The rise of agentic AI

From tools to teammates

Agentic AI marks a new era where artificial intelligence systems act as collaborative partners, capable of learning, decision-making, and independent action.

With 33% of organisations now deploying AI agents (a threefold increase in six months) these systems are rapidly evolving from experimental tools to core enterprise assets.

Leadership insight

AI is a dynamic asset that requires ongoing investment in platforms and teams to support its continuous evolution, not just one-off deployments.

To succeed and avoid high failure rates, you need strong governance and clear ROI metrics from the start.

AI agents

'The evolution from thought to autonomous action' - a blog from Nathan Marlor, Head of Data & AI, Version 1.

The future contains smart, autonomous assistants that can understand your needs and handle the tasks for you. It’s a future where AI agents are not just passive responders but active participants in our digital lives. Instead of asking for answers, we’re asking them to take action.

Illustrating AI’s progression, this diagram highlights the shift from fixed models to self-optimising agents capable of continuous learning and integration

STEP 1

Static model deployment

AI models are trained once and deployed with fixed capabilities. Updates require manual retraining and redeployment, creating operational bottlenecks.

STEP 2

Real-world interaction

Models encounter new data, user feedback, and changing business requirements in live environments, revealing limitations of static approaches.

STEP 3

Self-editing and adaption

Next-gen agents (e.g., SEAL) can autonomously update their own code and behaviour, learning from new challenges and outcomes.

STEP 4

Multiagent collaboration

Platforms like BeeAI enable modular AI agents to work together, share knowledge, and solve complex problems collectively through standardised protocols.

STEP 5

Dynamic discovery and integration

Agents use registries (e.g., MCP) to connect to new tools and services, expanding their capabilities on demand.

STEP 6

Continuous optimisation

Adaptive architectures (e.g., Mixture-of-Experts, ParetoQ) refine performance and efficiency post-deployment, ensuring ongoing business value.