A production orchestration platform for building, testing, and running long-running multi-agent workflows across enterprise tools.
Added Jun 12, 2026
Last signal 11h ago
Companies are hiring engineers to build action queues, playbooks, Salesforce Flows, and agentic workflow systems because multi-step AI automation is hard to make reliable in production. Teams struggle with conditional logic, long-running workflows, external API? coordination, testing, CI/CD, and clear boundaries between agents, tools, and business systems.
AgentOps Workflow Orchestration Console provides a visual and code-friendly environment for designing modular agent workflows with queues, conditional branches, retries, approvals, and external API? integrations. It includes testing, observability, versioning, CI/CD hooks, and runtime reliability controls so enterprises can deploy agent workflows without rebuilding orchestration infrastructure internally.
The job signals show multiple companies moving from simple LLM? experiments to production-grade agentic automation with dynamic decision-making and measurable outcomes. As enterprises connect agents to real business systems, orchestration, reliability, and governance become urgent platform needs.
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Comfortable translating business logic into scalable workflows, automations, prompts, agents, and operational guardrails. Highly effective working across systems such as Jira, CI, bug trackers, telemetry platforms, planning tools, and internal dashboards.
Build automation workflows that integrate applications, APIs, data sources, and enterprise platforms such as MES, ServiceNow, SharePoint, and reporting systems. Develop scalable approaches for agent orchestration, deployment, monitoring, and continuous improvement.
Agent Orchestration & Workflow Infrastructure Build and operate the infrastructure for deploying multi-step agent workflows — state management across complex reasoning chains, tool routing and execution runtimes, and long-running agentic processes that persist over time. Own the orchestration layer that coordinates agent planning, tool calls, and human-in-the-loop patterns. Design systems that handle agent failure modes gracefully: retries on ambiguous tool outputs, fallback strategies when models produce unexpected results, and observability into multi-step execution traces.
• Help the team design systems that handle long-running workflows, external APIs, and dynamic decision-making by agents .
Build declarative automation using Flows where appropriate, balancing maintainability with technical complexity
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