By Nina Schick, Author, Advisor, World-leading Authority on GenAI, and AI Council Member, Qlik
We are entering the Agent Systems Era. Now that AI has started to surpass human performance across benchmarks including image classification, visual reasoning, and English understanding, deploying agent-based systems that can independently execute tasks and adapt to feedback is both feasible and critical in unlocking economic value.
However, it is only possible by establishing data authenticity and taking steps to secure applied value. Together, they provide the foundation upon which agent systems can be deployed successfully. This foundation needs to be in place for companies to enjoy increasingly sophisticated agentic support in the future. Massive context windows, improved chat interfaces with text-to-action capabilities, and improved reasoning models will enable agents to solve problems on humans’ behalf beyond what most of us can grasp today.
Further, opportunities for agent systems cover both organisational and individual use cases. Corporate agents may become as much a face of business as a website or app, while all employees could soon access smart personal assistants, helping them in their daily roles. We are already seeing role-based agents emerge rapidly in industry-specific domain areas, but also in areas more widely applicable like programming and customer service.
Rise of AI agents
Are agents and multi-agent architecture going to become the de facto reality of dealing with complex workflows? Absolutely. As intelligence becomes more sophisticated, both by default and design, these agents will start working and competing with one another to undertake complex workflows. Unlike people, they are not going to get sick or tired.
By 2030, multi-agent architectures will not be revolutionary, it will be ordinary. Businesses, from Fortune 500 giants to two-person startups, will harness this intelligence at their fingertips.
Trends impacting the rollout of agent systems
Upcoming multi-agent architectures: Just as there are competing cloud environments and AI foundation models, expect to see multiple agentic architectures co-existing. Interoperability and avoiding vendor lock-in will be critical to realising the full potential of agentic reach and value. Some agents will be good at data integration, others at schema cleaning, text-to-SQL generation, automation, or building dashboards. Over time, these agents will learn to interact with one another. But humans must stay in the loop, or at least “over the loop”, for surveillance and governance.
Criticality of process intelligence and automation for agent-to-agent interaction: Bad processes that have been automated are still bad processes. In a world of autonomous agents process flows must be understandable, and do not look like spaghetti. Use process mining and analytics to optimise what workflows should look like. This will act as a highway for agents. Automations are then the vehicle of the agents on that highway, safely connecting applications, facilitating their talking to one another, driving agent-to-agent interactions and actions.
Real-time is crucial: Up-to-date data is critical to trust agents. A customer service agent cannot make a decision or give advice based on stale inventory data. Real-time data not only gives an immediate advantage but reduces the chance of it being stale, irrelevant, or incorrect. The need for real-time is leading to a substantial evolution in architecture. Technically, we are reaching an inflection point where ingestion and transformation of data can be done in real-time with hybrid transactional and analytical data that can be stored and processed in the same place.
Reimagining applications in the agent era: A world with agents forces us to re-think applications. Sometimes we do not need applications as agents can fetch the answers we need. Other times we will want to buy pre-packaged applications for expediency and domain-specific logic. The combination of text-to-action, large context windows, and agents will also enable us to build more apps in-house. As applications become more dynamic and intelligent, they will morph into alignment with our changing needs and learn from new data to deliver more personalised, predictive, and context-aware experiences.
To cut through the ambiguity, businesses must build authenticity, deliver applied value, and accelerate into the agentic systems era.