AI agents are being used in 78% of organizations in some form, while 85% have adopted AI agents in at least one process.

Companies are already using agentic AI to automate IT support, financial operations, and customer service while learning which applications deliver measurable business value.

What Are Agentic AI Applications?

AI agents are autonomous systems that complete complex business tasks without human intervention.

Customer service agents resolve routine requests and escalate complex issues. Supply chain agents monitor inventory, predict demand, and reorder products automatically. Financial trading agents analyze market data and execute trades based on performance. HR agents screen resumes, schedule interviews, and send candidate communications.

Healthcare agents monitor patient vitals and alert medical staff to changes. Manufacturing agents detect equipment problems and schedule maintenance. Sales agents qualify leads and update CRM systems after prospect interactions.

These applications adapt based on new information and past outcomes, improving performance without reprogramming.

Why Are Agentic AI Applications So Important? 

Agentic AI applications solve digital fragmentation by connecting disconnected systems and automating complex business decisions that previously required human intervention.

Most organizations struggle with isolated software applications that cannot share data or coordinate workflows. When systems don’t connect, AI agents can’t do their jobs. Agentic applications bridge these gaps by accessing multiple data sources simultaneously and making decisions based on complete information rather than partial views.

By 2028, 33% of enterprise software will include agentic AI, automating 15% of work decisions.

Agentic AI Applications vs. Traditional Automation 

Agentic AI differs from RPA, chatbots, and rule-based automation by making autonomous decisions rather than following predefined scripts, connecting multiple systems simultaneously, and handling exceptions without human intervention.

Traditional automation tools like robotic process automation follow predefined scripts. Agentic AI agents make autonomous decisions based on context and data. They connect multiple systems simultaneously instead of operating within single applications.

These tools stop when they encounter exceptions and wait for human intervention, while AI agents can proceed without human input.

Organizations can automate complex workflows and respond to changing conditions in real time with agentic AI.

Dive into your AI agent strategy with the Ultimate Guide to AI Agent Management

Real-World Agentic AI Applications by Department 

Agentic AI applications vary by department based on data sources, decision complexity, and automation objectives, with each area requiring different levels of human oversight and system integration.

• IT Service Management
  • Using conversational tools to create more informative tickets, software provisioning, incident detection, VPN troubleshooting, device monitoring.
• Security Operations
  • Network monitoring, threat detection, automated incident response, vulnerability scanning, compliance reporting.
• Sales and Marketing
  • Lead scoring, pipeline management, campaign automation, customer segmentation. Mention Boomi’s integration with Amazon Q Business for grounding agents in company knowledge bases.
• Customer Service
  • Ticket routing, case management, knowledge base automation. Include Australian Red Cross example: scaled from 30 to 300,000 incidents/day during wildfire emergencies in under 24 hours.
• Engineering and Development
  • Code generation, CI/CD monitoring, bug prioritization, documentation automation, testing workflows.
• Operations and Supply Chain
  • Inventory management, demand forecasting, route optimization, supplier monitoring, warehouse automation.PN troubleshooting, device monitoring.
• Human Resources
  • Resume screening, benefits administration, onboarding workflows, PTO tracking, employee data updates.
• Financial Operations
  • Expense report automation, compliance monitoring, invoice processing, payment approvals. Reference Forrester TEI study: 307% ROI over 3 years, $3.4M incremental revenue.

Industry-Specific Agentic AI Applications 

Different industries deploy AI agents for tasks ranging from patient monitoring in healthcare to fraud detection in banking, with each sector focusing on automating high-volume, decision-intensive processes.

• Healthcare and Life Sciences
  • Genentech: Built gRED Research Agent to automate manual searches and speed up drug discovery
• Manufacturing
  • Ford: AI-driven predictive maintenance alerts maintenance teams before equipment failures
  • GM: AI-powered robotics adapt to production schedule changes without downtime
  • Toyota: AI virtual agents handle in-vehicle voice commands for audio, climate control
  • BMW: Building driver assistance systems for 2025 vehicles using cloud-based AI tools
• Financial Services
  • JPMorgan Chase: Coach AI tool enables advisors to respond 95% faster during market volatility .
  • Wells Fargo: Fargo virtual assistant uses AI agents enhanced with Google Gemini for customer service
  • Capital One: AI workforce grew significantly for engineering talent and infrastructure
• Retail
  • Walmart: Four “super agents” – Marty for suppliers, Sparky for shoppers, Associate Agent, Developer Agent. Their AI inventory system manages real-time stock levels during peak holiday shopping
  • Amazon: AI agents optimize delivery routes and warehouse operations Target and other retailers: 69% report revenue growth from AI-driven personalization
• Public Sector
  • U.S. Patent Office: AI-powered search system helps examiners find relevant patent data
  • Veterans Affairs: Automating medical imaging processes for diagnostic services
  • Department of Motor Vehicles: AI systems detect fraudulent license applications
  • Federal agencies: 11 agencies doubled AI use cases from 571 to 1,110 between 2023-2024
• Higher Education
  • Stanford Virtual Lab: AI professor leads team of AI scientist agents for research
  • Harvard Business School ChatLTV: Aggregates student-AI interactions for professor insights
  • MIT Medical AI Bootcamp: Students are working with AI on medical research projects
  • Universities are deploying AI teaching assistants for 24/7 student support

Technical Requirements for Agentic AI Applications 

Organizations need scalable computing resources, API-connected systems, current data feeds, and control frameworks to run AI agents.

• Integration Architecture
  • Pre-built connectors to enterprise applications eliminate custom development work and allow AI agents to access data from CRM, ERP, and other business systems immediately. Organizations avoid building point-to-point connections that create maintenance overhead and integration bottlenecks.
• Data Management:
  • Unified data view across systems prevents agents from making decisions based on incomplete or conflicting information. Boomi Data Hub provides a synchronized 360-degree view of data, ensuring AI agents access current customer records, inventory levels, and transaction history from a single source rather than querying multiple databases separately.
• API Management:
  • Flexible API design and governance controls how AI agents interact with business applications while maintaining security and performance standards. Boomi API Management handles security and scale, providing authentication, rate limiting, and monitoring capabilities that prevent agent abuse while ensuring reliable system access during high-demand periods.

Why Boomi Powers Successful Agentic AI Applications

Boomi addresses the digital fragmentation that prevents organizations from deploying AI agents by providing the integration foundation, data synchronization, and agent management capabilities needed for autonomous systems to operate across disconnected business applications.

Boomi Agentstudio provides full AI agent lifecycle management through four key components that address design, deployment, monitoring, and integration challenges:

• Agent Garden: Unified space for AI agent design, testing, deployment across the organization with centralized governance and version control.
• Agent Designer: No-code design interface with pre-built templates and built-in guardrails that allow business users to create agents without programming expertise.
• Agent Control Tower: Centralized dashboard for monitoring and orchestrating agent activity, providing real-time visibility into agent performance and decision-making.
• Agent Step: Embed AI agents directly into integration processes, allowing agents to participate in automated workflows and data transformations.

Boomi’s integration platform provides the technical foundation AI agents need to operate effectively:

• Pre-built connectors eliminate custom development work
• Model Context Protocol (MCP) native support for automatic API exposure to AI systems
• Amazon Q Business integration for grounding agents in company knowledge bases
• Support for Amazon Bedrock and Salesforce Agentforce agents
• API Management for secure, scalable API exposure to agents
• Data Hub providing synchronized 360-degree view of data across all systems
• Visual, drag-and-drop automation interface requiring no coding expertise
• FedRAMP authorization for government compliance requirements
• 33,000+ AI agents already deployed across customer base

Boomi delivers AI agent capabilities built on proven integration expertise as the only vendor to be positioned as a Leader n 2025 in the Gartner® Magic Quadrant™ for Integration Platform as a Service (iPaaS) and the Gartner® Magic Quadrant™ for API Management.