---
title: "How IdeaBot drives AI innovation workflows on OpenShift"
description: "Red Hat’s emerging technologies team built IdeaBot, an AI-enabled SaaS platform on OpenShift that automates innovation workflows from initial concept to a standardized idea report. The platform guides users through four stages—discovery, research, refinement, and synthesis—using conversational interviews, automated research across internal and external sources, a curation board with confidence scoring, and a synthesis view that compiles deliverables like executive summaries and market analysis. Security enforces session isolation in dedicated pods, credential isolation through an auth proxy and token service that injects short-lived tokens, and stateless compute with PostgreSQL persistence and a warm pod pool to reduce cold starts. Development uses EARS syntax and Gherkin feature files to drive behavior-driven development, enabling automated test generation and reproducible builds. Red Hat plans to extend IdeaBot toward fully autonomous, reproducible agentic SDLC pipelines using factory patterns."
---

# How IdeaBot drives AI innovation workflows on OpenShift

[Red Hat](https://yomu.fyi/company/red-hat) · Tom Coufal · Oct 7, 2026

**Type:** Explainer

## Summary

Red Hat’s emerging technologies team built IdeaBot, an AI-enabled SaaS platform on OpenShift that automates innovation workflows from initial concept to a standardized idea report. The platform guides users through four stages—discovery, research, refinement, and synthesis—using conversational interviews, automated research across internal and external sources, a curation board with confidence scoring, and a synthesis view that compiles deliverables like executive summaries and market analysis. Security enforces session isolation in dedicated pods, credential isolation through an auth proxy and token service that injects short-lived tokens, and stateless compute with PostgreSQL persistence and a warm pod pool to reduce cold starts. Development uses EARS syntax and Gherkin feature files to drive behavior-driven development, enabling automated test generation and reproducible builds. Red Hat plans to extend IdeaBot toward fully autonomous, reproducible agentic SDLC pipelines using factory patterns.

## Context

Red Hat’s emerging technologies team needed a structured way to vet new ideas for open source projects and product integration, ensuring each concept aligned with the portfolio and addressed real needs. IdeaBot was created as an AI-enabled platform on OpenShift to automate the incubation, research, and refinement process, codifying institutional knowledge into an enablement tool. Key challenges included user adoption, over-automation, and security of sensitive internal data.

## Approach / What changed

IdeaBot is a four-stage workflow: discovery uses a conversational interface to define the problem and audience; research dispatch runs automated agents across isolated internal and external sources; refinement board lets users curate findings with confidence scores and revalidation; synthesis view compiles deliverables. Security: session isolation in dedicated pods, credential isolation via an auth proxy and token service, and stateless compute with PostgreSQL persistence and a warm pod pool. Development uses EARS syntax with Gherkin for BDD to generate automated tests and reproducible builds.

## Takeaways

- IdeaBot’s workflow has four stages: discovery, research, refinement, and synthesis, with a conversational interview at discovery and a curation board with confidence scores at refinement.
- Security architecture uses session isolation in dedicated pods, credential isolation via an auth proxy and token service, and stateless compute with PostgreSQL persistence and a warm pod pool.
- Development uses EARS syntax and Gherkin feature files to drive behavior-driven development, enabling automated test generation and reproducible builds.

## FAQ

### How does IdeaBot protect sensitive internal data?

IdeaBot enforces session isolation by running each session in a dedicated pod with an isolated network namespace. Credential isolation is achieved through an auth proxy sidecar that strips inbound credentials and injects short-lived tokens from a token service, while stateless compute persists state in PostgreSQL and uses a warm pod pool to reduce cold starts.

### What are the four stages of IdeaBot's workflow?

The four stages are discovery, research, refinement, and synthesis. Discovery uses a conversational interview to define the problem and audience, research dispatches automated agents, refinement provides a curation board with confidence scores, and synthesis compiles deliverables like executive summary and market analysis.

### How does IdeaBot ensure reproducibility in its own development?

IdeaBot uses EARS syntax and Gherkin feature files to define requirements in a behavior-driven development approach. This enables automated test generation and reproducible builds, ensuring the entire application can be re-created seamlessly without manual intervention.

**Tags:** [AI](https://yomu.fyi/topic/ai), [LLMs](https://yomu.fyi/topic/llm), [OpenShift](https://yomu.fyi/topic/openshift), [Security](https://yomu.fyi/topic/security)

- Source: [Red Hat](https://developers.redhat.com/articles/2026/10/07/how-ideabot-drives-ai-innovation-workflows-openshift)
- Source URL: https://developers.redhat.com/articles/2026/10/07/how-ideabot-drives-ai-innovation-workflows-openshift
- Ingested by Yomu: 2026-10-07T15:00:09.953Z

[Read original post](https://developers.redhat.com/articles/2026/10/07/how-ideabot-drives-ai-innovation-workflows-openshift)