The past two years in AI have felt like a sprint. We’ve seen powerful new capabilities come online—LLMs that can reason, tools that can be called programmatically, agents that can plan and act autonomously, and emerging protocols that make these pieces work together. But here’s the important part:

This is not a linear evolution.

LLM-based applications are not “turning into” agents. Tools aren’t replacing apps. Agents aren’t the inevitable next step for everyone. Instead, we’re witnessing the expansion of the AI architecture toolkit.

And developers now have more choices than ever.

Some applications will stay simple—just an LLM plugged into a chat interface. Others will become highly autonomous systems coordinating across dozens of tools. The question isn’t what’s next, it’s what fits.

At Gentoro, we’re building infrastructure for this growing complexity. Our focus is on integration—connecting the reasoning capabilities of agents to the systems, APIs, and tools they need to get things done. We do this by standardizing what we call the agentic tool—a durable, reusable unit that plays well in both traditional LLM apps and next-gen agent ecosystems.

This post explores the current AI application stack and the architectural choices developers have today. Let’s walk through how protocols like MCP and A2A (and platforms like Gentoro) are helping make sense of it all.

What are LLM-based Applications?

LLM-based applications are still the most common—and in many cases, the most appropriate—way to use AI.

These are conventional applications that include a large language model as a component. Think chatbots, Q&A systems, form summarizers, email generators, or retrieval-augmented search interfaces. The model provides language understanding and generation, but the flow of logic is fixed—decided by developers.

Frameworks like LangChain have become the go-to choice for building these kinds of systems. LangChain abstracts away prompt management, memory, retrieval, and tool use, letting developers quickly build smart, linear flows using reusable primitives.

Today, these are often called agentic workflows—even if they don’t involve a fully autonomous “agent.” And that’s okay. Not every AI system needs to plan, reason, or dynamically route actions. Sometimes, simpler is better.

What is Function Calling in AI?

For systems that do require more flexibility, function calling introduced the next major capability. First introduced by OpenAI in mid-2023 and rapidly adopted across the ecosystem, function calling allowed models to pick a tool based on the user’s intent.

Rather than hardcoding every possible user path, developers could register a set of functions or APIs. The model would determine when a function was needed, fill in the parameters, and pass the result to the application for execution.

This pattern enabled more dynamic behaviors. Applications could now delegate decisions to the model: when to fetch weather data, when to generate a calendar invite, when to submit a form.

It laid the foundation for agentic systems, where the LLM isn’t just responding—it’s acting.

MCP: Standardizing Tool Calling (not Function Calling)

Function calling solved the problem of choosing what to do. But it didn’t solve how to actually execute tools in a standardized way—especially across teams, applications, and enterprises.

Every developer was forced to implement their own tool-calling layer: HTTP clients, error handling, authentication, retries, output formats. Worse, there was no way to share tools between systems, even if they solved the same problem. The model knew what to do—but developers still had to reinvent how to do it, every time.

That’s where MCP (Model Communication Protocol) comes in.

Introduced by Anthropic in 2024, MCP doesn’t unify function calling formats (those remain vendor-specific, and frameworks like LangChain already abstract them). Instead, MCP standardizes how tools are described and called—defining a shared protocol for execution, independent of model or vendor.

This unlocked something powerful: the ability to separate agent builders from tool builders.

Agent builders now focus on reasoning, planning, and choosing actions. Tool builders focus on secure integrations, compliance, and system execution. And tools—once hard to share—can now be packaged, reused, and invoked reliably across agents, apps, and workflows.

At Gentoro, this is core to our work. We help organizations define and manage agentic tools—tools that comply with MCP, can be discovered and composed, and can serve both traditional applications and autonomous agents.

What are Agentic Tools? Gentoro’s Unit of Integration

At Gentoro, we believe the agentic tool is the stable unit of integration in this new world.

It’s reusable. It’s composable. It can be invoked by a simple LLM-based app—or by a chain of agents executing long-running workflows.

This flexibility matters. The AI stack is still evolving, but tools are not going away. In fact, they’re becoming more important. That’s why Gentoro focuses on tools as first-class objects—not just one-off API calls.

And unlike most platforms, Gentoro supports tool composability. Sometimes, you don’t want to rebuild a tool—even if you could. Maybe it’s owned by a third party. Maybe it’s tested and trusted. Or maybe you just don’t have the time.

With Gentoro, developers can compose tools into higher-level capabilities, chain tools across teams, and orchestrate behavior without tightly coupling their systems. This is crucial for scaling responsibly and maintaining flexibility over time.

Understanding A2A: Enabling Agent-to-Agent Interoperability

As systems grow more complex, we’re starting to see the rise of multi-agent systems—where different agents specialize in different roles and collaborate to complete workflows. This could mean sourcing job candidates, scheduling interviews, processing approvals, or managing inventory—each handled by a separate, specialized agent.

To make this work, agents need to talk to each other.

This is where the A2A protocol (Agent-to-Agent) comes in. Developed in partnership with Google Cloud and other industry leaders, A2A provides a standard for how agents discover each other, share tasks, and manage collaboration across frameworks and vendors.

It supports long-running workflows, capability negotiation, and even UI modality adaptation (like embedding videos or forms). It’s a huge step forward in agentic system design.

But here’s something interesting: sometimes, an agent is a tool.

Gentoro recognizes this hybrid reality. Our platform is being designed to let agents call not just tools, but other agents, treating them as composable, callable units. This will be critical as systems get more modular, and as agents specialize further.

How Gentoro Fits Into the Expanding AI Stack

At Gentoro, we’re not just building for one approach to AI. We’re building for all the approaches that coexist in today’s hybrid stack.

Some teams will continue to build LLM-based applications with fixed flows. Others will explore fully autonomous agents. Many will land somewhere in between—leveraging tools, workflows, and planning in varying degrees.

Across all of these patterns, we believe one thing stays constant: the value of the agentic tool.

Gentoro is built to help developers define, manage, and integrate these tools—securely, flexibly, and at scale. Whether you’re working with LangChain, building agents with open protocols like MCP and A2A, or running a mix of all of the above, Gentoro ensures your systems can connect, collaborate, and grow.

We’re not trying to define what the future must look like. We’re helping build the infrastructure for whatever it becomes. If you’d like to learn more, get in touch with us today! 

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