Agentic AI: Where Machines Pursue Goals, Not Just Commands

As artificial intelligence continues to evolve, we are entering a new era—not defined by static automation, but by dynamic, autonomous intelligence. In this interview, Mostafa Kabel, General Manager, Solutions & Services at Mindware, shares how Agentic AI is reshaping enterprise operations by empowering machines to pursue goals, adapt strategies, and make independent decisions.

What Can Agentic AI Solve That Traditional AI Can’t?

Agentic AI unlocks capabilities beyond the reach of narrow, task-specific models. Its dynamic and goal-oriented nature makes it ideal for solving complex, multi-step problems across industries.

Examples include:

• Autonomous IT operations: Diagnose and resolve issues end-to-end with no human input.
• Supply chain optimization: React to disruptions in real-time and adapt logistics autonomously.
• Customer experience management: Identify friction points proactively, personalize responses, and coordinate resolutions across teams.

“These are not workflows you can script,” Kabel notes. “They demand adaptive intelligence.”

Balancing Autonomy: When Should Humans Stay in the Loop?

With greater autonomy comes the question: how much control should AI have?

“Context is key,” Kabel emphasizes. “Routine, low-risk activities can be fully automated to improve efficiency. But high-stakes or ethically sensitive decisions must remain human-driven.”

A practical model? “AI proposes, human approves.” Agentic AI can forecast risks, generate options, and manage execution, but the final call—especially for strategic decisions—should stay with humans.

Infrastructure Challenges in Scaling Agentic AI

Scaling Agentic AI comes with significant IT infrastructure demands. These systems require persistent compute power—often GPU-based—and generate increased energy and cooling requirements.

Key challenges include:

• Compute and memory: High-performance architectures are needed for multi-agent processing.
• Latency: Especially critical for real-time use cases like smart retail or automation.
• Scalability: No one-size-fits-all model—deployments must support simultaneous agents across locations.

Mindware is actively investing in composable infrastructure solutions and building partnerships with leading AI vendors to ensure scalability, flexibility, and efficiency.

The Future of Agentic AI: Collaboration Between AI Agents

The next frontier? Multi-agent collaboration.

Just like human teams bring diverse skills to the table, AI agents will begin working together: one agent handling research, another planning, a third executing. The result? Fully autonomous teams tackling end-to-end workflows.

“In marketing, for instance, agents could run entire campaigns—one writing copy, another segmenting audiences, another analyzing results,” says Kabel. “In engineering, agents might co-design products and optimize supply chains in real time.”

This vision is rapidly moving from fiction to reality.

Will Open-Source Keep Up with Proprietary Agentic AI Platforms?

There’s an ongoing debate about whether open-source models can keep pace with proprietary Agentic AI platforms. While open-source has fueled experimentation, Kabel points out that Agentic AI is about more than model size—it’s about orchestration, memory, safety layers, and multi-agent communication.

Proprietary platforms currently lead in those areas, but hybrid models may emerge, combining open-source cores with proprietary orchestration layers. Ultimately, the race will be about trust, control, and interoperability—not just raw capability.

Conclusion

Agentic AI marks a turning point in enterprise technology. By shifting from reactive automation to proactive, goal-oriented intelligence, businesses can reimagine how work gets done. As infrastructure evolves and AI agents become collaborators—not just tools—the future is no longer theoretical. It’s already in motion.