In contrast to conventional AI fashions that reply to single prompts (like ChatGPT’s primary Q&A mode), AI brokers can plan, purpose, and execute multi-step duties by interacting with instruments, information sources, APIs, and even different brokers.
Sounds summary? That’s as a result of it’s. Whereas most may agree with this definition or expectation for what agentic AI can do, it’s so theoretical that many AI brokers accessible at the moment wouldn’t make the grade.
As my colleague Sean Falconer noted recently, AI brokers are in a “pre-standardization section.” Whereas we would broadly agree on what they ought to or may do, at the moment’s AI brokers lack the interoperability they’ll must not simply do one thing, however truly do work that issues.
Take into consideration what number of information methods you or your functions must entry every day, corresponding to Salesforce, Wiki pages, or different CRMs. If these methods aren’t at present built-in or they lack suitable information fashions, you’ve simply added extra work to your schedule (or misplaced time spent ready). With out standardized communication for AI brokers, we’re simply constructing a brand new sort of information silo.
Regardless of how the business adjustments, having the experience to show the potential of AI analysis into manufacturing methods and enterprise outcomes will set you aside. I’ll break down three open protocols which can be rising within the agent ecosystem and clarify how they may show you how to construct helpful AI brokers—i.e., brokers which can be viable, sustainable options for advanced, real-world issues.
The present state of AI agent improvement
Earlier than we get into AI protocols, let’s evaluation a sensible instance. Think about we’re enthusiastic about studying extra about enterprise income. We may ask the agent a easy query through the use of this immediate:
Give me a prediction for Q3 income for our cloud product.
From a software program engineering perspective, the agentic program makes use of its AI fashions to interpret this enter and autonomously construct a plan of execution towards the specified purpose. How it accomplishes that purpose relies upon solely on the listing of instruments it has entry to.
When our agent awakens, it is going to first seek for the instruments underneath its /instruments listing. This listing can have guiding recordsdata to evaluate what’s inside its capabilities. For instance:
/instruments/listing
/Planner
/GenSQL
/ExecSQL
/Choose
You too can take a look at it based mostly on this diagram:
Confluent
The principle agent receiving the immediate acts as a controller. The controller has discovery and administration capabilities and is accountable for speaking straight with its instruments and different brokers. This works in 5 elementary steps:
- The controller calls on the planning agent.
- The planning agent returns an execution plan.
- The choose evaluations the execution plan.
- The controller leverages GenSQL and ExecSQL to execute the plan.
- The choose evaluations the ultimate plan and offers suggestions to find out if the plan must be revised and rerun.
As you may think about, there are a number of occasions and messages between the controller and the remainder of the brokers. That is what we’ll consult with as AI agent communication.
Budding protocols for AI agent communication
A battle is raging within the business over the best option to standardize agent communication. How will we make it simpler for AI brokers to entry instruments or information, talk with different brokers, or course of human interactions?
At this time, we’ve got Mannequin Context Protocol (MCP), Agent2Agent (A2A) protocol, and Agent Communication Protocol (ACP). Let’s check out how these AI agent communication protocols work.
Mannequin Context Protocol
Mannequin Context Protocol (MCP), created by Anthropic, was designed to standardize how AI brokers and fashions handle, share, and make the most of context throughout duties, instruments, and multi-step reasoning. Its client-server structure treats the AI functions as purchasers that request info from the server, which offers entry to exterior assets.
Let’s assume all the info is saved in Apache Kafka matters. We are able to construct a devoted Kafka MCP server, and Claude, Anthropic’s AI mannequin, can act as our MCP consumer.
In this example on GitHub, authored by Athavan Kanapuli, Akan asks Claude to hook up with his Kafka dealer and listing all of the matters it accommodates. With MCP, Akan’s consumer software doesn’t must know methods to entry the Kafka dealer. Behind the scenes, his consumer sends the request to the server, which takes care of translating the request and operating the related Kafka perform.
In Akan’s case, there have been no accessible matters. The consumer then asks if Akan want to create a subject with a devoted variety of partitions and replication. Similar to with Akan’s first request, the consumer doesn’t require entry to info on methods to create or configure Kafka matters and partitions. From right here, Akan asks the agent to create a “international locations” subject and later describe the Kafka subject.
For this to work, you could outline what the server can do. In Athavan Kanapuli’s Akan venture, the code is within the handler.go file. This file holds the listing of capabilities the server can deal with and execute on. Right here is the CreateTopic instance:
// CreateTopic creates a brand new Kafka subject
// Non-compulsory parameters that may be handed by way of FuncArgs are:
// - NumPartitions: variety of partitions for the subject
// - ReplicationFactor: replication issue for the subject
func (ok *KafkaHandler) CreateTopic(ctx context.Context, req Request) (*mcp_golang.ToolResponse, error) {
if err := ctx.Err(); err != nil {
return nil, err
}
if err := ok.Shopper.CreateTopic(req.Matter, req.NumPartitions, req.ReplicationFactor); err != nil {
return nil, err
}
return mcp_golang.NewToolResponse(mcp_golang.NewTextContent(fmt.Sprintf("Matter %s is created", req.Matter))), nil
}
Whereas this instance makes use of Apache Kafka, a extensively adopted open-source know-how, Anthropic generalizes the tactic and defines hosts. Hosts are the large language model (LLM) functions that provoke connections. Each host can have a number of purchasers, as described in Anthropic’s MCP architecture diagram:
Anthropic
An MCP server for a database can have all of the database functionalities uncovered by way of the same handler. Nonetheless, if you wish to develop into extra refined, you may outline current prompt templates devoted to your service.
For instance, in a healthcare database, you could possibly have devoted capabilities for affected person well being information. This simplifies the expertise and offers immediate guardrails to guard delicate and personal affected person info whereas guaranteeing correct outcomes. There’s way more to be taught, and you’ll dive deeper into MCP here.
Agent2Agent protocol
The Agent2Agent (A2A) protocol, invented by Google, permits AI brokers to speak, collaborate, and coordinate straight with one another to resolve advanced duties with out frameworks or vendor lock-in. A2A is expounded to Google’s Agent Development Kit (ADK) however is a definite part and never a part of the ADK package deal.
A2A ends in opaque communication between agentic functions. Meaning interacting brokers don’t have to reveal or coordinate their inner structure or logic to change info. This offers totally different groups and organizations the liberty to construct and join brokers with out including new constraints.
In follow, A2A requires that brokers are described by metadata in identification recordsdata generally known as agent playing cards. A2A purchasers ship requests as structured messages to A2A servers to eat, with real-time updates for long-running duties. You’ll be able to discover the core ideas in Google’s A2A GitHub repo.
One helpful instance of A2A is this healthcare use case, the place a supplier’s brokers use the A2A protocol to speak with one other supplier in a unique area. The brokers should guarantee information encryption, authorization (OAuth/JWT), and asynchronous switch of structured well being information with Kafka.
Once more, take a look at the A2A GitHub repo when you’d wish to be taught extra.
Agent Communication Protocol
The Agent Communication Protocol (ACP), invented by IBM, is an open protocol for communication between AI brokers, functions, and people. Based on IBM:
In ACP, an agent is a software program service that communicates by way of multimodal messages, primarily pushed by pure language. The protocol is agnostic to how brokers perform internally, specifying solely the minimal assumptions essential for easy interoperability.
If you happen to check out the core concepts outlined within the ACP GitHub repo, you’ll discover that ACP and A2A are comparable. Each have been created to remove agent vendor lock-in, pace up improvement, and use metadata to make it straightforward to find community-built brokers whatever the implementation particulars. There’s one essential distinction: ACP allows communication for brokers by leveraging IBM’s BeeAI open-source framework, whereas A2A helps brokers from totally different frameworks talk.
Let’s take a deeper take a look at the BeeAI framework to grasp its dependencies. As of now, the BeeAI venture has three core parts:
- BeeAI platform – To find, run, and compose AI brokers;
- BeeAI framework – For constructing brokers in Python or TypeScript;
- Agent Communication Protocol – For agent-to-agent communication.
What’s subsequent in agentic AI?
At a excessive stage, every of those communication protocols tackles a barely totally different problem for constructing autonomous AI brokers:
- MCP from Anthropic connects brokers to instruments and information.
- A2A from Google standardizes agent-to-agent collaboration.
- ACP from IBM focuses on BeeAI agent collaboration.
If you happen to’re enthusiastic about seeing MCP in motion, take a look at this demo on querying Kafka topics with natural language. Each Google and IBM launched their agent communication protocols solely not too long ago in response to Anthropic’s profitable MCP venture. I’m desirous to proceed this studying journey with you and see how their adoption and evolution progress.
Because the world of agentic AI continues to develop, I like to recommend that you just prioritize studying and adopting protocols, instruments, and approaches that prevent effort and time. The extra adaptable and sustainable your AI brokers are, the extra you may give attention to refining them to resolve issues with real-world impression.
Adi Polak is director of advocacy and developer expertise engineering at Confluent.
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