Most AI agent frameworks perform in MVPs but collapse in production. Download this white paper to uncover: Why AI Agents fail for existing frameworks under real-time load, How Rust + Python hybrid architecture fixes concurrency and orchestration bottlenecks, and Benchmarks showing 5–7× efficiency gains at scale.
Enterprises want to scale with Agentic AI but Python-centric frameworks aren't keeping up. Bottlenecks in concurrency, fragile orchestration and debugging overhead prevent adoption. With GraphBit, enterprises finally get predictable, stable, and ultra-efficient execution.
Ensuring consistent performance under varying loads and conditions while maintaining service quality.
Maximizing processing capacity and transaction volume to handle enterprise-scale operations.
Optimizing resource utilization and operational costs while maintaining high performance standards.
Building resilient systems that minimize downtime and ensure continuous operation in production environments.
“Most agent frameworks work in MVPs but falter in production. By combining Rust's systems-level efficiency with Python accessibility, GraphBit ensures enterprise AI runs with stability, predictability, and scale.”
Founder and CEO
GraphBit
Current AI agent frameworks face critical limitations that prevent enterprise adoption and scalability.
Tools crash under real-time load and high-demand scenarios.
Agents lose mid-task context, breaking workflow continuity.
Missing support for parallel processing and multi-threading.
Debugging and patching waste valuable engineering hours.
Cross-platform stress tests show GraphBit consistently combines efficiency, predictability, and stability, lowering both infrastructure and operational costs.
% CPU Usage (Lower is Better)
MB per Task (Lower is Better)
Tasks/min (Higher is Better)
% Stability (Higher is Better)
Download the full white paper and discover why GraphBit is the backbone for enterprise agentic AI.
