A production platform that monitors, debugs, and hardens LLM?-powered agent workflows when they break against real-world data.
Added May 23, 2026
High opportunity (82%)
Teams across data, product, security, and operations are racing to build LLM?-powered agents, RAG? pipelines, and tool-using workflows, but these systems frequently break when they meet messy real-world data and production environments. Engineers lack purpose-built tooling to detect, diagnose, and prevent these failure modes at scale.
A platform that instruments agent workflows (including multi-agent and A2A orchestration) to trace tool calls, capture data-grounding failures, and surface regressions across LLM? providers like OpenAI, Anthropic, and Vertex AI. It provides evaluation harnesses, replay/debugging, and guardrails so engineering teams can ship agents into production with confidence instead of one-off glue code.
Job postings across data infrastructure, SaaS?, fintech, aerospace, and observability companies are simultaneously demanding hands-on experience deploying LLM? agents in production, signaling that agentic workflows have moved from prototypes to load-bearing systems that need dedicated reliability tooling.
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Develop and integrate agentic workflows and LLM-powered applications (e.g., RAG systems, tool-using agents) Monitor model performance in production (drift, latency, cost, and quality) and iterate accordingly
Agentic remediation systems — LLM-powered agents that detect, diagnose, and resolve operational issues automatically, replacing manual runbooks with self-executing guidance. Self-healing deployment platforms — safe primitives, automated integration testing, and pipeline intelligence that drive teams toward fully continuous, hands-off delivery.
A track record of shipping AI or agent products that people rely on, with clear ownership of the engineering behind them. You understand how these systems work in depth: LLM agents, orchestration, tool calling, RAG, and the evaluation and monitoring that keep them dependable.
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