Maximizing Agentic AI: Balancing Potential with Pragmatism
Eden Zoller, Chief Analyst, Applied AI
This has generated excitement but also massive hype that creates false expectations as to what agentic AI can and cannot achieve.
Omdia’s definition of agentic AI and components provides a framework for understanding and assessing agentic AI, and vendor offerings in this space.
Omdia’s framework also clarifies the relationship between agentic AI and generative AI (GenAI), and traditional forms of robotic process automation (RPA).
Understanding differences between these technologies clarifies how they can complement each other, and what is unique about agentic AI.
This is crucial for designing effective AI strategies, from both a vendor roadmap and marketing perspective, and for enterprise deployment strategies.
Agentic AI is not an application or single technology but rather an architecture that integrates a range of technologies, techniques and components to create goal-oriented AI agents that are highly autonomous.
AI agents use foundation models and advanced Machine Learning (ML) techniques (e.g., chain-of-thought (CoT) reasoning), memory modules and external tools to proactively plan and execute complex tasks independently or with little human intervention.
A significant development in agentic AI is multi-agent systems where specialized agents operate within a coordinated framework to achieve common goals.
Multi-agent systems enhance autonomy through parallel task execution and distributed workloads, offering greater adaptability and resilience.
Enhance business intelligence
Enable scalable automation
Create adaptive workflows
Provide intelligent orchestration across complex processes
For vendors, agentic AI presents opportunities to extend existing GenAI and RPA capabilities into comprehensive, full-stack solutions spanning from content generation to autonomous execution.
This positions vendors to pursue larger, more complex deals with deeper enterprise integration and greater customer dependency.
However, agentic AI introduces significant complexity, magnifying existing GenAI challenges while introducing new ones.
Agentic AI can place greater demands on infrastructure, requiring integration with internal systems, external environments, tools, and data sources.
The highly autonomous nature of agentic AI also raises critical concerns relating to reliability, transparency, and security.
For successful implementation, enterprises should avoid adopting agentic AI indiscriminately, instead focusing on use cases where its capabilities offer genuine advantages over simpler solutions.
Organizations with lower AI maturity can leverage pre-configured, domain-specific agents or low-code platforms, while ensuring robust governance frameworks, continuous monitoring, and strong security measures.
Vendors should incorporate feedback mechanisms allowing agents to seek human input when facing uncertainty, enable comprehensive data observability, implement robust verification processes, provide explainability tools, and develop sophisticated multi-agent orchestration solutions to maximize impact and customer appeal.
Figure 1: Defining attributes of agentic AI
Source: Omdia
Content in this e-book is derived from four Omdia intelligence services:
Workplace Transformation Intelligence Service
Advanced Computing Intelligence Service
AI Applications Intelligence Service
Managed Security Services Intelligence Service
