As large language models (LLMs) evolve from static responders into autonomous actors, developers are facing a new kind of systems challenge: building infrastructure that can support reasoning, decision-making, and continuous action. Gravity’s agentic AI platform is one of the most advanced real-world examples, a system where LLMs interact with tools, memory, and guardrails to execute complex, multi-step workflows.
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Despite the challenges, developers can build a system like Gravity’s from the ground up, covering modular orchestration, behavioral safety, observability, and the integration of LLMs with business logic. Whether you’re designing intelligent assistants, AI copilots, or autonomous decision agents, these patterns will help you build something robust, transparent, and safe.
Modular orchestration with event-driven workflows
Traditional pipelines fall short when building agents that must respond to dynamic, evolving contexts. Gravity tackles this by embracing event-driven architecture and modular orchestration. Agents are modeled as independent services that react to discrete events, allowing the system to flexibly coordinate multiple actors across different stages of a task.
