Struggling with LLM integration? Learn how agentic protocols like A2A and MCP enable autonomous AI workflows, reduce glue code, and scale faster.
Introduction
LLMs (Large Language Models) are powerful. They can generate content, answer questions, even write code. But using them in real products is a lot harder than it looks.
According to Zeta Alpha’s Trends in AI report, coordination, context retention, and orchestration continue to be key challenges in enterprise LLM adoption.
Founders often end up building complex logic, managing memory, and writing glue code to connect the LLM to other tools.
But now, there’s a better way:
- Agentic Protocols
These new standards let multiple AI agents talk, collaborate, and take real-world actions.
They turn AI from a solo act into a coordinated team.
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Why LLM Integration Is So Hard
1. One LLM Can’t Do Everything
LLMs are amazing at language, but they come with real limitations:
- They can’t operate tools
- They can’t coordinate tasks
- They can’t track memory across actions
That’s where agentic protocols come in.
Multiple LLMs = You’re choosing the best brain for each job.
Agentic Protocols = You’re giving those brains the ability to talk to each other, share memory, and complete workflows as a team.
Think of it like this:
Multiple LLMs are like hiring different experts: one for support, one for billing, one for identity verification.
Agentic protocols are like creating a shared office and task manager where those experts can coordinate, assign tasks, and follow up with each other without you being the middleman.
So, using multiple models gives you flexibility. Using agentic protocols gives you coordination.
When combined, they let you build truly autonomous AI systems.
2. Founders Have to Build Everything Around It
- Manage APIs
- Store and retrieve context
- Create routing logic
- Integrate with CRMs, databases, calendars, etc.
It’s like hiring a brilliant intern who can write essays but needs you to click every button.
3. Lack of Multi-Agent Support in Most Frameworks
Most current-gen LLM APIs (like OpenAI, Anthropic, or Gemini) are single agent focused.
According to LangChain’s 2025 Agent Survey, over half of AI teams already use agents in production with 78% planning to scale up in the next 12 months. However, full multi agent coordination remains limited in most frameworks today.
LLM vs. Agentic Protocols
| Feature | LLM ( Old Way) | Agentic Protocols (New Way) |
|---|---|---|
|
Task Coordination |
Manual |
Autonomous |
|
Memory Handling |
Fragile |
Shared and Persistent |
|
Tool Integration |
Complex |
Built in Agent Tools |
|
Scaling |
Custom Logic |
Composable Workflow |
|
Cost |
High engineering effort |
Faster to deploy + maintain |
- LLM = One smart intern
- Agentic Protocols = A full team of AI agents that can talk, plan, and act without constant supervision
What Are Agentic Protocols?
Agentic protocols are standards that let multiple AI agents:
- Communicate
- Coordinate
- Share memory
- Delegate tasks
- Work across tools and APIs
Popular Examples:
- A2A (Agent to Agent) by Google: Enables agents to collaborate and invoke each other using JSON based messages.
- MCP (Model Context Protocol): Enables memory and tool sharing across models and tools.
These protocols allow LLMs to evolve from chatbots into powerful, autonomous digital agents.
Real-World Example: AI Sales Assistant
With just an LLM:
- It can write an email reply
- But can’t book meetings, update CRM, or follow up
With agentic protocols:
- One agent finds leads
- Another writes a personalized pitch
- A third books the meeting
All of them coordinate no human in the loop required.
How to Get Started with Agentic Protocols
Use open-source frameworks
You don’t have to build from scratch. Here are tools and frameworks to explore:
- Crew AI: Build multi-agent workflows with role-based agents
- Lang Graph: Graph-based agent orchestration built on Lang Chain
- Auto gen: A Microsoft-backed multi-agent framework for complex AI pipelines
Try protocols
- A2A by Google: Agent to agent collaboration using JSON-based communication
- MCP (Model Context Protocol): Memory and tool sharing for Claude agents and others Instead of relying on one big AI model to do everything, you can now build a team of smaller, specialized agents that collaborate.
Final Thoughts
We’re moving beyond solo, prompt based AI into something far more powerful: coordinated, autonomous agent systems.
LLMs gave us the power to understand and generate language.
Agentic protocols unlock the ability to act, remember, and collaborate at scale.
As these standards mature, early adopters won’t just ship faster they’ll build smarter, autonomous systems that scale effortlessly.
From strategy to delivery, we are here to make sure that your business endeavor succeeds.
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