How to build a production-grade agentic AI platform - lessons from Gravity
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How to build a production-grade agentic AI platform - lessons from Gravity
"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. Technologies like Temporal, pub/sub messaging, or custom orchestrators can be used to handle event sequencing and retries. The key is decoupling logic."
"The key is decoupling logic. Instead of a monolithic agent, build a composable graph of task-specific mini-agents that can be audited independently. To future-proof your system, define interfaces around each task. This allows you to swap out or upgrade capabilities (e.g., replacing an LLM tool-caller with a newer model) without breaking the entire flow."
Agentic systems require modular orchestration, fail-safe design, hybrid memory management, and LLM integration with domain knowledge to reason, act, and adapt at scale. Event-driven architectures model agents as independent services reacting to discrete events, enabling composable graphs of task-specific mini-agents coordinated via Temporal, pub/sub, or custom orchestrators with retries and event sequencing. Interfaces around each task support swapping or upgrading components without breaking flows. Behavioral guardrails, observability, and fail-safes mitigate risks from unexpected agent actions. Hybrid memory and domain-informed LLM tools connect business logic, memory, and tools to execute complex multi-step workflows safely and auditablely.
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