Enterprises building AI‑driven agents often struggle with fragmented infrastructure: separate databases for different data types, high‑latency streaming systems, heavyweight data lake stacks, and limited observability of LLM API calls. Integrating these components requires extensive custom code, leading to increased development time, operational complexity, and higher costs. Agentnative offers a cohesive suite of infrastructure tools designed specifically for agentic AI workloads.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises building AI‑driven agents often struggle with fragmented infrastructure: separate databases for different data types, high‑latency streaming systems, heavyweight data lake stacks, and limited observability of LLM API calls. Integrating these components requires extensive custom code, leading to increased development time, operational complexity, and higher costs.
Solution
Agentnative offers a cohesive suite of infrastructure tools designed specifically for agentic AI workloads. AgentDB consolidates SQL, vector, graph, key‑value, and full‑text search into a single multi‑model database that includes built‑in agent memory and knowledge bases. AgentStream provides a Kafka‑compatible streaming engine with 10–100× lower latency and native primitives for orchestrating agents. AgentLake replaces the traditional Hadoop ecosystem with a single Go binary that supports S3‑compatible storage, Apache Iceberg tables, and a PostgreSQL‑compatible SQL engine, all within a ~100 MB footprint. AgentProxy adds zero‑code LLM API traffic management and observability across major providers, while AgentTune enables Go‑native fine‑tuning of models using LoRA/QLoRA without Python dependencies. Together, these components streamline the development, deployment, and monitoring of AI agents, reducing operational overhead and accelerating time‑to‑value.
Target Audience
Primary customers are technology teams and enterprises developing AI‑agent applications, including developers of conversational assistants, autonomous workflows, and AI‑enhanced CRM or support platforms.
Features
- AgentDB: unified multi‑model database with built‑in agent memory, supporting SQL, vector, graph, key‑value, and full‑text search
- AgentStream: Kafka‑compatible streaming engine with 10–100× lower latency and native agent orchestration primitives
- AgentLake: single‑binary data lake (~100 MB) offering S3‑compatible storage, Apache Iceberg tables, and PostgreSQL‑compatible SQL without a JVM
- AgentProxy: LLM API traffic management and observability for Anthropic, OpenAI, Google, etc., requiring no code changes
- AgentTune: Go‑native fine‑tuning framework supporting LoRA, QLoRA, multi‑GPU, and seamless integration with AgentStream
- AgentCRM and AgentSupport: ready‑to‑use AI‑powered applications for relationship management and customer support built on the same infrastructure stack