Enterprise integration estates now span APIs, databases, EDI, event streams and cloud data pipelines. Much of a developer’s time goes in configuration mechanics rather than integration design – and valuable knowledge sits with a few experienced people.
Integration teams lose time in five places. They rebuild the same profiles, connections, maps and deployment packages on every project, trace inherited logic one component at a time, correlate logs by hand to diagnose failures, depend on a few senior developers for platform know-how, and maintain documentation that drifts away from what is deployed.
AI coding agents can help, but general-purpose agents lack Boomi-specific platform knowledge.
Boomi Companion closes that gap. It is an open-source suite of Agent Skills that equips an AI agent that a developer already uses, with Boomi knowledge and tools, so that integrations can be built, modified, tested, deployed and diagnosed through natural language [1]. This white paper maps Companion’s documented capabilities to recurring engineering problems, presents Psiog’s field evidence (that includes a full runtime migration) and sets out the controls and adoption steps needed for responsible use.
What Psiog has seen
Across Integration, Master Data Hub, API Management and Event Streams work, tasks took about 82% less time using Companion than doing them manually. For runtime Migration, every process on one runtime was moved to a new Azure runtime from a single prompt in 4 hours instead of an estimated 4 person-days (Section 5). Across seven typical tasks, effort fell from 75 hours to 13.75 hours, and every task saved between 70% and 87% even after counting review and validation time (Table 2).
What to keep in view
Companion is a developer offering without a support SLA, covers only part of the platform, and exchanges data with the developer’s AI provider [2]. Psiog’s figures are effort estimates, not benchmarks. Companion does not yet cover Flow, API Management or Runtime Management, so some work stays in the UI (Section 3.4). Component metadata, execution logs and pasted payloads go to the AI provider, and every generated component, explanation and fix still needs review by its owner (Section 7).
Recommended next step
A time-boxed proof of concept in a non-production environment with defined metrics (Section 6). Psiog recommends four phases (prepare, pilot, measure and scale), starting with read-only analysis under scoped roles, using Plan mode for builds, and keeping peer review and release controls in place before anything reaches production.
