AI-Based Agent Frameworks: Architectures, Secure Implementation, and Applications in Distributed Systems

Autonomous agents powered by AI are transforming distributed computing, enabling the automation of complex tasks in dynamic environments.

Applications in decentralized networks, social simulations, cybersecurity, and IoT are already benefiting from this technology. However, developing robust agents involves architectural, scalability, and security challenges.

In this article, I will provide a detailed analysis of the main architectures used in the development of intelligent agents, compare relevant frameworks, and discuss advanced techniques to ensure security in distributed implementations.

2. Architectures of Intelligent Agents

The choice of architecture directly impacts the behavior, adaptability, and security of the agent. Different approaches are applicable depending on the problem to be solved.

2.1 Reactive Architecture vs. Cognitive Architecture

Agents can be classified into two major paradigms:

• Reactive agents: operate based on stimulus-response, without sophisticated internal models. They are widely used in embedded systems, IoT, and robotics.
• Cognitive agents: Possess complex internal models, enabling them to reason about their states and objectives.

2.2 Multi-Agent Architecture (MAS)

In distributed systems, agents often operate together, interacting with each other to achieve common goals. The main concepts include:

• Multi-Agent Organization-Based Systems (MOAS): Hierarchical structures of agents, used in traffic management and military simulations.
• Cooperative Autonomous Agents: Strategy used in multi-agent games and financial market AI.

Frameworks for Multi-Agent Implementation

3. Secure Implementation in Distributed Systems

As autonomous agents gain autonomy, it becomes crucial to ensure the security and integrity of data.

3.1 Secure Communication Protocols

Agents frequently interact through messages, which makes them vulnerable to attacks. Techniques to mitigate risks include:

• FIPA ACL (Agent Communication Language): Secure communication standard for distributed agents.
• gRPC + Protobuf: Efficient and secure communication between autonomous agents via binary serialization.
• MQTT with TLS: Security for agents operating in IoT.

3.2 Main Threats to Agent Security

a) Spoofing and Data Poisoning

• Adversarial attacks can manipulate the agent's perceptions, inducing errors in deep learning.
• Defense: Implementation of Zero Trust Architecture (ZTA) to validate each input.

b) Man-in-the-Middle Attacks in Agent Communication

• Possibility of intercepting and altering exchanged messages.
• Defense: Digital signatures and mutual authentication via TLS.

c) Secure Execution of Agents

To avoid malicious execution, agents can be isolated using:

4. Performance Benchmarks in Distributed Environments

The choice of platform directly impacts the latency, resource consumption, and scalability of the agent.

5. Conclusion

The implementation of AI agents in distributed systems requires strategic decisions regarding architecture, communication, and security. The use of appropriate protocols and secure isolation of agents is fundamental to ensure integrity and reliability.

The evolution of frameworks like JADE, SPADE, and CARLA reflects the growing demand for increasingly autonomous, secure, and efficient agents. Advances in computational security, such as eBPF and WebAssembly, offer new layers of protection, ensuring that these systems operate reliably in hostile environments.

References

1. "AI Agents Under Threat: A Survey of Key Security Challenges" - Arxiv, 2024.
2. "Paradigms of Computational Agency" - Arxiv, 2021.
3. "The Blue Amazon Brain (BLAB): A Modular Architecture" - Arxiv, 2022.
4. Official documentation of JADE, SPADE, and CARLA.