Empowering Purple Technology with Next-Gen AI Workflows

The Challenge: The "Good Enough" Plateau

Like many top-tier engineering teams, Purple's developers were already using AI. They had the basics down—ChatGPT for snippets, maybe some Copilot for autocomplete.

The Pain Point: The team was hitting the "Good Enough" Plateau. They were using AI as a tool (like a better StackOverflow), not as a collaborator. There was a disconnect between their project management (Linear), their codebases (GitHub), and their execution (IDE). The friction of context switching—copy-pasting tickets, explaining context to the AI, and manually reviewing simple tasks—was eating up valuable creative time.

The Goal: To shift the mindset from "AI as a helper" to "AI as an agentic partner." We needed to show them how to bridge the gap between a ticket in Linear and a Pull Request in GitHub with minimal friction.

The Solution: A Full-Stack AI Workshop

Together with František Dalecký, we designed a custom, hands-on workshop tailored to Purple's specific stack. This wasn't a lecture; it was a live hacking session.

We focused on transforming how the team thought about AI—moving from occasional use to deep integration into their daily workflow.

Phase 1: The Foundation (Conversational to Contextual)

We started by auditing the current stack. The "aha" moment here wasn't about a new tool, but a new method.

Prompt Engineering 2.0: Moving beyond simple queries to structured, context-heavy prompts that minimize hallucinations.

The Context King: Demonstrating how to feed the right files, docs, and architectural patterns into the context window so the AI "thinks" like a Purple Technology senior engineer.

Phase 2: The IDE Revolution (Cursor)

This is where the eyes started to light up. We dove deep into Cursor, the AI-first code editor.

Deep Integration: We showed how Cursor isn't just VS Code with a chatbox. We utilized its codebase indexing to ask complex questions like "Where is the auth logic for X and how do I refactor it to support Y?"

Tab Autocomplete on Steroids: Demonstrating how Cursor predicts next edits, not just next words.

Phase 3: The Agentic Workflow (Linear + GitHub)

This was the core of the workshop. We tackled the "glue" problem.

The Workflow: We took a real ticket from Linear.

The Magic: Instead of writing code manually, we used AI agents to read the Linear ticket, understand the requirements, plan the changes, and execute them in the codebase.

The Result: A seamless flow where the AI acts as the junior dev who prepares the work, allowing the senior dev (the human) to focus on architecture and review.

"We watched AI automatically transform tasks from Linear into ready-to-review code and create pull requests without missing a beat." — Tomas Zaoral, Purple Technology

Phase 4: The Frontier (MCP & Devin)

We pushed the boundaries with Model Context Protocol (MCP) and Devin.

MCP Servers: We introduced how to build and use MCPs to give AI tools direct access to internal APIs and databases, effectively giving the AI "hands" to do work, not just "eyes" to read code.

Devin AI Demo: The showstopper. We unleashed Devin on a complex task. The room watched as Devin autonomously: (a) Read the task, (b) Planned the execution, (c) Wrote the code, (d) Fixed its own errors, and (e) Opened a PR on GitHub.

The "Aha!" Moment

The turning point came during the Linear to PR demonstration.

The developers watched a task move from a project management card to a ready-to-review Pull Request without a single line of code being manually typed by a human. The realization hit: This isn't about replacing developers; it's about removing the boring parts of development.

Why This Worked: Cultural Readiness

Purple's team was successful because they were culturally ready to experiment. Fear of AI prevents adoption; curiosity fuels it.

Their engineering culture, which values technological excellence and freedom to experiment, created the perfect environment for rapid adoption of these advanced workflows.

Read more about Purple Technology's developer culture and AI adoption journey in their blog post.

Conclusion

The transition from basic AI usage to deep, agentic workflows isn't just a technical upgrade—it's a paradigm shift in how engineering teams operate. For Purple Technology, this workshop successfully moved developers from using AI as a fancy autocomplete to deploying it as an autonomous partner that handles the Linear ticket → PR pipeline. The result was developers freed up to focus on high-level architecture and creative problem solving rather than grinding through repetitive implementation tasks. If your team is stuck using AI as a fancy spellchecker, you're leaving 50% of your productivity on the table.