AI Agents: What are they, and what can they do for my business?

AI Agents are autonomous, goal-driven software systems that perceive their environments, decompose complex objectives into sequenced tasks and execute those tasks across digital platforms with minimal human intervention.

An example of a basic "AI Agent" Workflow Build

Integrating large language models (LLMs) with planning engines, memory modules and Retrieval-Augmented Generation (RAG), agents ground their reasoning in up-to-date knowledge bases, improving accuracy and contextual relevance.

“We’re going to operate in a multiagent world,” says Qualtrics’ Brad Anderson—highlighting how specialised agents can collaborate across departments to turn feedback into real-time actions

What are AI Agents

AI Agents are a big step up from traditional, rule-based automation, toward a truly autonomous system capable of dynamic decision-making and continuous learning. Unlike simple chatbots that follow predefined scripts, intelligent agents can perceive varied inputs—from API responses to unstructured documents—interpret high-level goals.

How AI Agents Work

When an organisation launches an AI Agent, the process typically unfolds as follows:

1. Define Goals: Stakeholders articulate clear, measurable objectives (e.g., “Resolve 90 % of support tickets autonomously”) to guide agent prioritisation

   
   

2. Sense the Environment: Agents fetch and preprocess data from internal systems (CRM, ERP), external APIs and document repositories to build contextual awareness

   
   

3. Decompose Tasks: The planning engine uses LLM reasoning to split the overarching goal into concrete steps, such as “retrieve invoice,” “draft response” and “update the ticket”

   
   

4. Execute Actions: Agents perform the subtasks via function calls or UI automation, interfacing with email systems, databases or third-party services without manual coding

   
   

5. Monitor & Learn: Ongoing monitoring captures performance data—success rates, latency, user satisfaction—which feeds back into memory modules and model training pipelines for optimisation

A more complex Workflow, with multiple AI Agents operating together

Key Use Cases of AI Agents

AI Agents unlock value across a range of enterprise functions by automating complex, multi-step workflows:

• Customer Service: Agents handle routine queries over chat, email and SMS, personalise responses using prior interaction data and escalate novel cases to human specialists, reducing average handle time.

  
  

• IT Operations & Cybersecurity: Continuous log analysis by agents enables real-time anomaly detection, automated incident triage and even remediation steps like credential resets or endpoint isolation.

  
  

• Supply-Chain Orchestration: By integrating demand forecasts, IoT sensor feeds and supplier systems, agents automate procurement, predict disruptions (e.g., weather, port congestion) and dynamically reroute shipments, reducing stock-out events.

  
  

• Research & Development: In pharmaceuticals and materials science, agents automate literature reviews, experimental design and draft reporting, compressing weeks-long research cycles into days

  
  

• Human Resources: AI Agents streamline onboarding by filling forms, scheduling training and answering policy questions, freeing HR teams to focus on strategic initiatives and improving new-hire satisfaction.

  
  

How AI Agents will Benefit Your Busienss

Adopting AI Agents delivers a blend of operational and strategic advantages. Organisations gain scalability, as each agent can serve thousands of interactions in parallel without degradation in quality. They achieve cost efficiencies by offloading repetitive tasks—leading to reduced headcount requirements in back-office functions—and 24/7 availability, ensuring continuous business operations across time zones .

Importantly, agents preserve institutional knowledge by codifying best practices into their memory stores, mitigating the impact of talent turnover. Finally, continuous feedback loops drive progressive improvements in accuracy and speed, creating a self-optimising automation platform.

AI Agents bridge the gap between simple automation and fully autonomous workflows, enabling enterprises to streamline complex operations, scale expertise and operate continuously.

Start with targeted pilots, ensuring data integrity, selecting the right technological foundation and embedding robust governance, organisations can harness AI Agents to drive cost savings, elevate customer and employee experiences and accelerate innovation. As Agentic AI matures, decision-makers who embrace this paradigm shift will secure a lasting competitive advantage in an increasingly digital landscape.