What are AI Agents?

AI agents are intelligent assistants that go beyond traditional automation. Instead of following rigid scripts, they understand context, make decisions within defined boundaries, and work collaboratively with other agents or systems to complete tasks from start to finish.

In simple terms, they're designed to think a little more like people do — interpreting intent, using information from multiple sources, and adjusting their actions depending on what's needed. Unlike chatbots, which often rely on pre-programmed answers, AI agents can plan, act, and even coordinate with other agents to get things done.

This makes them ideal for complex environments — from HR and finance to IT and customer support — where every request or decision may look slightly different.

Why Enterprises Should Care

AI agents deliver measurable impact. They enhance efficiency, consistency, and the employee experience — taking on repetitive or time-consuming tasks so people can focus on higher-value work.

For organisations, that means fewer manual hand-offs, less administrative overhead, and faster response times. Because agents follow the same logic every time, they also help reduce human error and ensure compliance — while still allowing flexibility where judgement is needed.

In customer-facing settings, agents can provide immediate answers, trigger follow-up actions, or even bring in a human expert when required. In internal workflows, they help teams move from doing tasks to achieving outcomes, freeing up time for creativity, strategy, and innovation.

Where This is Heading

The future is multi-agent collaboration. Specialised agents across HR, Finance, IT, and Customer Care will work seamlessly together — not as isolated bots, but as a coordinated system that adapts to the needs of every organisation.

This evolution mirrors how teams operate in real life: different roles, different expertise, all contributing to a shared goal. Soon, agents will not only automate individual processes but orchestrate entire workflows — from data retrieval and document generation to system updates and employee support.

As these systems become part of everyday tools, the boundary between human and AI effort will continue to blur. Employees will no longer have to switch between platforms or departments for information — their digital assistant will simply know where to look, who to ask, and what to do next.

Employee Onboarding

AI agents simplify coordination. When a new hire joins, the HR agent creates the employee record, the IT agent sets up equipment and access, and the Learning agent assigns mandatory training — all automatically, in sync with company policy.

The Payroll agent confirms salary details, the Facilities agent prepares the workspace, and the HR agent later provides the manager with progress updates — ensuring every department stays aligned.

For the new hire, this means a seamless start — no delays, no confusion, and all resources ready on day one. For HR, it means less manual follow-up and more time spent focusing on people, not paperwork. Over time, the same approach can extend beyond onboarding — into performance management, internal mobility, and learning — creating a truly connected employee experience.

What is MCP?

Model Context Protocol (MCP) is an open standard that defines how AI applications and systems share context with large language models in a consistent and secure way. It provides a common language for describing data sources, tools, and resources that models can access.

Instead of building custom integrations for each model or platform, developers can connect everything through MCP, making it easier to scale safely and maintain consistency.

You can think of it as a universal interface for AI — a way for models to request data, use tools, or interact with systems without needing to know the technical details behind them.

Why Enterprises Should Care

MCP helps organisations simplify how they connect their systems to AI, reducing custom engineering work and improving governance.

Speed and efficiency

Reusable schemas and connectors mean developers can integrate once and reuse across different models or environments.

Control and assurance

Every request and response follows a predictable structure, with permissions, logging, and audit trails built in. This ensures models only access what they're authorised to, and every action can be traced.

Flexibility across vendors

Because MCP is open and model-agnostic, it lets you adopt new models or move between providers without re-architecting your integrations.

Where This is Heading

MCP is helping to build a more connected and interoperable AI ecosystem by standardising how tools and systems interact with models.

Multi-tool coordination

A model can use multiple MCP resources in sequence — for example, fetching data, analysing it, and writing results — all through one consistent interface.

Certified connectors and policies

An expanding ecosystem of secure MCP connectors will make it easier for enterprises to plug in to trusted data sources, applications, and APIs.

Domain-specific standards

Industries like banking, healthcare, and retail are expected to develop MCP patterns that align with their regulatory and data-handling requirements, ensuring both consistency and compliance.

Research Summary Using MCP

User: "Summarise the key takeaways from our latest sales report."

Here's what happens behind the scenes:

1. The model requests context

Instead of the model having direct access to your systems, it sends a structured request through MCP asking for the file or dataset it needs.

MCP identifies the available tools and resources, such as a "Sales Reports" folder, an analytics API, or a connected data warehouse.

2. The MCP server retrieves the right data

Using its configured permissions, MCP fetches only the specific file or report the model is authorised to access, such as Q3_Sales_Performance.pdf.

The model never interacts with your file system directly. It simply receives the information it requested through a secure, standard interface.

3. The model performs the task

Once the report is received, the model analyses the content and produces a short summary highlighting trends and insights.

4. The system logs every step

MCP automatically records what was accessed, by which model, and for what purpose. Every action is auditable and compliant.

What MCP enables

A consistent way for models to discover and request the data or tools they need without custom integrations.

Clear permission boundaries, so AI systems only access what they are allowed to.

Reusability and interoperability across different models and environments.

Full traceability and governance because every request and response is logged by the MCP server.

What are Communication Protocols (ACP & A2A)?

Communication protocols like ACP (Agent Communication Protocol) and A2A (Agent-to-Agent Protocol) define how AI agents talk, share information, and collaborate securely.

They give agents a shared language to exchange messages, delegate work, and understand each other's intentions, regardless of which company built them or where they run.

These protocols are essential for interoperability — allowing agents developed by different vendors to work together across platforms, tools, or cloud environments without custom integrations.

Why Enterprises Should Care

Communication protocols simplify how AI agents interact, cutting down integration time while improving security and governance.

Speed and efficiency

Agents can connect instantly using a shared message format instead of requiring one-off integrations. This means faster deployment and easier scaling.

Trust and transparency

Every interaction between agents follows agreed standards, with metadata that records who initiated a request, what was exchanged, and why. This provides a clear audit trail.

Flexibility and control

Because ACP and A2A are open standards, organisations can mix agents from different vendors while maintaining full control over communication rules and permissions.

Where This is Heading

As AI ecosystems grow, communication protocols will form the foundation for secure, multi-agent collaboration across industries.

Cross-vendor collaboration

Agents built by different companies will be able to cooperate seamlessly on shared workflows, such as customer care, HR, or supply chain.

Shared understanding frameworks

Standard message structures will allow agents to interpret requests more accurately and coordinate complex tasks end-to-end.

Industry alignment

Sectors like banking, healthcare, and telecom will adopt tailored communication models that align with their data and compliance standards, ensuring safe, trusted automation.

Customer Refund Handling

User: "I need to request a refund."

Instead of a single system managing every step, different agents coordinate through a communication protocol:

What this means with ACP / A2A

Each agent knows the others' capabilities and can delegate tasks securely.

Every exchange is logged, ensuring accountability and compliance.

Agents from different vendors can work together without additional engineering or data-sharing risks.