Agentic AI: Goal-directed orchestration
Agentic AI, which is about autonomous systems that can reason, plan, and act toward goals, takes this need for integration to the next level. The more I learn about agentic AI, the clearer it becomes: integration will be an integral part of making it work. Here’s one way to look at it:
- It decides what needs to happen.
- Then figures out how to make it happen.
- And finally, executes using tools that interact with real-world systems: APIs, SaaS platforms, databases, internal services, or even physical devices.
In essence, agents become process orchestrators. They dynamically sequence tool use to complete tasks, just like a human might say:
“Check the customer order → validate stock → send invoice → notify the warehouse.”
Tools are interfaces — Often powered by APIs
Beyond the language models, agents rely heavily on tools to reach their goals. These tools might:
- Be self-contained pieces of code performing a specific function.
- Or, more often, connect to other systems, applications, or data sources.
This connection is typically made using existing integration technologies, especially API calls. Tools, then, can be seen as integration points. While frameworks may vary in how these tools are exposed and executed, it’s fair to say that they function like lightweight, task-specific APIs.
MCP: Maintaining shared context in agentic workflows
As agents evolve beyond isolated tasks and begin operating in more dynamic, multi-step environments, maintaining coherent context becomes essential. This is where the Model Context Protocol (MCP) comes into play.
MCP provides a standardised way to manage and persist context across agent interactions, tool usage, and system boundaries, ensuring that agents don’t lose track of goals, inputs, and outcomes as they operate over time.
Just like APIs standardise how systems exchange data, MCP standardises how context is carried, updated, and reused.
It’s a different layer of integration. Not about connecting systems directly, but about preserving semantic continuity across the intelligent agents driving them.
From single agents to the agentic mesh
To move beyond individual agents, we need collaborative agent ecosystems, that is often called an Agent Mesh. In such environments, multiple agents work together to accomplish complex goals, which, of course, requires integration, not just with systems, but between agents themselves.
One emerging protocol that aims to standardise this is the A2A (Agent-to-Agent) framework. This can be seen as yet another integration pattern, but one focused specifically on inter-agent communication and delegation.
Conclusion: Integration isn’t optional
From where I stand, integration is central to the success of agentic AI. Orchestration will certainly evolve, and new protocols and standards will emerge. But the core purpose will remain the same:
Securely and reliably sending and receiving data between all services, devices, systems, and now intelligent agents.
Without integration, there’s no orchestration, and without orchestration, agentic AI falls short. Integration is here to stay.