A developer platform for building, testing, and deploying AI agents that connect reliably to enterprise tools, data sources, and multi-step workflows.
Added May 31, 2026
High opportunity (79%)
Companies are hiring engineers to co-build production AI agents because integrating agents into real business systems is still difficult. Teams struggle with messy internal tools, cross-app orchestration, failure handling, and turning prototypes into reliable workflows used across engineering, operations, marketing, legal, compliance, and customer-facing teams.
The product provides a harness for designing agentic workflows, connecting agents to enterprise apps and data sources, testing multi-step decisions, and monitoring failures before deployment. It would include reusable integration templates, workflow simulation, tool-permission controls, and observability for agent actions across systems.
Multiple companies are explicitly hiring for production agent orchestration, internal agent tooling, and multi-agent workflow oversight. The market is shifting from simple LLM? API? calls toward operational AI agents embedded in existing workflows.
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Design, implement, and deploy end-to-end AI workflows and agents that solve real problems across multiple business domains Develop and iterate on agent architectures, evaluation pipelines, and performance frameworks to ensure reliability and measurable outcomes
Internal builder platforms: Create reusable frameworks that let teams quickly build their own internal tools, dashboards, automations, and agents. Design, build, and operate the core platform behind agent-driven software development at Decagon.
Agent-native developer infrastructure: Design CI/CD, local development, APIs, CLIs, and platform tooling that can be operated directly by both engineers and agents. Internal builder platforms: Create reusable frameworks that let teams quickly build their own internal tools, dashboards, automations, and agents.
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