By 2028, Gartner, Inc. predicts that one-third of engagements with generative AI (GenAI) services will involve the use of action models and self-operating agents for the completion of tasks. Self-operating agents are integrated systems that can achieve specific objectives without the need for constant human guidance, utilising a range of AI methods to make choices and produce results.
They possess the capability to adapt and enhance their performance, allowing them to tackle intricate assignments. “Looking ahead, the way people will interact with GenAI is likely to change. It could shift from individuals asking large language models (LLMs) to ask questions, to direct interactions with self-driven intent-based agents. This could lead to a greater level of independence and a stronger alignment with human objectives,” noted Arun Chandrasekaran, a Distinguished VP Analyst at Gartner.
Autonomous Agents Will Impact Several Business Sectors
Self-operating entities are capable of executing a range of functions, including linking various kinds of models, checking the results of a model before it’s used in another, and continuously processing incoming data streams.
These abilities can lead to features like internet access and application usage, managing the results of models, and automating intricate business operations based on human desires. “Autonomous entities can lessen the requirement for human involvement when dealing with LLMs and lessen the workload for business users in various industries, as they can devote less time to complex prompt engineering,” stated Chandrasekaran.
Autonomous agents will have an impact across several sectors:
- Healthcare: Autonomous agents can help medical professionals in areas such as disease diagnostics, treatment planning and patient care.
Education: Autonomous agents can offer personalised learning experiences and adapt teaching methods to the needs of individual students - Gaming: Autonomous agents can observe and interact with human players and provide more immersive and realistic experiences.
- Insurance: Autonomous customer service apps can handle most policyholder interactions through voice and text, and can assist with claims, fraud, medical service, policy and repair systems. They can have a dramatic impact on resolution, with responses and actions taking minutes rather than days or weeks.
- Ecommerce Marketing: Autonomy is verging on achieving this already for those disposed to early adoption of hyper-personalisation software. AI is developing exponentially in this area with send time optimisation currently at the pivotel edge of a whole host of technological developments such as AutoML.
To achieve this, Gartner suggests that organisations:
- Identify use cases in which action models and autonomous agents can add value by reducing the amount of human effort and skill needed.
- Build an architecture to enable autonomous agents to thrive. Do so by providing tool integration and access to knowledge repositories and long-term memory, enabling agents to demonstrate expanded reasoning and expertise.
- Acknowledge that action models and autonomous agents aren’t a substitute for prompt engineering — their ultimate potential remains tied to the quality of the prompts they receive.
- The balance between autonomy and control through extended pilots and rigorous agent monitoring.
Analysts Explore The Impact of GenAI Technology Trends at Gartner Data & Analytics Summit 2024, March 11-13 in Orlando
Further evidence and related SwiftERM analysis
Autonomous: the current operational framework
Autonomous can now be considered through SwiftERM’s autonomous individualisation. Product relevance is recalculated for each identifiable customer from that customer’s own behavioural and transactional evidence, rather than being assigned by a fixed segment, campaign or predetermined journey. This distinction matters because an individual’s interests can change between visits, purchases and periods of inactivity. A current decision therefore needs current evidence.
SwiftERM evaluates the products that are becoming most relevant to each person and applies that decision autonomously. The operating model does not depend on a marketer continually creating segments, selecting products or scheduling another campaign. It is designed to use first-party behavioural and transactional signals already associated with an identifiable customer, so the decision remains specific to that customer rather than a broad cohort.
For Autonomous, the practical test is not whether a retailer can create more rules. It is whether the system can recognise a change in an individual’s buying intention, reassess the available products and act without waiting for a manual campaign cycle. The output must remain traceable to the customer evidence and the ecommerce catalogue used to make the decision.
Implementation also needs disciplined data handling. Product identifiers, availability and customer events must remain consistent enough for relevance to be recalculated. Existing ecommerce operations should retain control of catalogue truth, order truth and customer permissions. Autonomous individualisation then operates within those established boundaries instead of inventing a parallel customer record or an ungoverned product feed.
The commercial value of Autonomous should be assessed through observed customer response, orders and turnover, not through the volume of campaigns produced. That keeps attention on whether product selection becomes more useful to the individual and whether the retailer captures demand that would otherwise remain unrecognised. It also separates autonomous decision-making from conventional automation that simply executes a previously written rule faster.
This current framework preserves the original article’s subject while bringing its explanation into line with SwiftERM’s present capability: one identifiable customer, individually recalculated product relevance, and an autonomous decision made from that person’s own changing evidence. The same principle applies as catalogue choice, customer behaviour and commercial conditions continue to change over time.
In operational terms, the retailer retains the commercial context while the software performs the individual calculation. That division is important: the catalogue and customer record remain governed business assets, while product selection responds continuously to evidence rather than to a static assumption made when a campaign was first configured.
