Has Agile lost its way? How AI-powered DevSecOps can help [Q&A]

Agile. What started 25 years ago as a movement for responsiveness and customer value now often gets bogged down by backlogs, burndowns, and bloated frameworks. Teams find themselves saying, “we’re Agile, but…” — a clear sign of compromise.

So has Agile lost its way and what can be done about it? We spoke to Bryan Ross, field CTO at GitLab, who argues that the future of Agile lies not in replacing it, but in using AI platforms to finally deliver on its founding principles.

BN: After 25 years of Agile, what are the main challenges facing traditional Agile planning today?

BR: After 25 years, rigid processes and administrative headaches have overshadowed the promise of Agile planning. Traditional Agile planning is now showing its age, weighed down by backlogs, burndowns, and technical debt.

As Agile scaled across businesses, something was lost in translation, leading to frameworks like the Scaled Agile Framework (SAFe) that tried to bridge the gap between team-level agility and enterprise requirements. As a result, the software industry’s most dangerous phrase moved from “we’ve always done it this way” to “we’re Agile, but…,” signaling a compromise of the principles that made Agile groundbreaking in the first place.

However, the core tenets of Agile — responsiveness, iteration, and a focus on customer value — remain profoundly relevant. The challenge lies in executing them at scale within complex organizational structures. It’s not about replacing Agile, but about enabling a new generation of tools that embody Agile ideals.

BN: How can AI-powered platforms help teams return to Agile’s core principles rather than replace them?

BR: My journey with Agile started with thought leaders such as Jon Kern, one of the original signatories of the Agile Manifesto, who taught me that customer-centricity and delivery are more effective than documentation. This mindset helped me to build small, high-performing teams that delivered huge results through rapid iteration and customer feedback. But I’ve also experienced firsthand how these principles can get lost in enterprise environments.

Today’s AI-powered platforms offer a path back to those fundamental ideals. Enter Multi-Agent Collaboration Platforms, integrated environments where AI agents coordinate to scan codebases, analyze customer feedback, and suggest fixes. This coordinated intelligence enables teams to respond to real-time insights.

Imagine AI systems that can analyze customer feedback, support tickets, and usage patterns, using that data to automatically identify and cluster related issues into meaningful epics without marathon planning sessions. These systems could then intelligently decompose epics into right-sized stories based on data about team velocity and dependencies, allocating them to sprints that optimize for both business value and technical coherence.

BN: What are some real examples of how AI can reduce the administrative burden of Agile, like sprint planning and backlog grooming?

BR: AI could minimize the burden of manual backlog grooming, estimation poker, and sprint planning to brief validation sessions where human creativity and strategic thinking focus on the ‘why’ rather than the ‘how.’ Teams could spend more time delivering value than examining how to deliver value.

Here’s a real-world example. European software development company Cube found that adopting a unified platform strategy that enabled AI to work across every stage of the development lifecycle significantly enhanced both development speed and code quality. This aligns with a recent UK C-Suite Survey: The Economics of Software Innovation, which found that AI-powered software innovation is saving £11K per developer annually.

None of this is about removing human judgment from Agile; it’s about uplifting it from administrative burden to strategic guidance, enabling teams to truly embrace the responsive, value-focused delivery that Agile originally promised.

BN: Can you explain what Multi-Agent Collaboration Platforms are and how they differ from traditional planning tools?

BR: These are lightweight issue management systems that seamlessly integrate with the entire development lifecycle, which are already replacing monolithic planning tools with complex workflows. When issue tracking lives alongside code repositories, CI/CD pipelines, and delivery mechanisms, we create an environment where AI can truly enhance our workflows.

This integrated platform approach enables a key shift in how we plan and execute. A few potential applications:

• Remediating Planning Powered by AI: Rather than viewing security as a separate workflow, intelligent AI tools can automatically create remediation issues from vulnerability scans, prioritize them by risk level, and schedule them alongside feature work. This ensures that security debt doesn’t accumulate in forgotten backlogs while providing clear visibility into application security posture.
• Automating Intelligent Code Review: AI can automatically analyze code changes, identify potential bugs, suggest optimizations, and verify compliance with architectural patterns, all before a human reviews them. This shifts human review time from identifying basic issues to making strategic decisions about implementation approaches.
• Orchestrating Intelligently Across Platforms: Through agent-to-agent (A2A) communication frameworks, organizations can build powerful integrations between development platforms and planning and issue management tools. These integrations let AI agents automatically synchronize data across platforms, providing a comprehensive view of development activities regardless of where planning happens. They adjust sprint allocations based on developer activity and provide early warnings when timelines or team capacity are at risk.

Capabilities like these, which we already have today, can make developers more efficient and help leadership make informed decisions. The result is a cohesive ecosystem where information flows seamlessly between planning and execution tools, eliminating the requirement for developers to context-switch between systems.

BN: What steps should teams take to assess whether AI-enhanced Agile planning would benefit their current processes?

BR: The shift toward AI-enhanced Agile planning needs a practical assessment of your current processes and toolchain.

• Begin by evaluating whether your current processes create bottlenecks between development and deployment, looking for gaps where Agile ceremonies exist but traditional approval workflows still dominate critical path decisions.
• Next, determine how much time your teams spend on planning ceremonies versus actual development work. Consider whether AI could automate administrative tasks, such as backlog grooming, estimation sessions, and status updates, while maintaining human strategic input on priorities and technical decisions.
• Examine your current toolchain to determine where you must manually coordinate the planning, development, and deployment phases. Look for opportunities where AI can automate data synchronization and provide predictive insights into capacity and timeline risks, limiting the context switching that fragments developer focus.
• Finally, review your current planning overhead and identify which administrative tasks can be automated, allowing your team to focus on delivering customer value and making strategic technical decisions rather than adhering to process compliance. The goal is not to remove human judgment but to elevate it from routine tasks to the strategic thinking that drives innovation.

The future belongs to teams that take advantage of lightweight, AI-enabled platforms, where planning, code, and delivery coexist in a single, integrated environment.

Image credit: Elnur_/depositphotos.com