A SaaS testbench that validates LLM agents against real enterprise data workflows before they reach production.
Added May 24, 2026
Last signal 1w ago
Companies are hiring engineers who know where LLM agents fail when connected to real data, tools, and production workflows. The repeated need for agentic workflows, RAG pipelines, MCPs, A2A protocols, and multi-agent orchestration suggests teams struggle to evaluate reliability before embedding agents into products and operations.
Build a validation platform where teams define real data tasks, tool calls, expected outcomes, failure modes, and regression suites for LLM-powered agents. The product would run scenario tests across OpenAI, Anthropic, Vertex AI, MCP-based tools, RAG pipelines, and multi-agent workflows, then produce reliability reports for pre-sale POCs, production readiness, and ongoing monitoring.
Job postings show enterprise teams are moving from experimentation to production deployment of LLM agents inside products, IT systems, security workflows, and customer-facing platforms. As agent workflows become operational infrastructure, reliability testing becomes a direct buying need.
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Showing 1-19 of 19 signals
- Help build and maintain benchmark datasets to evaluate agent performance, accuracy, and safety - Implement, test, and optimize services and tooling across the stack — backend, data, scripting, automation, and glue code
Develop AI agents using the latest LLMs and contribute to model training workflows Test solutions with rigor and monitor deployed models for accuracy and data drift
Design and implement test strategies, validation approaches, and release readiness criteria for AI-enabled software, automation, and agentic workflows. Partner closely with software and AI engineers to identify failure modes early across code, prompts, models, integrations, infrastructure, and user workflows.
AI Implementation Experience: Proven experience building and maintaining AI agentic workflows or MCPs . You know how to leverage LLM APIs (Vertex AI/OpenAI) to build practical, task-oriented agents.
Designing and integrating LLM-powered systems — including agents, copilots, and tool-using workflows — into production environments
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