As giant language fashions (LLMs) evolve from static responders into autonomous actors, builders are dealing with a brand new form of techniques problem: constructing infrastructure that may help reasoning, decision-making, and steady motion. Gravity’s agentic AI platform is without doubt one of 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, overlaying 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 resolution brokers, these patterns will assist you to construct one thing strong, clear, and protected.
Modular orchestration with event-driven workflows
Conventional pipelines fall quick 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 impartial companies that react to discrete occasions, permitting the system to flexibly coordinate a number of actors throughout completely different levels of a job.
