AI agents are gaining attention as businesses seek to automate tasks, enhance efficiency, and improve decision-making. But what exactly are AI agents, and how do they differ from traditional automation or chatbots? More importantly, what are their real-world applications?
Agent Smith from the Matrix (or the many Agent Smiths) were tasked with enforcing laws and protecting the system from being exposed by truth.
What are AI Agents?
An AI agent is a software-based system capable of perceiving its environment, making decisions, and taking action toward a goal. Unlike traditional automation, which follows rigid workflows, AI agents adapt to new information, interact dynamically with users, and continuously improve over time.
Instead of just answering queries, AI agents can analyze patterns, automate workflows, and take actions that traditionally required human intervention.
Many CRM's and LLM's are now promoting their agentic AI frameworks. While automation SaaS players like Zapier, Make, Ui Path & N8N have been automating businesses for a long time, inviting GenAI models to this party with their advanced reasoning & decision making abilities has created this hype in 2025. But, beware some agentic AI tools are just robotic process automation, all dressed up with a new name. They also can't be left 100% unattended to do the task, for the majority. A human in the loop is key.
What AI Agents Can’t (and Shouldn’t) Do
While AI agents are powerful, they are not a complete replacement for human expertise. They work best when:
• Paired with human oversight for complex decision-making.
• Continuously trained on new data to remain effective.
• Designed to escalate cases when emotions, ethics, or creativity are required.
AI agents are not eliminating jobs—they are redefining roles. Organizations that strategically integrate AI agents are improving operational efficiency, enhancing employee productivity, and creating better customer experiences.
Where is Agentic AI Being Used Successfully?
Customer Service: AI Agents for Instant Support
Traditional customer service relies on human representatives to handle customer queries. AI agents are now being deployed as the first layer of support, handling repetitive issues and escalating complex cases when needed.
Example: Telecom AI Service Assistant
A large telecom provider introduced AI agents to handle customer support for billing inquiries, service upgrades, and basic troubleshooting. The AI agent could:
• Retrieve billing history and payment details.
• Walk customers through common troubleshooting steps.
• Escalate cases requiring human intervention.
Outcome:
• 50% fewer support tickets required human involvement.
• 60% faster resolution times.
HR: AI Agents for Recruitment and Employee Support
Hiring and HR support are resource-intensive, requiring significant manual effort. AI agents are now streamlining recruitment and employee engagement.
Example: AI in Candidate Screening
A multinational corporation deployed an AI agent to assist with hiring. The agent:
• Reviewed resumes and identified strong candidates.
• Conducted preliminary video interviews.
• Matched candidates to roles based on skills and culture fit.
Outcome:
• 70% faster candidate screening.
• 40% reduction in hiring costs.
Beyond recruitment, HR AI agents also help employees with benefits enrollment, payroll queries, and company policy questions.
Caution: Businesses without a strong legal team & budget may want to weigh the risks of using certain AI in hiring selection due to equal opportunity and discrimination concerns. Lawsuits like this one against Workday are real and part of being an early adopter for certain AI use cases.
IT: AI Agents for Automated Troubleshooting
IT departments spend significant time responding to helpdesk tickets. AI agents can act as digital assistants, troubleshooting common issues and even performing automated fixes.
Example: AI in IT Support
A financial services firm implemented an AI-powered IT support agent. The system:
• Assisted employees with password resets and software installations.
• Diagnosed connectivity and performance issues.
• Provided step-by-step solutions or escalated complex cases.
Outcome:
• 50% decrease in IT support tickets.
• 80% faster resolution for common issues.
CX: Contact Center AI in Back Office Processes
Sit any good AI Engineer down in your back office contact center team to shadow and you're bound to find a treasure trove of agentic AI use cases.
Example: Claims Processing in an Insurance Contact Center
A large insurance company’s contact center had a dedicated back-office team responsible for processing customer claims after initial first notice of loss. The process involved:
1. Reviewing call transcripts or case notes.
2. Extracting relevant policy and customer data.
3. Validating claims against policy coverage.
4. Flagging discrepancies for manual review.
5. Sending approval or follow-up requests to the customer
The company deployed an AI-driven back-office agent to streamline this workflow. The AI agent:
• Automated Data Extraction – Analyzed transcripts and structured unstructured data from emails, chats, and voice interactions to pre-fill claim forms.
• Policy Validation – Cross-referenced the claim details with customer policies and flagged issues (e.g., missing documents, coverage limits).
• Fraud Detection – Used anomaly detection models to flag potential fraudulent claims for manual review.
• Decision Support – Generated claim approval recommendations for human adjusters based on historical data
• Customer Notifications – Sent automated updates to customers, reducing inbound calls about claim status.
Outcome:
40% reduction in processing time per claim.
30% fewer errors due to manual data entry being eliminated.
Improved compliance through AI-driven audit trails and automated documentation was another benefit and great example of where AI could or should be leveraged.
AI agents in the back office don’t just process information—they understand, validate, and automate entire workflows. They reduce workload for human teams, increase efficiency, and improve accuracy, making them invaluable for industries like insurance, healthcare, and finance where compliance and documentation are critical.
Caution: Having a human in the loop process designed for claims is key, versus completely automating approvals/denials. Class action suits like this one will continue to make headlines beyond healthcare.
AI agents are already proving their value in real-world applications. If you’re considering them for your organization, start by identifying:
• High-volume, repetitive tasks that can be automated.
• Areas where AI can complement human employees.
• Existing workflows that could benefit from intelligence and adaptability.
By understanding the opportunities AI agents offer, businesses can move beyond the hype and start leveraging them as practical tools for growth and efficiency. A new term for some very familiar AI use cases can create confusion & hype. But buzzwords help to sell and AI agents are one step beyond where they were just last year.
"AI is not the enemy. Incorrectly used or deployed AI is." - Josh Streets
These stories enable businesses to prepare for the future, without overcommitting AI resources or costs prematurely. By focusing on AI policy, governance, strategy, readiness and refinement of entry level AI automations, companies can make strategic moves today that position them for success in a GenAI future. While the stats are exciting and showing a clear trend, ROI and AI trust require a carefully executed plan.