It’s incredibly cliché to say that artificial intelligence (AI) is moving quickly, but the last six months really have seen some serious evolution. If you're playing buzzword bingo in 2025, "Agentic AI" is undoubtedly the winning phrase. But this isn't just another incremental update; it represents a fundamental shift in what AI systems can achieve. This post lightly touches on this new landscape: what's happening, what it means, and how you can navigate it. It’s important to note that many of the statistics and recommendations in the AI space come from the companies building the technology, so a healthy dose of critical thinking is always advisable.
The changing world
To understand where we're going, you first need to grasp the sheer scale of what's happening now. The May 2025 report on Artificial Intelligence Trends by Mary Meeker and Bond Capital paints a vivid picture of a sector in overdrive:
- Unprecedented user adoption: Generative AI tools have achieved mass adoption faster than any previous technology, including the internet and smartphones.
- Soaring infrastructure investment: Top tech giants (Apple, NVIDIA, Microsoft, Alphabet, Amazon, Meta) spent a combined $212 billion on capital expenditures in 2024, a huge portion of which was dedicated to AI infrastructure like data centres and custom silicon.
- Shifting cost dynamics: The cost to train a state-of-the-art foundation model remains astronomically high, somewhere in the hundreds of millions of dollars. However, the cost to use these models (the inference cost) is plummeting, making AI more accessible than ever before.
- Intense competition and rapid imitation: AI is boosting productivity and driving competition between products.
- Global AI "space race": Nations are treating AI supremacy as a strategic imperative, leading to significant government investment and policy-making, particularly in areas like the semiconductor supply chain, with the US, Europe, and China all building new fabrication plants.
With this level of investment and adoption, can you confidently say this is a bubble about to burst?
Sir Demis Hassabis, CEO of Google DeepMind, puts this huge change on the same magnitude as the industrial revolution and the launch of the internet. Data from Gartner supports this, suggesting that by the end of 2025, 39% of organizations worldwide will have moved into the experimentation phase of AI adoption.
The shift is well and truly on.
What does AI look like in 2025?
AI is underpinned by machine learning models, which are trained, not programmed.
Engineers feed them vast amounts of data, and they learn patterns, concepts, and relationships. Different types of models are used for different purposes, such as those specialising in human language interactions (large language models, LLMs) and artwork generation (diffusion models).
When using AI systems, such as chatbots, you’re not interacting with the model directly but rather with additional software that uses the model as its “brain”. This allows you to implement guardrails to check user inputs and model outputs, helping to filter out harmful or inappropriate content.
Modern AI systems are rarely just a wrapper around a model. They integrate with other tools and services to enhance their capabilities, such as searching the web for real-time information or accessing private company documents to provide context-specific answers.
The year of agentic AI
An AI agent is a system that can autonomously pursue a goal. Instead of responding to a single prompt, it can reason, plan, and execute a series of steps to accomplish a complex task. It can also decide which tools to use and in what order. An AI agent may still be a chatbot or run constantly in the background.
Big tech companies are adamant that agentic AI is the next evolution, with Google, Amazon, and Microsoft all predicting the next wave of innovation over the next two years.
A key catalyst for this explosion was the release of the open-source Model Context Protocol (MCP) by Anthropic in late 2024. MCP provides a standardized way for AI models to discover and use tools. As the official documentation puts it:
"Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals... MCP provides a standardized way to connect AI models to different data sources and tools."
MCP has been a game-changer, dramatically simplifying the process of giving AI systems new capabilities and accelerating the move from AI systems that know things to AI systems that do things.
It’s no coincidence that technology companies then started to release their guides for building AI agents following MCP’s release – with Microsoft, Google, Cloudflare, OpenAI, and Amazon following close behind.
Technology to watch
Finally, a few key technologies that will define the next phase of AI include:
Model Context Protocol (MCP)
Continue to watch this standard. As more tools and platforms adopt MCP, the ecosystem of "plug-and-play" capabilities for agents will explode, as will the security risks.
Simon Willison puts it perfectly by describing a “lethal trifecta”. AI systems with access to private data, the ability to communicate externally, and exposure to untrusted content could easily lead to serious consequences.
Source: Simon Willison
Authorisation for AI systems
As agents move from knowing things to doing things (e.g., booking travel, purchasing supplies, modifying code), security becomes paramount. We need robust authorisation.
This will involve human-in-the-loop (HITL) approvals, likely powered by modern authentication standards like Client-Initiated Backchannel Authentication (CIBA), which can send a push notification to a manager to approve an agent's action.
Thought leaders from Microsoft suggest an overhaul to OAuth, with agentic systems having their own distinct identities and security considerations.
One thing’s for sure: proper authorization is complex – difficult to get right and catastrophic to get wrong.
Agent-to-agent communication
Current AI agents are specialized for a specific purpose, but next-generation AI functionality comes through the use of multi-agent systems, which can be deployed in a variety of architectures, such as hierarchical or swarms.
How agents communicate with each other, share memory, and share capabilities is still in its relative infancy, especially when AI agents may be hosted independently and written with different frameworks.
Two competing protocols are emerging: Google's Agent2Agent protocol and IBM’s Agent Communication Protocol (ACP). It's too early to call a winner, but the development of a standard here will be a major milestone.
We are at the beginning of the agentic era. 2025 is the year for experimentation. It's time to move from simply using AI to actively building with it, automating the tedious, and unlocking new forms of creativity and productivity.
Getting the most out of AI
If one thing’s for sure, it’s that the AI landscape is moving fast. So it’s crucial that you and your organisation are at the forefront of AI developments and making the most out of the latest technologies.
Keep your eyes peeled for brand new labs in this space coming very soon!
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