As artificial intelligence (AI) continues to advance, a key trend shaping the technological landscape in 2025 is “agentification.” Highlighted in Capgemini’s TechnoVision 2025 report, agentification represents a shift in AI from performing isolated tasks to operating as interconnected and autonomous agents.
This evolution is expected to transform industries by improving efficiency, decision-making, and adaptability.
What is agentification?
Agentification refers to the development of AI systems that function as independent agents rather than mere tools for singular tasks. These AI agents are designed to interact, collaborate, and manage increasingly complex processes with minimal human intervention.
Unlike traditional AI applications that require direct input and oversight, agentified systems operate autonomously, leveraging advanced reasoning and learning capabilities.
One of the most significant advancements in this area is the emergence of superagents, AI systems that orchestrate multiple models to optimise processes. These superagents act as decision-makers, integrating insights from different AI models to create a more cohesive and responsive technological ecosystem.
The move towards autonomous AI agents is driven by several tech advancements, particularly in generative AI (Gen AI). These improvements enable AI to take on responsibilities previously managed by humans, such as predictive maintenance, supply chain management, and risk assessment. The ability to provide “reliable, evidence-based outputs” is a key factor in making these agents viable for real-world applications.
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Multi-agent systems are also becoming more adept at complex and dynamic decision-making. By leveraging transformer models and other advanced AI architectures, these systems can process vast amounts of data, recognise patterns, and react to unpredictable situations.
This makes agentification particularly valuable in industries that require real-time responses, such as healthcare, finance, and legal services.
Industry impacts
Agentification is poised to revolutionise various industries by introducing AI-driven decision-making and automation. Some of the key sectors expected to benefit include:
Healthcare
AI agents can enhance diagnostics, monitor patient health, and assist in personalised treatment plans, reducing the burden on medical professionals.
Financial services
AI-driven agents can manage fraud detection, risk assessment, and automated trading, improving efficiency and security.
Supply chain management
AI agents can optimise logistics, predict disruptions, and streamline operations, increasing resilience and agility.
Cybersecurity
AI-enhanced cyber defences can detect and respond to threats in real-time, mitigating risks associated with Gen AI-driven cyberattacks.
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The rise of superagents
A survey by the Capgemini Research Institute found that 32 per cent of top executives consider AI agents the leading tech trend in data and AI for 2025. This underscores the growing recognition of agentification as a transformative force in business operations.
Furthermore, a crucial development within agentification is the emergence of superagents. These systems go beyond individual AI agents by coordinating multiple AI models to optimise performance and decision-making. Superagents act as orchestrators, integrating insights from different AI sources to provide a comprehensive and efficient solution.
This multi-layered AI approach allows businesses to scale applications more effectively, reducing inefficiencies and enhancing productivity.
Superagents will likely play a central role in enterprise AI ecosystems, facilitating seamless interaction between different AI models and automating complex workflows.
Despite its potential, agentification comes with challenges that must be addressed to ensure responsible implementation:
Reliability and accountability
As AI systems become more autonomous, ensuring the reliability and accuracy of their outputs remains a priority. Mechanisms for error detection and human oversight must be integrated into AI agent frameworks.
Ethical considerations
With AI making more decisions independently, ethical concerns around bias, privacy, and decision transparency must be carefully managed.
Security risks
As AI systems become more interconnected, they also become more vulnerable to cyber threats. Companies will need robust cybersecurity measures to protect AI ecosystems from potential attacks.
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AI’s broader influence in 2025
Beyond agentification, AI is expected to drive several other key technological advancements in 2025:
Cybersecurity
AI is both a threat and a defence mechanism, with more sophisticated AI-driven attacks emerging alongside advanced AI-powered security solutions.
Robotics
AI is improving robotics, enabling them to adapt to various environments and work alongside humans more efficiently.
Energy industry
The massive energy demands of AI are driving investments in alternative energy sources, particularly nuclear power.
Supply chain innovation
AI, in combination with blockchain, IoT, and satellite connectivity, is improving predictive analytics and operational efficiency.
The rise of Artificial General Intelligence (AGI) is also a topic of increasing discussion. Predictions suggest that AGI may reach commercial viability by 2030, with 60 per cent of top executives and venture capitalists believing in its maturation within this timeframe. This indicates a broader shift towards AI systems that can reason and operate beyond narrow applications.
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