The future of AI agents: Trends shaping tomorrow’s intelligent systems

The AI agent market is growing and according to MarketsandMarkets, it’s projected to expand from $5.25 billion in 2024 to $52.62 billion by 2030 – a nearly tenfold jump. This substantial growth reflects a fundamental shift in AI agents’ capabilities, from reactive assistants to proactive collaborators that can understand context beyond text, orchestrate other agents, and improve their reasoning, ultimately redefining how people, businesses, and machines interact.

At OneAdvanced, we believe the future of AI agents isn’t just about deploying these smart systems; it’s about cultivating an ecosystem of explainable, trustworthy and outcome-driven agents that address real business challenges. As organisations navigate this evolving landscape, understanding how AI agents have matured and where they’re heading next is crucial.

From assistants to autonomous teams: The evolution of AI agents

The evolution of AI agents mirrors how organisations themselves are evolving toward autonomy, adaptability, and continuous learning. At OneAdvanced, we define this journey through five key levels of maturity:                            

Level 1 – Narrow assistant

These early agents functioned as basic language processors, understanding only the immediate input without broader context. Think chatbots, search engines, or text summarisers that respond only to direct commands – they “understand what you say, and that’s it.”

Level 2 – Partially automated workflows

Here, agents were early-stage LLM-powered tools that used rule-based logic to automate repetitive, well-defined tasks, like drafting emails or scheduling meetings. They operated within narrow domains and needed clear instructions: “I can complete simple tasks, but only when told.”

Level 3 – Agentic AI

This is where autonomy begins. Given high-level instructions, agents at this stage can plan, reason, and execute tasks within set boundaries by leveraging APIs, tools, or web-browsing.

Level 4 – High autonomy systems

Agents at this level develop memory, learn from feedback, adapt strategies, and coordinate across multiple systems, tools, and humans to manage complex tasks. Imagine dynamic pricing systems that adjust prices based on market trends – “they plan and adapt continuously over time.”

Level 5 – Fully autonomous

This is the pinnacle, where agents are self-directed, independently set and reprioritise goals, adapt in real time, and pivot strategies based on evolving circumstances. They personalise responses and decisions by learning from user behaviours and feedback.

Top five trends shaping the future of AI agents

At OneAdvanced, we’ve identified five emerging trends that will shape the next era of AI innovation. Understanding these will help your organisations stay competitive, agile, and future-ready.

1. Multimodal intelligence: Agents will think beyond texts

The future of AI agents lies in the multimodal intelligence where agents can process multiple data types such as text, images, video, audio, and even sensory inputs simultaneously to see, hear, and interpret the world around them, just like humans do.

Real world impact:

• In healthcare, multimodal agents can read patient notes, scan images of diagnostic forms, match those with structured EMR data, and help clinicians with a complete picture of care.
• Retailers can leverage multimodal AI to analyse product images, search queries, and customer reviews to optimise product discovery and provide a more immersive and personalised customer experience.
• A customer support agent with multimodal intelligence can combine screenshots, chats, and voice data to resolve issues faster and drive higher satisfaction scores

2. Voice agents: Agents will respond to voice

Voice is becoming the most natural interface for human-AI collaboration. Imagine verbally asking an agent:

“Find all type 2 diabetes patients with elevated HbA1c, summarise lifestyle trends, and generate a summary report highlighting high-risk patients and suggested interventions.”

Voice agents, in this case, won’t just respond, they’ll understand the tone, sentiment, urgency, and intent of the request. By performing the above tasks, they offer real-time assistance to healthcare professionals, saving their time and effort.

Real world impact:

• In healthcare, these agents can guide patients through pre-diagnosis, appointment scheduling, and follow-up care, making it easier for them to access care while saving staff time and reducing friction.
• Retailers can use voice AI agents to provide product recommendations to their customers, handle product return issues, and improve customer experience.

3. Multi-agent systems: Agents will no-longer be isolated

With the projection to grow from $6.3 billion in 2025 to $184.8 billion by 2034, multi-agent systems are trending in the AI world. Unlike single agents that work in isolation, multi-agent systems consist of a team of specialised entities that talk to each other to solve complex workflow challenges, manage large-scale tasks, and deliver faster, more dynamic solutions.

Real-world impact:

• In e-commerce, a multi-AI agent system could include one agent handling customer enquiries, another managing inventory and a third optimising pricing based on-demand, all working together to improve efficiency and maximise sales.
• In the global supply chain, multiple AI agents can work together to coordinate logistics, source materials and manage distribution, keeping everything running smoothly.

4. RAG agents: AI agents will have better reasoning

Imaging AI agents that don’t just generate answers; they think, perform real-time research, and deliver insights tailored to users’ need. That’s the promise of Retrieval Augmented Generation (RAG) agents. These intelligent tools combine the generative capability of AI with real-time search functionality to make their knowledge up-to-date and reasoning visible at every steps.

Real-world impact:

• Legal and compliance teams can use RAG agents to track policy updates, summarise new regulations, and guide organisational responses.
• Technical support teams can resolve customer problems by accessing the most recent internal resources and documentation.

5. Deep Research Agents: Agents will perform multi-step research

Deep Research Agents take AI reasoning to new depths. They don’t just skim the surface; they expertly plan and execute complex research-based tasks, analyse information from various online sources, and combine their findings with precision and clarity. By automating demanding investigative work, they deliver transparent, traceable results and orchestrate multi-step workflows by collaborating with other agents and digital tools.

The road ahead

In conclusion, the future of AI lies not in replacing human intelligence, but in amplifying it, creating systems that are explainable, adaptive, and aligned with human goals. As we move ahead, our vision is clear: to shape AI that empowers organisations to streamline processes, improve efficiency, and achieve their business goals.

As this future takes shape, the question for every organisation becomes: how can you turn AI’s potential into measurable performance?

Watch our latest webinar, “From Potential to Performance: Putting OneAdvanced AI to Work” and learn how forward-thinking organisations are leveraging our Agentic AI to solve real-world challenges.