[DevoxxGR2026] The Pragmatic Path: Structured Adoption of Agentic AI in Software Development
Lecturers
Dimitris Papageorgiou and Konstantina Mavrodimitraki are Senior Solutions Architects at Amazon Web Services in Greece. With extensive experience as software and data engineers, they have supported numerous enterprise customers in adopting cloud-native and AI technologies. Their work focuses on practical implementation strategies that deliver measurable business value while addressing real-world concerns around quality, security, and team readiness.
Abstract
Dimitris Papageorgiou and Konstantina Mavrodimitraki present a pragmatic framework for integrating agentic AI into software development lifecycles. Based on hands-on implementations across multiple customer environments, the session addresses common barriers such as code quality fears, lack of structure, and resistance to change. Through concrete examples—including optimized code reviews returning over 16,000 developer hours annually and 65-80% faster issue resolution—they outline a phased approach from individual experimentation to cross-team standardization and organizational scaling.
The Current State of AI Adoption in Development Teams
Many organizations purchase AI tool licenses and distribute them broadly, expecting immediate productivity gains. In practice, developers experiment individually—often engaging in “vibe coding”—without shared practices or metrics. This leads to fragmented adoption, inconsistent quality, and difficulty demonstrating return on investment to leadership.
The speakers identify a critical gap: while tools proliferate, teams lack a common language and structured methodology. Success requires moving beyond ad-hoc usage to deliberate integration aligned with specific pain points.
A Framework for Systematic Agentic AI Adoption
The proposed framework operates along two dimensions: organizational pain points and AI maturity levels. Pain points—such as code review bottlenecks, testing coverage, or feature development velocity—must be identified first. Maturity progresses from individual experimentation to team standardization and finally cross-team integration.
Teams begin at their current maturity level and implement solutions appropriate to that stage. For code review bottlenecks, level-one teams conduct structured experimentation with various tools, followed by retrospectives to select winners. Level-two teams document guidelines, define success metrics, and establish processes. Level-three organizations embed AI into pipelines with shared patterns and governance.
Applying the Framework: Code Reviews and Testing
For code reviews, a real-world AWS customer in betting and gaming implemented an agentic workflow using Amazon Bedrock. Pull request events trigger enrichment via data pipelines before an agent analyzes changes against coding standards, security rules, and business requirements. The system posts comments directly, with optional human validation.
Metrics showed over 16,700 developer hours returned annually, allowing focus on higher-value work. Similar patterns apply to testing: starting with AI-assisted unit test generation, teams progress to standardized pipelines and shared test patterns across the organization.
Feature Development with Spec-Driven Approaches
Spec-driven development extends AI assistance across the lifecycle. Rather than isolated prompts, teams collaborate with agents to refine requirements, architectural decisions, and task breakdowns. Amazon Q Developer exemplifies this, generating user stories, acceptance criteria, designs, and implementation tasks from high-level intents.
This approach reduces back-and-forth during sprint planning and ensures generated code aligns with broader context. Workshops help teams adapt the process to their needs, fostering ownership and continuous improvement.
Scaling and Avoiding Common Pitfalls
Successful scaling requires executive sponsorship, dedicated time for experimentation, and clear metrics. Leadership must treat AI adoption as a strategic initiative rather than a side project. Engineers should share learnings and metrics to build momentum.
Pitfalls include unstructured experimentation leading to technical debt, over-reliance on AI without human oversight, and failure to measure impact. The speakers recommend divide-and-conquer: tackle one pain point thoroughly before expanding.
Conclusion: AI as a Multiplier of Good Practices
Agentic AI amplifies existing strengths in clean code, testing, documentation, and collaboration. By following a pragmatic, maturity-aligned path, teams achieve faster delivery, higher quality, and greater developer satisfaction. The framework transforms AI from a hype-driven experiment into a structured capability delivering tangible results.