AI Agents and Protocols:
The Hidden Architecture Behind Intelligent Automation in Cybersecurity and Risk Management
When Intelligent Machines Need to Talk
Imagine you're at a massive international airport where thousands of planes need to coordinate landings, takeoffs, and gate assignments. Without clear protocols—standardized ways of communication—chaos would ensue.
The same principle applies to AI agents working in complex environments like Third-Party Risk Management (TPRM) and Cyber Threat Intelligence (CTI).
AI agents aren't just chatbots or simple automation tools. They're sophisticated software entities that can perceive their environment, make decisions, and take actions to achieve specific goals. But here's the catch: they need protocols to work effectively, especially when multiple agents collaborate or when they interact with various systems and databases.
What Are AI Agent Protocols?
At their core, AI agent protocols are standardized methods of communication and interaction that enable agents to:
Exchange information
with other agents or systems
Coordinate actions
to avoid conflicts and redundancy
Maintain consistency
in data and decision-making
Handle errors
gracefully without system-wide failures
Scale operations
as complexity increases
Think of protocols as the "language" and "etiquette" that agents use to work together harmoniously.
Today, AI agents trying to work together (whether LangChain, AutoGen, CrewAI, or custom solutions) speak their own dialect. This can of course cause chaos for developers. Standardized protocols have emerged that promise to fix some of this chaos. MCP (Anthropic), A2A (Google), ANP (Open Source Community), and ACP (IBM), LangChain Expression Language (LCEL). Each aims to solve the agent communication problem, but each approaches it from very different angles.
Have a look at our use cases:
When a Major Cloud Provider Gets Compromised
Coordinated APT Campaign Detection
Dynamic Threat Analysis Across Varying Confidence Levels
The augmented LLM
Agentic systems start with an LLM. Vut when you add tools, memory, and retrieval, they come alive. These models can search, decide, and adapt, all on their own.
This visual was inspired by documentation from Anthropic and was created by the Black Kite Data Research team.
This visual was inspired by documentation from Anthropic and was created by the Black Kite Data Research team.
Autonomous agent
An autonomous agent begins with input from a human, either a direct command or a conversation. The agent then takes initiative: it plans, acts, observes the outcome, and adjusts accordingly. Throughout this loop, it interacts with tools and the environment, using real-time feedback to stay aligned with its goal. It can pause for human input when needed or stop based on predefined conditions. While the loop may seem simple, it enables dynamic, self-directed behavior.