A developer platform for building, testing, and monitoring multi-step LLM? agents, RAG? pipelines, and tool-calling workflows.
Added May 31, 2026
High opportunity (82%)
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.
Trend snapshot pending
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Showing 1-20 of 35 signals
- Build and continuously improve intelligent Agents powered by Large Language Models (LLMs). Apply Prompt Engineering, RAG, MCP, Skills, multi-Agent orchestration, tool calling, and related techniques to intelligent fault diagnosis, root cause analysis, automated remediation, and intelligent customer service.
* Build agentic AI workflows using LLM agents, tool and function calling, and orchestration frameworks such as LangGraph, Semantic Kernel, AutoGen, or the Model Context Protocol, applied to autonomous fault detection, triage, and remediation.
Develop production-grade applied AI-powered applications and developer tools, including LLM workflows using RAG, tool calling, structured outputs, agents, and human-in-the-loop patterns
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