In a Shift of Digital Landscape: Agentic Applications Emerging as Modern Counterparts to Traditional Apps
Towards the AI-Driven Workforce: A Look at Real-World AI Agents Revolutionizing Business
In the rapidly evolving tech landscape, AI agents are stepping into the spotlight, promising to reshape the way businesses operate. As Dharmesh Shah of HubSpot puts it, we might be moving towards an era where AI agents are the new apps. Here's a glimpse into the impactful AI agents making a difference in enterprises today, and the basic architecture that powers them.
Changing the Game: AI Agents in Action
While designing decisions dominate the headlines, real organizations are already embracing the AI maturity curve, shifting from experimentation to deployment. It's not just about what AI agents can do; it's about how broadly they're being deployed across the enterprise. Below are some of the most impactful AI agent applications today:
1. Software Development Lifecycle (SDLC): Boosting Efficiency and Compliance
AI agents are integrating into the SDLC, reshaping how teams build, test, and maintain applications. The primary objectives are to accelerate delivery cycles and minimize human error. Key areas of agentic application include access & infrastructure management, automated testing & provisioning, code generation, and end-to-end development orchestration.
2. Customer Service: AI Agents as Brands' Extensions
AI agents are making a mark in the customer service field. From retail associate training to personalized gift recommendations, they're handling tasks across the customer journey, reducing burden on service teams and improving responsiveness.
3. Back-Office Operations: Going Leaner, Smarter
Long-burdened back-office functions, plagued by manual processes, are benefiting from AI agents that focus on reducing error, accelerating routine operations, and avoiding overhead. Key areas include inventory management, automated invoicing, document analysis, market behavior forecasting, and autonomous auditing.
4. Data Analysis and Compliance: Insights, Simplified
Powerful AI agents are enhancing the ability to interpret voluminous data via a combination of AI, machine learning, and automation. The result? A faster, trusted way to simplify complex data analysis and stay ahead of regulatory demands.
5. Operational Efficiency: Streamlining with Minimal Human Input
From IT operations to executive decision-making, AI agents are handling routine, time-sensitive tasks to free up valuable human time. They focus on network diagnostics, executive reporting, and more.
Enterprise Agent Architecture: Built for Orchestration, Reasoning, and Execution
To successfully deploy AI agents in the enterprise, a foundational architecture designed for orchestration, reasoning, and secure execution is essential. The core components and requirements of an enterprise-ready AI agent system include a user interface, reasoning layer, agentic flow orchestration, deterministic runtime, and APIs.
In this new era, where AI agents become true operational partners embedded in the fabric of the business, it's time to rethink our foundational architecture for an integrated, end-to-end ecosystem. This transformation also calls for collaborative professionals who bring deep domain knowledge and know-how to work seamlessly with AI for maximum output.
At Inclusion Cloud, we're committed to helping companies create the AI foundation for the modern enterprise. Whether it's rethinking architecture, integrating business platforms, or embedding agentic capabilities across operations, we're your partner in building the future.
Let's connect and shape the enterprise of tomorrow, together.
Additional Resources
- The State of the AI Agents Ecosystem
- Suite-as-a-Service: The New Standard for Enterprise AI
- The Most Important Design Decisions When Implementing AI Agents
- Enterprise AI Demands a Platform Shift-Are You Prepared?
- Are AI Agents Apps the Next Business Users?
- Choosing Between Open-Source LLM & Proprietary AI Model
- If AI Can Write Code, What's Left for Developers?
- AI Is Changing How We Code. But Is Technical Debt the Price Tag?
- AI Model Training: Is Your IP at Stake?
- Reinforcement Learning: Smarter AI, Faster Growth
- AI Roles: Who Do You Really Need for Implementing AI?
- Enterprise AI Security Risks: Are You Truly Protected?
- What Are Multiagent Systems? The Future of AI in 2025
- What Is SaaS Sprawl? Causes, Challenges, and Solutions
- Is Shadow IT Helping You Innovate-Or Inviting Risks You Don't Need?
- Given the AI maturity curve, businesses are not only focusing on what AI agents can do, but also on how they can be deployed broadly across the enterprise, including in finance for minimizing human error and investing decision-making.
- As AI agents are embedded in the business fabric, it's essential to understand the policy and training implications, as seen in the customer service sector where AI agents are extending brands by handling tasks, such as retail associate training and gift recommendations.
- To capitalize on the potential of AI agents in the technology domain, businesses need to consider not just agentic capabilities in software development lifecycle (SDLC) but also the financing and investing perspectives, aiding in streamlining operations and sustainably scaling AI adoption.