A developer platform for building, testing, and monitoring multi-step LLM? agents, RAG? pipelines, and tool-calling workflows.
Added May 31, 2026
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High opportunity
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Companies are hiring engineers who can build agentic LLM? systems that go beyond simple API? calls, including RAG?, orchestration, tool use, and failure handling. These workflows are complex to design and maintain because prompts, retrieval, function calls, and multi-step reasoning all interact in production.
AgentFlow Reliability Workbench would provide a SaaS? environment to prototype agent workflows, connect tools and vector stores, run regression tests, and inspect failures across each reasoning step. It would help product and AI engineering teams validate prompts, RAG? responses, tool calls, and orchestration logic before and after deployment.
Multiple companies are explicitly hiring for hands-on LLM?, RAG?, agent workflow, and AI-powered analytics experience, signaling that agentic AI systems are moving into production. As adoption grows, teams need reliability tooling rather than only model APIs? and custom code.
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Develop and enhance AI-powered features, including LLM integrations, retrieval-augmented generation (RAG), tool calling and agent-based workflows. Implement automated testing, troubleshoot production issues and maintain software quality.
Design, build, and deploy AI-powered solutions (Single and Multi-Agent systems with Retrieval-augmented generation (RAG); versioned prompt libraries; skills; evals) using Google’s Agent Development Kit. Deliver high-quality code and tests, ensuring the reliability, security, and accuracy of deployed solutions, including testing and evaluation frameworks for deterministic and model-based outputs.
Implement Agentic frameworks to orchestrate LLM interactions with internal APIs, databases, and software tools. Build and maintain agent evaluation harnesses to benchmark agent accuracy, reliability, and task success across complex, multi-step workflows.
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