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As large language models (LLMs) evolve from static responders into autonomous actors, builders are going through a brand new form of programs problem: constructing infrastructure that may assist reasoning, decision-making, and steady motion. Gravity’s agentic AI platform is among the most superior real-world examples, a system the place LLMs work together with instruments, reminiscence, and guardrails to execute advanced, multi-step workflows.
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Regardless of the challenges, builders can construct a system like Gravity’s from the bottom up, masking modular orchestration, behavioral security, observability, and the mixing of LLMs with enterprise logic. Whether or not you’re designing clever assistants, AI copilots, or autonomous choice brokers, these patterns will make it easier to construct one thing strong, clear, and protected.
Modular orchestration with event-driven workflows
Conventional pipelines fall brief when constructing brokers that should reply to dynamic, evolving contexts. Gravity tackles this by embracing event-driven structure and modular orchestration. Brokers are modeled as unbiased companies that react to discrete occasions, permitting the system to flexibly coordinate a number of actors throughout totally different levels of a activity.