CLOSE
CLOSE
https://www.sikich.com

The Current State and Future of Agentic AI: Insights and Innovations

Agentic AI, or AI systems capable of autonomous decision-making and task execution, has become the latest buzzword in the AI landscape. But what exactly does it mean, and why is everyone talking about it?

Generative AI captured the world’s imagination with tools like ChatGPT, demonstrating the power of AI to create new content based on a simple prompt. Now, the evolution to agentic AI takes it further—beyond providing answers to performing actions based on those answers and even orchestrating a series of actions.

Imagine a system that continuously monitors a marketing campaign, learns from the results, and automatically adjusts strategies without human intervention. Or picture a supply chain module in an ERP system autonomously placing orders for low-risk products to maintain optimal inventory levels.

You may wonder, “That sounds great! Have I already missed out?” The answer is a resounding no. We are in the early stages of this transformative technology. Even Gartner predicts that by 2028, only up to 15% of day-to-day workflows will be handled autonomously by AI agents.

The Levels of Agentic AI: A Framework for Understanding

Agentic AI spans several levels, each reflecting a different degree of autonomy and sophistication in how AI systems act and learn:

  1. Reactive Agents
    Reactive agents operate on a simple sense-and-respond model, reacting to current inputs without memory or planning.
    Examples: Basic chatbots, thermostats.
  2. Proactive Agents
    Proactive agents go beyond reacting by learning from past experiences and planning limited actions to achieve goals.
    Examples: AI scheduling tools, recommendation systems.
  3. Adaptive Agents
    Adaptive agents learn dynamically from their interactions, optimizing responses and strategies in complex environments.
    Examples: Self-driving cars, adaptive CRM tools.
  4. Fully Agentic Systems
    Fully autonomous AI systems independently set goals, make decisions, and perform tasks in unstructured environments.
    Examples: Experimental platforms like Microsoft’s Magentic-One.

Each level builds upon the last, reflecting the growing capacity of AI to act autonomously and intelligently.

The Current State of Agentic AI

Agentic AI is making its mark across industries, transitioning from experimental tools to practical applications. Current systems excel at:

  • Automation at Scale: Reducing the need for human input in repetitive processes.
  • Proactive Decision-Making: Solving problems and making recommendations in real-time.
  • Personalization: Tailoring actions based on contextual understanding.

Magentic-One by Microsoft

Microsoft’s Magentic-One is a standout example, showcasing the power of agentic AI. This currently experimental platform integrates with Microsoft’s enterprise ecosystem to:

  • Automate complex workflows using advanced task orchestration.
  • Provide real-time, context-aware assistance.
  • Continuously improve through self-learning systems.

Future Possibilities of Agentic AI

As agentic AI evolves, its potential will expand into even more advanced applications:

  1. Enhanced Enterprise Autonomy: AI agents that don’t just execute but also strategize and optimize processes.
  2. Cross-Domain Integration: Bridging fields like healthcare and legal research for deeper problem-solving.
  3. Human Collaboration: AI as a proactive partner, collaborating seamlessly with human teams.

These advancements could revolutionize areas such as autonomous logistics, predictive crisis management, and smart city design.

Risk and Mitigation Strategies

Agentic AI, while promising, introduces several risks:

  • Ethical Concerns: Decisions may conflict with our values or norms.
  • Skewed Decision-Making: Flaws in training data can foster errors.
  • Security Threats: Vulnerabilities to adversarial attacks or misuse.
  • Loss of Oversight: Over-reliance on autonomy risks human control and understanding.

Mitigation Strategies:

  • Build transparent, explainable systems for decision-making.
  • Continuously test and make corrections as you go.
  • Implement robust security measures.
  • Maintain human-in-the-loop systems to oversee critical decisions.

Realize the Power of Today’s Agentic AI

Agentic AI represents a dramatic shift in how technology interacts with humans and the world. While the future is brimming with possibilities, the tools and capabilities available today are already game-changing.

Platforms like Microsoft 365 Copilot and Salesforce Einstein demonstrate advanced AI capabilities, offering proactive assistance, workflow automation, and contextual recommendations. However, these tools represent an early stage of agentic AI. They excel at enhancing productivity and decision-making but remain heavily reliant on user direction and input, stopping short of true autonomy. These systems are building blocks toward fully agentic solutions, offering organizations an opportunity to start leveraging AI for impactful results today.

At Sikich, we specialize in helping organizations define, build, and integrate these intelligent agents into their existing technology landscapes. Whether you’re looking to extend the functionality of Microsoft 365 Copilot, leverage the power of Salesforce Einstein, or enhance Dynamics 365 products, our expertise ensures a seamless transition from concept to implementation.

Don’t wait for the future—start your journey with agentic AI today. Let Sikich guide you in transforming your vision into reality, unlocking the potential of these powerful tools to drive innovation and efficiency in your organization.

Contact us to explore how the Sikich AI Solutions StudioTM can partner with you in building intelligent, autonomous solutions tailored to your needs.

This publication contains general information only and Sikich is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or any other professional advice or services. This publication is not a substitute for such professional advice or services, nor should you use it as a basis for any decision, action or omission that may affect you or your business. Before making any decision, taking any action or omitting an action that may affect you or your business, you should consult a qualified professional advisor. In addition, this publication may contain certain content generated by an artificial intelligence (AI) language model. You acknowledge that Sikich shall not be responsible for any loss sustained by you or any person who relies on this publication.

About the Author