidelines
• Connect agents to the data they need for performing analysis and completing tasks
• Orchestrate agent activities so that agents can work together effectively to complete complex tasks
• Monitor and secure agents, so developers, administrators, and other stakeholders can ensure agents are operating in compliance with data security and privacy guidelines
• Assess the security status of agents to ensure they haven’t been compromised in a cyberattack

These platforms should offer natural language interfaces whenever possible, allowing even non-technical business users to build and run agents that assist them with their daily tasks.