Building Multi-Agent Systems (Part 3) (blog.sshh.io)

A recent update in the field of multi-agent systems emphasizes a critical evolution in how AI agents operate, highlighting the shift from relying on manual JSON scripting to allowing agents to autonomously write and execute code. The author discusses the transition from rigid architectures to more fluid, code-first environments, with a focus on the emergence of a more generalized agent architecture. This includes the introduction of three main agent types: Planning Agents, Execution Agents, and Task Agents, which work in concert to tackle complex problems effectively. The increasing intelligence of AI has reduced the need for detailed manual setup, enabling agents to navigate tasks more independently. This development is significant for the AI/ML community as it indicates a maturation in multi-agent system design, where agents can perform long-horizon tasks in diverse domains. With the adoption of virtual machine sandboxes, agents are now equipped to handle non-coding tasks with a degree of autonomy that enhances efficiency and reduces latency. Additionally, the emphasis on context engineering—organizing the environment rather than just controlling the tools—marks a paradigm shift that promises to streamline the deployment of AI in real-world applications. As agents become more adept at self-managing their coding processes, the potential applications expand dramatically, paving the way for more intelligent and capable AI systems.