Assemble the tech you need to shift AI from tool to agent

The age of agentic AI is upon us—and with it, AI that can autonomously pursue complex goals. For AI to cross the threshold from tool to autonomous agent, enterprises must engineer a technological stack capable of balancing power with alignment and autonomy with ethics and control. Here’s our first look at the core technologies providing informed intelligence to align agency with human objectives.

Understanding the technology behind agentic AI will give you a straightforward understanding of how this new kid on the block can work for you. Exhibit 1 shows the capabilities you should expect of effective offerings in this space as firms such as Salesforce (with its Agentforce) and Microsoft (with the addition of AI agents to its Copilot suite) roll out solutions targeting business needs.

Exhibit 1: Your agentic AI environment must deliver foundational intelligence, safety, and adaptability

Vendors named are provided as examples only.
Source: HFS Research, 2024

Secure, scalable infrastructure underpins real-time decision-making at scale

Autonomous, goal-driven AI agents demand significant and robust computational support. Cloud and edge computing infrastructure enable agents to process data, make decisions, and take action in real time, even when handling massive data volumes.

Data privacy and security measures, from encryption to access control, protect sensitive information, ensuring that agentic AI can operate securely without compromising data integrity. Enterprises embracing agentic AI must build or access scalable infrastructure that meets the growing demands of real-time, data-intensive applications while safeguarding sensitive data.

Data management platforms serve as the intelligence infrastructure

Data management platforms (DMP) serve as the backbone of the agentic AI stack by providing the infrastructure, quality control, and context needed for AI agents. By integrating, managing, and enriching data, DMPs ensure that agents operate with the correct information, enabling them to make informed decisions and take actions that align with business goals.

In short, DMPs give agentic AI the “intelligence infrastructure” required to navigate complex data landscapes, ensuring accuracy, compliance, and actionable insights.

Large language and multimodal models are the cognitive core

Agentic AI’s “brain” is driven by large language models (LLM) and emerging multimodal models. These anchor the capability to comprehend, reason, and adapt. They need domain-specific fine-tuning for the precision that makes or breaks an agent’s relevance and reliability.

LLMs trained to integrate cross-domain data can adjust their “thought process” to match nuanced user needs, while multimodal models expand this cognitive toolkit. By simultaneously processing text, visual, and auditory data, agentic AI can interpret multi-sensory cues, interact fluidly across mediums, and refine understanding in real time.

We envision the development of industry process-specific LLMs, whether comprehensive LLMs with unique tuning or narrowly focused solutions. For example, solutions to manage travel and hospitality booking and changes must understand locations, complex fare and room policies, and rates and have end-to-end context based on a fully planned itinerary.

Reinforcement and adaptive learning build resilience in goal-driven agents

Agentic AI must continuously learn, adapt, and optimize. Reinforcement learning (RL) is central to this since it enables agents to refine decision-making based on reward structures that mirror strategic objectives. Continual and active learning empowers these systems to adapt dynamically to shifting goals, environments, and feedback loops, a necessity for organizations where adaptability is a competitive differentiator.

The value of your agentic AI will correlate directly with its ability to learn autonomously to enable the resilience required to evolve with minimal retraining.

Action-oriented interfaces bridge the digital and physical worlds

To deliver the greatest value, agentic AI must be free to execute tasks autonomously across platforms. APIs serve as the bridge, linking AI capabilities with applications, databases, and external systems to execute real-time decisions.

As agents move into real-world applications, interaction interfaces—whether robotic or sensor-based—will allow AI to interact and gather environmental data physically. These capabilities pave the way for agents that don’t just respond but actively shape digital or physical environments, achieving a new level of interactivity.

Specialized frameworks for agent interactions are emerging—such as Agent Protocol (which proposes a common interface designed to be tech stack agnostic) and Agent-to-Agent Communication Protocol (AACP), emphasizing the importance of standardized message codes and structures for agent interaction. However, neither eliminates the need for APIs, and the core principles of defining clear interfaces, data formats, and communication methods remain integral.

Autonomous planning and decision-making turn complexity into action

In complex and ever-evolving scenarios, agentic AI must be smart enough to break down tasks and prioritize actions—akin to human planning but accelerated by algorithms. With autonomous planning and decision-making engines, agents can sequence tasks in logical, goal-aligned orders.

Technologies such as causal inference enable them to anticipate the consequences of various actions, determining the most effective route to goal achievement. This capability isn’t just automation; it’s orchestrated action, providing high value in enterprise settings such as supply chain management, finance, and customer service.

User interaction and feedback systems cultivate a responsive, personalized AI experience

At the interface level, agentic AI relies on conversational capabilities that mimic natural human interaction for both usability and user acceptance.

These systems must capture real-time user feedback, adapting behaviors and responses based on evolving needs and preferences. Personalization technology enables agents to tailor responses and actions according to user history, enhancing the relevance of every interaction.

By integrating user feedback, agentic AI can become more responsive and attuned to individual user objectives, laying the foundation for genuine collaboration. Foundational to this experience is unlocking the stories buried deep inside data that explain the history, importance, and context for agents to act.

Knowledge graphs and ontologies connect the dots between understanding and action in complex contexts

Agentic systems need more than raw data—they need context. Knowledge graphs and ontologies provide structured information maps, grounding the agent’s responses in factual knowledge. For example, a knowledge graph could include interconnected entities such as patient attributes, symptoms, diseases, risk factors, treatments, diagnostic tests, treatment protocols, dosages, and side effects. An agentic AI equipped with this has the context for its analysis and decision-making. If, for example, a patient presents with chest pain, the agent can evaluate for a potential heart condition based on risk factors it is made aware of—such as age, family history, and lifestyle.

Agents maintain relevance even in fast-moving domains by continuously updating knowledge graphs and aligning responses with the latest developments. This dynamic integration delivers agentic AI’s credibility and adaptability, enabling systems to interpret relationships and execute contextually aware and situationally accurate actions.

Safety and alignment technologies provide frameworks for controlling risks

With agency comes risk, and alignment is no longer optional. Agentic AI must operate within ethical and safety frameworks to prevent harmful outcomes. Techniques such as Constitutional AI (an Anthropic-developed method for aligning general-purpose language models to abide by principles written into a constitution), reinforcement learning from human feedback (RLHF), and real-time guardrails ensure agents act consistently with human values.

Explainability and transparency tools provide critical insight into an agent’s decision rationale, which is essential where accountability is nonnegotiable. In fusing autonomy with control, agentic AI can support us as a trusted collaborator rather than an unpredictable risk.

The Bottom Line: Assemble technologies that deliver continuous adaptation while maintaining ethical alignment and safe execution.

Agentic AI is not simply an evolution of automation; it’s the beginning of a paradigm where machines actively shape outcomes in tandem with human intent. For enterprise leaders, the mandate is clear: adopt a layered, technologically rigorous approach that emphasizes ethical alignment, continuous adaptation, and safe execution.

By turning to solutions that include these critical components, enterprises will empower agents to work alongside humans, executing complex tasks autonomously and ushering in a new era of business capabilities grounded in AI-driven agency.