What the “Agentic AI Stack” Really Means for Business
For the past two years, most organisations have experimented with AI in the same way: a chatbot answering questions.
Helpful? Yes. Transformational? Not really.
What’s now emerging is something more powerful—and more relevant to business leaders: AI agents. These aren’t just tools you talk to. They are systems that can take action, use company systems, and work together.
A recent demonstration from Google for Developers shows how this next phase of AI is being built in a structured, enterprise-ready way on Google Cloud.
You don’t need to understand the code to understand the implication.
The Big Shift: From Single AI to AI Teams
Think of the difference like this:
- Chatbots → Answer questions
- AI agents → Do work
And increasingly:
- One agent → Limited value
- Multiple agents working together → Real leverage
To make this work at scale, three ideas matter.
The Three Building Blocks
- A Standard Way for AI to Use Tools
(MCP – Model Context Protocol)
Every business system today—finance, HR, CRM, pricing, inventory—has its own interface. Traditionally, integrating AI into these systems required custom work every time.
MCP changes that.
It provides a standard way for AI systems to access tools and data, much like USB-C became a standard connector for devices.
Business impact:
- Faster AI deployment
- Less custom integration risk
- Clear boundaries around what AI can and cannot access
- A Proper Way to “Employ” an AI
(ADK – Agent Development Kit)
Instead of treating AI like a clever prompt, ADK treats it like a digital worker with:
- a defined role
- clear instructions
- approved access to tools
- monitoring and testing
This is important because businesses don’t fail with AI due to intelligence—they fail due to lack of structure.
Business impact:
- AI becomes auditable and controllable
- Easier governance and oversight
- Less “shadow AI” chaos
- A Way for AI Systems to Work Together
(A2A – Agent-to-Agent Protocol)
This is the most forward-looking piece.
Rather than building one massive AI system that tries to do everything, A2A allows:
- specialised AI agents
- to discover each other
- and collaborate when needed
Think of it like internal departments:
- Finance AI
- Procurement AI
- Customer Support AI
Each focused. Each accountable. Each able to ask another agent for help.
Business impact:
- Modular AI strategy (swap parts without breaking everything)
- Reduced vendor lock-in risk
- AI systems that scale with the organisation
A Simple Example: Currency Conversion (Why It Matters)
In the demo below, Google uses a currency exchange agent. That sounds trivial—but it proves a serious point.
Instead of:
- hard-coding exchange rates
- relying on static data
- or building fragile integrations
The AI:
- Understands the question
- Calls a trusted, approved data source
- Returns a current, accurate answer
- Can be reused by other AI systems
Now imagine the same pattern applied to:
- pricing approvals
- credit checks
- supplier validation
- compliance lookups
- inventory availability
That’s where this becomes operational, not experimental.
Why This Matters for Leaders in 2026
This architecture signals a shift away from:
“Let’s see what AI can do”
Toward:
“How do we redesign work assuming AI is part of the team?”
The organisations that will benefit most are not those chasing the smartest model—but those who:
- define clear AI roles
- control access properly
- and allow systems to collaborate safely
This is how AI moves from productivity boost to operating model change.
The Imbila Perspective
At Imbila, we see this as the start of a more mature AI phase:
- fewer experiments
- more structure
- clearer ownership
AI agents won’t replace teams—but they will reshape how work flows across teams.
The winners will be businesses that design for that reality early.