Ideas Signals Validator

60 Signals

AgentOps Reliability Control Plane

A SaaS platform that tests, monitors, and debugs LLM agent workflows before and after deployment.

Added Jun 7, 2026

AI Infrastructure

Developer Tools

Observability

Opportunity score

High opportunity (86%)

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The Problem

Companies are moving from simple LLM integrations to agentic systems that reason, plan, call tools, and coordinate across workflows. These systems often fail unpredictably against real data, making it hard for engineering teams to evaluate behavior, trace failures, and operate agents reliably in production.

Potential Solution

Build an observability and evaluation control plane for LLM-powered agents and multi-agent workflows. The product would capture agent traces, tool calls, context usage, failures, and evaluation results, then surface where agents break and help teams compare prompts, models, orchestration strategies, and infrastructure changes before deployment.

Why Now?

Multiple companies are hiring for engineers with experience building, deploying, and operating LLM agents, multi-agent systems, LangChain/LangGraph applications, and AI platform infrastructure. This suggests agentic AI is moving from experimentation into production operations, where reliability tooling becomes necessary.

Market validation

Search demand

Trend snapshot pending

Competition (0)

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Showing 1-20 of 60 signals

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 11, 2026

yuno

Machine Learning Engineer

Agentic integration for ML: design and integrate agentic workflows (LLM based agents, tool calling pipelines) alongside traditional ML models; build the observability, guardrails and evaluation frameworks needed to run agentic systems reliably in production; explore how agents can automate parts of the ML lifecycle itself (monitoring, triage, retraining decisions).

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 adsSep 2, 2026

pointclickcare

Principal AI Engineer

- Drive cross-functional roadmaps and integration standards across business teams—setting API/versioning contracts, optimizing LLM/agent cost performance, and mentoring engineers to raise the bar. - Establish and evolve evaluation frameworks for LLMs and agentic systems, including offline and online testing methodologies, safety assessments, quality metrics, and business outcome dashboards.

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