LLM Agent Reliability and Observability Platform
45 Signals

LLM Agent Reliability and Observability Platform

A production platform that monitors, debugs, and hardens LLM-powered agent workflows when they break against real-world data.

Added May 23, 2026

AI Infrastructure
Developer Tools
Observability
Opportunity score

High opportunity (81%)

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The Problem

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.

Potential Solution

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.

Why Now?

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.

Market validation
Search demand

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Showing 1-20 of 45 signals

Job adsSep 18, 2026
safe
SDE III - Engineering Productivity (AI)

LLM APIs and model orchestration · production AI agents with tool use · RAG, retrieval, and vector databases · MCP or similar tool/context protocols · LLM evaluation, prompt and context engineering, AI observability · GitHub/GitLab APIs and deep CI/CD integration · Kubernetes and cloud platforms · internal developer platforms · DORA/SPACE-style engineering measurement · security or compliance-bound environments.

Job adsSep 12, 2026
klaviyo
Lead Engineer, Applied AI (Customer Agent)

Experience working on a multi-tenant agent platform at production scale, where external customers configure their own agent behavior on shared infrastructure. A track record with production evaluation systems, AI observability, or human-in-the-loop workflows for LLM-powered products.

Job adsSep 7, 2026
pinterest
Sr. Staff Software Engineer, Pinterest Assistant

Shape the architecture for AI-powered applications, including retrieval, context assembly, tool execution, memory, and model orchestration, in partnership with ML teams. Establish engineering best practices for production LLM and agent systems, including observability, logging, evals, safety mechanisms, rollout strategies, and incident response.

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