Customers
Built on agentmatic.
Anonymized stories from teams running agentmatic in production. Real workflows, real numbers, no inflated logos.
Distributed RAG across 12 worker nodes, all in their VPC — no SaaS dependency.
A Fortune 500 manufacturer needed RAG over 4M technical documents with strict data-residency requirements. They deployed Agentmatic's distributed runtime across 12 worker nodes in their VPC.
- 12-node coordinator/worker cluster serving 6,800 queries/day across business units.
- Zero data-residency incidents — no documents or queries leave their VPC.
- Median query latency 1.8 s; p95 4.2 s including retrieval + reranking + generation.
Built a production code review agent in 2 weeks using MCP + ReAct.
An 8-person dev-tools startup shipped an AI code reviewer using Agentmatic's MCP client and the prebuilt ReAct agent. From kickoff to GA in 14 days.
- Shipped to production in 14 days, with one engineer working part-time.
- MCP filesystem + git servers gave them a tested toolset on day one.
- Comment-quality eval: 78% accepted by human reviewers (target was 60%).
Cut support p95 latency from 9.2 s to 1.4 s — same prompts, same tools, same model.
A US consumer fintech replaced their LangGraph supervisor with Agentmatic. The graph, prompts, and tools stayed identical; only the runtime changed. Latency dropped 6.5×.
- Support p95 latency 9.2 s → 1.4 s on the same OpenAI model.
- Compute spend on the support service down 38% (smaller event-loop, fewer worker pods).
- Zero customer-facing regressions across 4,200 conversations in the first week.
Ship your next agent in minutes, not weeks.
MIT licensed. Drop-in for LangGraph. Native SDKs in 5 languages. Battle-tested resilience primitives in the box.