What is AI agent communication?

Large language models (LLMs): machine learning algorithms trained on vast amounts of data, give agents the ability to reason. With generative AI capabilities, agents can share the information they know with other entities. When agents have the ability to communicate with one another, an agentic system becomes more than the sum of its parts.

A multi-agent system can be thought of like a team of humans, each with expertise in their respective field. Autonomous agents share information that they alone can perceive about their environment, benefitting the whole group’s understanding. As more agents become capable of “talking to” one another across complex agentic workflows, we can expect entire ecosystems of agents working together in autonomous harmony to come online.

Networked AI agents can work together toward a common goal more efficiently than a single agent. But in order to coordinate their actions, they need to be able to effectively communicate.

Effective communication among AI agents leads to better situational awareness and more informed decision-making processes. When agents share data, they can refine their strategies and responses based on real-time information.

In complex systems, distributed AI can divide tasks among multiple agents, leading to faster problem-solving. Instead of a single AI trying to process everything, multiple agents can specialize in different aspects of a problem and communicate their findings.

AI agents that communicate can learn from each other, improving adaptability over time. By exchanging insights, they refine their behaviors based on shared experiences. Multi-agent AI systems can also scale efficiently, handling larger amounts of data and more complex tasks.

AI agents communicate in various ways depending on their role, environment and goals. Communication can be explicit or implicit, involving direct message exchanges or indirect observation of actions.

Some systems rely on centralized control, where a single AI processes and distributes data to other agents. Others use decentralized communication, where AI agents interact peer-to-peer.

Most agents are powered by LLMs, so they often talk to one another in natural human language. Agents must be able to not only share information, but express intention, coordinate within a hierarchy and negotiate over resource allocation.

Researchers are working on more efficient modes of agent-to-agent communication, such as Microsoft’s “DroidSpeak,” which aims to enable agents to communicate faster with minimal loss of accuracy.¹ Two dominant protocols for agent communication are KQML (Knowledge Query and Manipulation Language) and FIPA-ACL (Foundation for Intelligent Physical Agents – Agent Communication Language).²

The US’s Defense Advanced Research Projects Agency developed KQML in the 1990s, laying the foundation for agent-to-agent communication long before intelligent AI agents were feasible. FIPA’s developers built on this work shortly after, making standardization and semantic clarity improvements.

Many AI agents rely on cloud computing and Internet of Things (IoT) devices to exchange real-time data. Cloud AI systems store, retrieve and analyze large-scale datasets, while IoT-connected devices share sensor information across networks.

AI agents also communicate with humans using natural language processing (NLP), speech recognition and visual interfaces. Virtual assistants like OpenAI’s ChatGPT, Apple’s Siri and Amazon’s Alexa use NLP to interpret human queries and generate meaningful responses.

In customer support, AI chatbots provide automated assistance by understanding and responding to user inquiries. Some AI models also incorporate multimodal communication, combining text, speech and images to enhance interaction.

AI agents face several challenges that can impact accuracy, efficiency, security and scalability.

AI agents often operate across different platforms, each using unique protocols, data formats and communication languages. Protocols include information about the syntax and semantics of messages. Protocols can be predefined by human programmers, or emergent, arising organically from agent-to-agent communication.

Without standardized messaging frameworks, agents might struggle to interpret and respond to each other’s messages, leading to inefficiencies. For example, in smart cities, traffic management systems and autonomous vehicles might use different communication protocols, preventing seamless data sharing and coordination.

AI agents must process information with precision, yet ambiguity in message interpretation remains a challenge. Agents might misinterpret messages, leading to incorrect actions. In customer service chatbots, vague user queries such as “I want to change my order” could be misunderstood, resulting in the wrong modifications or cancellations.

Many AI use cases require real-time communication, but network latency and computational constraints can slow down response times. This is especially problematic in autonomous systems that require split-second decision-making. In self-driving cars, AI agents must instantly process data from cameras, sensors and GPS. Any delay in data exchange could result in poor navigation decisions.

AI agents communicating over networks are vulnerable to cyberattacks, data breaches and adversarial manipulation. Malicious actors could intercept or alter AI communications, leading to faulty decision-making and system failures.

Authentication, secure endpoints and proper handling of sensitive data are paramount. In healthcare AI systems, for instance, if an attacker modifies diagnostic data exchanged between AI agents, it could lead to incorrect treatment recommendations.

As the number of AI agents in a communication system increases, communication overhead grows, leading to scalability challenges. Agents must efficiently manage large-scale interactions without overloading computational resources.

In financial markets, thousands of AI trading bots communicate and react to market changes. If too many bots exchange data at once, network congestion could occur.

AI agents must communicate effectively in dynamic environments, where real-time information updates are necessary. If AI agents fail to adapt to new conditions, unexpected changes can disrupt their decision-making processes.

In disaster response, AI, autonomous drones and robots must continuously adjust their communication strategies based on unpredictable obstacles, such as collapsed buildings or lost network signals.

When AI agents interact with humans, communication challenges arise due to differences in language understanding, emotional context and reasoning styles. AI must correctly interpret human intent while providing clear responses.

In virtual assistants, understanding sarcasm, regional dialects or implicit requests remains a challenge. For instance, if a user says, “It’s freezing in here,” an AI assistant might not recognize that they want the thermostat increased.