5. November 2025 By Annette Kauppinen and Ville Vuorio
From Assistant to Collaborator - How AI Is Changing the Craft of Coding Part 5
Blog 5 of 6 — Custom AI Workflows: Turning Generic Agents into Specialized Teammates
One of the great promises of AI coding agents is their flexibility. But flexibility without guidance is just complexity.
For Ville Vuorio, Technical Architect at adesso Finland, the real value of tools like Roo Code and Cline became clear through hands-on experience at client projects. “Out of the box, these tools are helpful,” he says. “But once we aligned them with the client's workflow, they became powerful.”
Most AI tools start as general-purpose assistants—able to generate code, explain syntax, or suggest improvements. That’s useful. But in real-world development environments, one-size-fits-all rarely works for long. Every team has its own stack, internal conventions, security standards, and review flows.
To fully integrate AI into daily work, it needs to be taught how the team works.
Why customization matters
Without tailoring, AI agents behave like interns without onboarding. They may generate code that violates internal policies, misalign with architecture principles, or create friction with existing DevOps flows.
“The agent needs to understand more than syntax,” Ville explains. “It needs to follow how the team delivers software.”
That includes:
- How the pipeline is structured
- Which frameworks are in use
- What coding or security standards must be enforced
- Who is responsible for what in the review process
Customization makes the agent not just smart—but practical and trustworthy.
Roo Code & Cline: built for adaptability
Unlike many closed AI systems, Roo Code and Cline are designed to be extended and adapted. Both support the Model Context Protocol (MCP) — an open standard that allows agents to interact with tools, APIs, and services in modular ways.
This makes it possible to:
- Create specialized agent personas, like a “Security Auditor” or “Architecture Reviewer”
- Set up workflow triggers, such as auto-running tests or notifying reviewers
- Connect with Jira, GitHub, CI/CD pipelines, or internal tools
- Build role-specific modes, like “Debug Mode” or “Ask Mode” for explanation-driven development
In customer projects, Ville has used these capabilities to align AI support with real-world team workflows, standards, and constraints.
“It’s like giving the agent a real job description,” he says. “We’re not just experimenting—we’re assigning tasks.”
Natural language meets structure
Roo Code and Cline also bridge natural language with system-level automation. Developers and non-technical users alike can define tasks using plain English—or Finnish—but behind the scenes, the agents can interact with structured systems.
“You don’t need to program the agent,” Ville notes. “But you do need to be intentional in how you guide it.”
That’s why architects, DevOps leads, and security experts play a critical role—not just in adopting AI tools, but in shaping how those tools behave.
Tailored agents build trust
Perhaps the most underrated benefit of customization is that it creates trust. Developers are much more likely to rely on an AI agent that:
- Follows internal standards
- Understands project structure
- Fits into existing workflows and tools
Ville sums it up well: “The more the AI understands the context it’s working in, the more the team can trust what it produces.”
And once trust is in place, efficiency and adoption follow.
Next in the series:
In Blog 6, the final part of this series, we’ll zoom out. What does the rise of AI coding agents mean for the developer’s role, for leadership, and for the craft of software itself?
