What Are AI Agents? How They Work and Why They Matter

Have you ever wondered how AI agents are revolutionizing every area of business, including ITSM, HR, and customer care? Consider intelligent systems that can execute their job, decide based on the job done, complete recurring tasks, and even evolve on the go. 

So, in this blog, we will deconstruct what AI agents are, how they work, and why teams like DevTools use them (particularly in ServiceNow). We will lay out their most fundamental concepts, knowledge bases, Simple reflex agents, utility-based agents, machine learning, etc., but, at the same time, make it easy to read and skim.

What are AI agents?

An AI agent is an autonomous software entity that perceives its environment, reasons according to its knowledge base, and takes actions to achieve specific goals. Agents can range from reflex-based bots that respond to triggers to learning agents that evolve via machine learning.

In essence, they are action-based, goal-based, or utility-based systems that perform tasks and assist in areas like customer experience, IT operations, and business automation.

Why are AI Agents important today?

AI agents are changing the way contemporary businesses are run, creating intelligent automation, the 24/7 response capability, and optimization of internal and customer-facing operations. They are the only means through which they can think, decide, and act in the contemporary world of the digital-first environment.

• Automate repetitive tasks (e.g., ticket routing, invoice processing)
• Deliver real-time issue resolution and decision-based actions
• Free humans to focus on complex, creative work
• Enhance customer experience with 24/7 service
• Provide audit-friendly logs to point at the solution during reviews or regulatory questions

In sectors like ITSM powered by ServiceNow AI agents, this is critical. Agents can proactively detect incidents, open tickets, suggest fixes, and provide a knowledge base link to auditors when needed.

How does an AI agent work? (Step-by-step process)

An AI agent passes through a systematic cognitive loop, which begins with input receiving, interpreting, choosing a course of action and learning about the result. This loop allows continuous delivery of values and flexibility.

1. Perception: Collects information through APIs, sensors, events, or in chat interfaces.
2. Interpretation: Analyzes data (e.g., a ticket to reset a password).
3. Reasoning: Applies rules or logic like an agent which is of Simple reflex (if-then), Model-based reflex or a goal-based/ nothing-based process.
4. Decision: Chooses the best course of action based on utility or predefined goals.
5. Action execution: Triggers workflows, automates real-time fixes, calls CI/CD pipelines, or sends responses.
6. Learning & Feedback: A learning agent identifies new patterns and enhances future decisions through machine learning.

This step-by-step loop echoes how AI agents work to handle complex tasks and deliver value continuously.

What are the different types of AI agents used in businesses?

AI agents vary in complexity, from rule-based responders to adaptive systems that learn and plan. Businesses deploy different types based on the nature of the task and desired outcomes.

1. Simple reflex agents – follow condition‑action rules; perfect for predictable, repetitive tasks.
2. Model-based reflex agents – track internal state to make smarter, immediate responses.
3. Goal-based agents – plan steps toward a defined goal (eg, processing an HR onboarding request).
4. Utility-based agents – optimize between multiple outcomes, like cost vs performance.
5. Learning agents utilize machine learning to become smarter and handle evolving tasks.
6. Hybrid or multi-agent systems combine agent types to facilitate complex workflows.

Specialized agents in ITSM, HR, and customer service

Agentic AI is already revolutionizing key business functions by tailoring agents for domain-specific needs. These specialized agents combine knowledge, logic, and automation to deliver targeted outcomes.

• ITSM agents (e.g., ServiceNow Virtual Agent): integrate with knowledge base, trigger CI/CD in Azure Pipelines, escalate incidents.
• HR onboarding agents: automate repetitive tasks, gather customer data, and recommend training.
• Customer service agents: chatbots that enhance customer experience, pull from CRM, and escalate to humans only when needed

These agents optimize across tasks, whether decision-based or action-based, and improve customer experience in real time.

What are the key components of an AI agent system?

For AI agents to function effectively, they rely on a modular system architecture that supports sensing, decision-making, learning, and acting safely and reliably.

• Knowledge base: Store of domain info, rules, policies, and FAQs.
• Perception module: Input collectors (APIs, chats, sensors).
• Reasoning engine: Handles reflex, goal, or utility logic.
• Learning component: Utilizes data and feedback loops to enhance performance (for learning agents).
• Action modules: Connect to workflows and other tools.
• Monitoring & logging: Record activity for analysis, real-time error detection, and compliance, which is important for audits or regulatory reviews.

What are the main benefits of using an AI agent?

Automating tasks

They tackle repetitive actions, ticket routing, data entry, and document processing, freeing team members to focus on more complex work.

Enhancing decision-making

Combined with machine learning, agents analyze data and make utility-based decisions in milliseconds, ideal for dynamic environments.

Improving user experience

Agents deliver consistent, speedy responses to customers or internal users; they do background checks, find answers in the knowledge base, and personalize interactions.

Boosting productivity & reducing costs

They scale effortlessly, operate 24/7, and reduce human error. As AI agents improve over time, your ROI grows.

Compliance & transparency

Every action logged in real time helps point to the solution for auditors or regulators, reducing compliance headaches.

Other benefits:

• Lower time-to-resolution for IT issues.
• In HR, smoother onboarding with no manual follow-ups.
• In service, faster queries and higher satisfaction.

Where are AI agents used today?

• Customer support (ServiceNow Now Assist).
• ITSM: ticket triaging, incident detection, remediation bots in ServiceNow pipelines.
• HR: candidate screening, onboarding automation, benefits processing.
• DevOps: coding agents commit fixes, trigger CI CD Azure Pipelines, enforce quality via Jenkins, GitHub Actions, GitLab CI, with code quality checks.
• Finance: Smart assistants prepare reports, audits, and detect fraud.
• E‑commerce: purchase agents, chatbots, and inventory tracking.

Startups like Hebbia act as super interns, analyzing documents, building models, drafting memos with zero supervision

What are the risks and challenges of AI agents?

• Reliability: Even complex tasks can still fail; therefore, humans need to be involved in supervision.
• Ethical issues: Bias, accountability, misuse.
• Security: Exposure to sensitive customer data must be tightly controlled.
• Job displacement: Agents can replace junior roles, impacting career pipelines.
• Complexity: Hybrid or multi‑agent systems require careful orchestration

What are the best practices for implementing an AI agent?

1. Start small – automate simple, repetitive tasks first.
2. Leverage existing knowledge base – QA FAQs, IT policies.
3. Select the appropriate agent type: reflex, goal-based, utility-based, or learning.
4. Integrate with pipelines, including Jenkins, GitHub Actions, GitLab CI/CD, and Azure Pipelines.
5. Log everything – for troubleshooting, audits, and regulatory reporting.
6. Include human oversight – monitor via alerts and dashboards.
7. Iterate and learn – refine logic, add machine learning and AI loops.
8. Govern usage – guard data access, define ethical boundaries.

How is an AI agent different from chatbots, LLMs, and copilots?

• Chatbots respond to user queries but don’t act autonomously; AI agents can perform tasks proactively.
• LLMs (like ChatGPT) generate text but have no connection to actions/pipelines.
• Copilots (e.g., coding copilots) help inside IDEs but still require developer execution.
• AI agents combine perception, reasoning, action, and often learning in one package—they integrate with tools and perform real-world workflows.

What are some real examples of AI agents in action?

• ITSM: ServiceNow Virtual Agent detects incidents via monitoring, logs tickets, suggests fixes, and escalates when needed.
• DevOps: A coding agent reviews PRs, suggests changes, merges, and triggers CI CD Azure Pipelines, Bitbucket, Jenkins to automate code, or GitLab CI jobs.
• Sales: SDR agents contact leads, schedule meetings, and enter data into CRM.
• Finance: The Hebbia agent builds valuation models and memos as effectively as a junior analyst.
• Office automation: Microsoft’s agents handle scheduling, emails, and reimbursements 24/7.

What is the future of AI agents in business and technology?

AI agents are on the way to becoming a basic element in the way enterprises operate, interact, and innovate. Their intelligence, their autonomy, and their connectivity are increasing, and so will their impact on efficiencies and changes in the business.

• Greater acceptance: Anticipated to increase to several billion dollars by 2030; ~33 percent of enterprise applications will involve the use of agents in 2028.
• Multi-agent systems: Agents that collaborate to do end to end complex tasks.
• Greater Integration: With IoT, edge computing, blockchain, etc.
• Improved autonomy: Agent-based workflows self-govern:
• Upskilling human beings: Positioning of the Supervisory AI to avoid being replaced and promote ethical utilization.

About ServiceNow AI Agents

ServiceNow embeds agentic intelligence deep into the enterprise fabric, enabling powerful automation, insight, and action across business functions—all with enterprise-grade reliability and compliance.

• Virtual Agent: A goal-based conversational agent on the Service Portal.
• Now Assist: Pulls from the knowledge base to perform tasks, make real-time suggestions, and triage issues.
• Agents enforce workflows, call an API, integrate with CI/CD, and can kick off machine learning powered decision flows.

ServiceNow AI agents are engineered for high compliance and audit-readiness, with built-in logging and proof trails.

How can DevTools help you implement ServiceNow AI agents?

DevTools is your end-to-end partner for unlocking the full power of ServiceNow AI agents, from strategy to deployment, and beyond. We bring deep technical expertise and a track record of transforming workflows across industries.

• Assessment & Strategy: Identifying where AI agents can drive ROI.
• Integration: Hooking agents into ServiceNow along with Jenkins, GitHub Actions, GitLab CI, or Azure Pipelines.
• Designing agent logic: Selecting reflex, goal, utility, and learning agents based on need.
• Configuring knowledge base sourcing for audits and regulated use.
• Monitoring & Governance: Real-time dashboards and logs for auditors.
• Training & adoption: Enabling teams to work effectively with agents.

We’ve empowered clients to transform their ITSM, HR onboarding, customer support, and DevOps processes, and they’re now saving weeks of manual effort each month.

Contact DevTools today to start your AI agent journey.

Conclusion

AI agents are the evolution of automation, intelligent, autonomous, and capable of making decisions based on goals, utility, or learned experience. Whether they’re Simple reflex agents tackling repetitive tasks or learning agents evolving over time, they’re reshaping how businesses operate. Integrated into tools like ServiceNow, they enhance workflows, improve customer experience, and provide an audit-friendly trail. At DevTools, we help you select, implement, and govern AI agents wisely, enabling you to achieve ROI and remain compliant. Let’s start building your next-gen AI agent solution.

FAQs

What is an AI agent?

An autonomous software system that perceives its environment, reasons (using rules, goals, utility, or learned data), and acts to achieve specific goals—often within a knowledge base context.

What do AI agents do?

They automate repetitive tasks, aid with decision-making, improve customer experience, log actions for compliance, and can perform tasks proactively or reactively.

How is an AI agent different from an AI model?

An AI model (like GPT) predicts or generates data. An AI agent acts—it uses the model’s output to trigger workflows, make decisions, or call tools. It sees, thinks, and does.

What’s a real example of an AI agent?

ServiceNow’s Virtual Agent triages IT incidents by analyzing events, opening tickets, using knowledge base content, and escalating via workflows—all autonomously.

What are the key components of an AI agent?

Knowledge base, perception, reasoning engine, action modules, monitoring/logging, and (for learning agents) a machine learning loop.

Is ChatGPT an AI agent?

No, it’s a large language model. It generates text but doesn’t autonomously execute actions like agents do. ChatGPT can be embedded within an agent to help with reasoning or dialogue, but the orchestration is the agent’s role.