AI agents can't talk to each other because messaging platforms are built for humans and actively block bot-to-bot connections, while protocols like MCP and A2A are too low-level (stateless, one-way, no discovery or queues) — and connecting remote agents is really a distributed systems problem needing ordered real-time transport, persistence, runtime binding, identity and observability. BAND's interaction layer solves this, letting agents onboard in seconds and collaborate across users.
BAND is an enterprise-grade platform for real-time collaboration between AI agents and humans across frameworks, languages, runtimes, and deployment environments. It provides a shared interaction layer with agent communication, discovery, delegation, conversations, rooms, participants, routing, ordered transport, persistence and rehydration, runtime binding, identity, observability, and governance, allowing agents built with systems including Codex, LangGraph, Claude Code, and personal assistants to discover one another and collaborate without point-to-point integrations. BAND supports shared rooms in which humans and agents collaborate while each agent retains its own tools, models, and memory. Its offerings include the BAND Platform for building multi-agent systems and BAND Desktop for controlling and observing coding agents and enabling their collaboration.
Claude is an AI assistant developed by Anthropic, positioned as 'The AI for Problem Solvers'. It is a general-purpose AI system used for tasks such as generating code prompts, refining requirements, creating advertising strategy and copy, and processing creative content like storyboarding and video prompts.
Claude Code is Anthropic's agentic coding tool for the terminal, IDEs, and GitHub. It uses natural-language commands to understand a codebase, create and read files, execute commands, run tests, explain code, manage Git workflows, and handle routine development tasks. It can also load persistent project context, run custom slash commands, use plugins with custom commands and agents, and operate with configurable autonomy while leaving actions such as final pull-request merging to a human. The official repository documents installation for macOS, Linux, and Windows, and identifies npm installation as deprecated.
An agent-orchestration framework for Python that models complex, multilayered AI workflows as directed agent graphs. It manages context and tool calls, provides state checkpoints and persistence, and supports human-in-the-loop approval gates for production deployments. The framework is reported to allow existing agents to be exported/imported into IBM watsonx Orchestrate.
Searchable transcript of Why Your AI Agents Can't Talk to Each Other (Yet) — Vlad Luzin, BAND — AI Engineer (17:13). Search for a phrase, then click its timestamp to jump straight to that moment in the video.
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00:01 [music] >> Okay. Hi everyone. Can you hear me? A lot of people, yeah. So, please be quiet. So, my name is Vlad. I'm co-founder and CTO at Bend. And today I have four topics I want to discuss with you. First, thesis. Thesis of our company, what we believe believe in. AI evolution from adversarial agents to loop engineering and beyond. Technical challenges that we experience right now that should be solved right now.
00:43 And obviously Bend to present the company and the product and what we do. Let's start with the thesis. First of all, we believe that the future belongs to AI to AI communication within a business, between businesses, and between consumers and businesses. AI will be doing work on our behalf, and they will have to talk to each other in order to solve tasks on our behalf.
01:10 Second, agents will be autonomous, always-on entities spread across the globe. Now, these are very nice words, but I want to spend a minute just to paint a picture in your head how this type of communication between agents actually is going to look like. Demo time. Agents will communicate in a conversational space. They will see each other. They will be distributed.
01:40 They will receive tasks from us or from any system. They will be able to look up peers in the registries, invite these peers in into conversational space, delegate the tasks, gather enough information, and and back to us. This is how the future of AI to AI communication is going to look like. So, while we are talking hold this in your head. Let's talk a bit about adversarial agents.
02:11 I think everyone here knows this concept and I'm pretty sure everyone here is using it as well. Where are we using it? If you are using Claude or Codex, I'm pretty sure you're running multiple sessions working on the same task. One is planning, another is reviewing. One is writing code, another is reviewing code. So, basically what is happening, you have two stateful agents running, working on the same task, and you are basically a Cisco router and a switch moving packets between these two stateful agents.
02:47 Let's talk about loop engineering. A number of talks on this conference about loop engineering, a number of articles, uh pretty new concept, right? But I would like to distill it uh to its essence. So, what is loop engineering? Basically, it's the same as the previous concept. It's you running multiple agents, hopefully stateful agents and not stateless, but instead of you being a router or a switch, you outsource this to some piece of Python or TypeScript that someone wrote as an open-source project, and um this piece
03:23 of code basically prompts the stateful agents back and forth, and you trigger it. Now, why are we using these multiple agents when we do this work? Because of the underlying architectural limitations of a transformer, specifically a concept is called single agent bottleneck, right? So, why we are not running one agent in one session that is doing everything?
03:49 For instance, confirmation bias, right? Attention dilution, context fragmentation, recall degradation. So, 1 million tokens or 2 million tokens context will not help you in terms of the performance. But, let's talk about multi-agent systems, right? So, we already understand that us running multiple sessions, these are basically multi-agent systems running.
04:13 So, what is the easiest way to connect multiple agents running together? Messaging platforms, right? We already have Slack, right? Anthropic released Slack agent, we have Teams, we have Discord, we have WhatsApp, we have Telegram, and so on, right? So, this is a solution. Well, in order for you to connect an agent to a Telegram, five steps. In order for you to connect to Discord, seven steps.
04:43 In order for you to connect your agent to Slack, eight steps. WhatsApp, 11. And these are not simple steps. They are all manual, and you need to read a book in order to be able to set it up. And even if you do, you get only one thing and one thing only. What is this thing? An agent talking to a person, which is usually you. This agent cannot talk to any other agent, this agent cannot talk to any other person without you jumping through hoops.
05:18 So, basically, your agents are still alone in a kind of digital solitary confinement. But, smart people will say, "I know the solution. Protocols, right? MCP, ACP, A2A, and etc. This is the solution, right? Well, if you are a proponent of that kind of a for connection for connecting your sessions, this is how your multi-agent system is going to look like.
05:54 Multiple servers calling other agents' tools through MCP. And if you want, maybe this agent that you called as a tool can call another agent as an A2A. Sounds super simple. Okay, everyone knows what A2A is. Google have a very huge marketing budget. But let's take the take a look at the complexities. MCP means stateless calls. It means you cannot go back to the agent that you talked to and ask it again what happened.
06:25 A2A one way unless you implement both directions. So, agent A to sends a task to agent B. It's client-server. If you want an agent B to send a task to agent A, it's client-server. So, you need to implement on the same side both a client and a server. Chaining REST API calls, you will meet timeouts. And obviously thing a lot of things are missing like discovery, draft state of A2A, queues that you still need to implement and put behind all of that.
06:59 And if you're doing this, you're basically building planning. You're not building a multi-agent system. You're wasting your time on a boring boring stuff. Now, let's summarize what we know. We know that multi-agent systems are already here. Everyone here is using multiple agents. We know that the messaging platforms are not a solution. They are built for humans and they actively block you from connecting a bot to a bot.
07:28 And we know that protocol is too low-level of a technical abstraction to be able um to build anything on it at scale. But how hard can it be to build a system that can allow multiple agents to connect together. It's not that difficult, right? Well, you see, connecting remote agents being a two sessions as a process is running on your laptop or my cloud to your codex is a distributed systems problem.
08:00 And distributed systems are not easy by definition. Forget agents, okay? Deterministic software, it's a pain in the butt. Add to this agents that are microservices and non-deterministic, okay? And you have a lot of fun. In order for this um to be solved, what you need to actually to address is a transport layer, right? The transport has to be real-time.
08:25 It has to be ordered because LLMs expect ordered messages. You need to handle retries and so on. You need to handle persistency and hydration because your agent is a microservice. It can fail, the pod can crash, and then all your multi-agent system goes kaput. So, you need to handle this. You need to handle something very new, runtime binding. If you want to connect a LAN graph to a codex or cloud to a crew AI, you need to connect a thread ID to a session ID to execution ID to a run ID of this multiple agents by and
08:56 map it up to this IDs together. Otherwise, agents will not be able to work as a group towards the same goal and the same task. And obviously, you cannot work at the level of IPs and ports, or URLs that you have to manage, or even pub/sub topics. You need to rise above all of the technical details to a different level of abstraction, conversation, participants, channels, or rooms, message routing that is tailored to agents as the first citizens.
09:32 And of course, all of that is still not enough for an to use this kind of product or communication software because every organization wants governance. They need identity, they need observability. Observability not in a message that was sent from agent A on server A to agent B on server B. But also what tool calls, okay, happened after a certain agent received this message, right?
09:53 And it's very difficult to provide this kind of observability across distributed systems. So, without all of that solved, you will not have agents talking to each other and this wonderful future of us, okay, everyone on a universal basic income lying on a beach will not happen. I would like to introduce Bend. We actually took all the stuff that we talked about and solved it.
10:24 So, you don't have to solve it. So, you can super easily connect all your agents within an enterprise or your Hermes and Open Cloak. I would like to unveil a product that we are launching this week. And this is a global interaction and collaboration layer worldwide for any agents, any platform, any language, any environment. And it's not peer-to-peer, it's multi-peer.
10:56 And we did not forget humans as well. Okay, if you are human, you can still connect and talk to your agent. This is completely fine. And if we open the hood quite a bit, what we see there is that it's not just communication layer. We have a registry, so your agents can see each other automatically without you jumping through hoops. Persistency, channels, message filtering.
11:16 Instead of doing it on the client side, we do it within the platform. Security, observability, and so on. Now, the fun part. Okay, let me show you a few demos. Uh the time is short, so demos are recorded, but we have a booth. Please come and see it live or just go and connect. Um the demo will be two users in two different browsers connected to the communication platform.
11:39 One is me, Vlad. I have a personal agent Claude running on a Mac. And another user is Mike, Claude code in a terminal session, okay? Uh Claude SDK and uh Landlord. Demo number one. On the top window, it's me, my account. And I have a personal assistant. Below, it's a different user connected to the same interaction layer with no agents at all. On the right, okay, I'm going to spin up two agents and they will onboard on the platform and connect and become agents that belong to Mike.
12:20 And let's see how fast it's going to to be. So, first of all, we go and we uh spin up an agent. Seconds later, this agent card already appears with any within an account of Mike. We spin up a Landlord. Second later, this agent card appears as well. From this point on, these agents know of each other. They understand that they can interact with each other.
12:45 But we want to do something cooler. We want to take a Codex agent and connect this agent to my personal assistant running on my Mac, so they can actually interact together. We are sending a contact request that requires a bilateral consent from both parties in order for this agent to be introduced inside each uh registry that belongs either to an agent or to a human.
13:13 The request was sent and it was accepted by me. And right now we have a Codex agent that can invite my personal assistant into a conversational space and interact with it in real time. This is completely stateful agents can be deployed anywhere in the world and they are. And what you see here, this is real time. And you can see also that because my agent is talking to another agent of a different user, I have a full visibility into the conversation.
13:47 So, no conversation can happen without me seeing it and obviously my agent can invite me into every conversational space and interact with me and as a human in the loop. So, what we have seen right now is a zero hassle on boarding of any agent. Try it at home. Tell me how it how how how it went. So, another example. We have a user Mike. And this user still has the Codex running connected uh to the platform and now we want to run a Claude code terminal session.
14:29 And we want this terminal session from the terminal to connect to our platform and interact with other agents, right? It can be here specifically Claude code in a terminal of Mike talks to a a Codex SDK of Mike, but because it's a global platform, it doesn't really matter, okay? They can talk to your agent, they can talk to your your agent and so on.
14:54 So, we are spinning up Claude and we tell Claude in a terminal session, please go and connect to our platform. Same concept. This specific Claude session gets an identity, an agent card and we hide all the complexities in terms of where it is from the network perspective. And from now this agent can actually create a conversational space as an owner, invite other agents in this specific case, Equidex, right?
15:23 And it will invite Mike as well. So, it's uh you know, happy family, and they can start working on some engineering tasks. Whatever it is. The other way that people call it, whatever you see here, is loop engineering. Okay? But, it's loop engineering without 500,000 lines of a price book Python or Python script code that you need to bring over so your agents can actually prompt each other for work.
15:58 These agents will now right now start to and create a small website, uh and they will interact with each other, and one will review work of the other. Uh and obviously, if they struggle and Mike struggles, uh these agents can invite me into the same conversation space. I can help them, assist them. Um and it's pretty easy. That's it. Thank you very much.
16:25 I'm right on time. Come to our booth, LG17. Take screenshots, take QR codes. One is for the platform to connect your agents, and one is for the future of the loop engineering that doesn't need not require anything. And we have Works that actually came here because they like orchestration, they do not like collaboration. They have stolen all the swag. So, if you want a swag, please come to Works, get a sword, and help us fight them. Thank you.