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

Trend snapshot pending

Competition (0)

No matched competitors yet

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

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.

Job adsSep 4, 2026

smart-valor

ML/AI Engineer

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

Job adsAug 30, 2026

amazon

Software Development Engineer II, FBA

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.

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LLM? Agent Reliability and Observability Platform