By Lee Faus, Field CTO at GitLab
After 25 years of backlogs, burndowns, and technical debt, it’s time to confront an uncomfortable truth: traditional Agile planning is showing its age. The most dangerous phrase in software development isn’t “we’ve always done it this way,” it’s “we’re Agile, but…”
As Agile practices have scaled across enterprises, something was lost in translation, which gave rise to frameworks like the Scaled Agile Framework (SAFe) that attempted to bridge the gap between team-level agility and enterprise needs, but instead compromised the principles that originally made Agile groundbreaking.
The core tenets of Agile-transparency, experimentation, and a laser focus on delivering value to customers-are still profoundly relevant. The biggest challenge for enterprises is to implement these principles at scale while navigating the inherent complexity of a larger organization. Rather than seeking to replace Agile, organizations should strive to enable the next evolution of tools that embody Agile principles.
AI-Powered Platforms
My journey with Agile began alongside 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 impactful focus areas than documentation. This mindset enabled me to build small, high-performing teams that delivered outsized results through rapid iteration and customer feedback. However, I’ve also seen how these principles can get mistranslated in enterprise environments.
Today’s AI-powered platforms enable organizations to revisit foundational ideals, even in larger and more complex environments. For example, Multi-Agent Collaboration Platforms are integrated environments where AI agents work together to scan codebases, analyze customer feedback, and suggest solutions. This coordinated intelligence enables teams to stay focused on real-time insights.
The next generation of AI systems will be able to analyze customer feedback, support tickets, and usage patterns, using those to automatically identify and cluster related issues into meaningful epics without marathon planning sessions. These systems could then intelligently deconstruct epics into digestible stories based on data about team velocity and dependencies, allocating them to sprints that optimize for both business value and technical coherence.
The manual toil of backlog grooming, estimation poker, and sprint planning could be transformed into brief validation sessions, allowing human creativity and strategic thinking to focus on the ‘why’ rather than getting bogged down in the ‘how.’ Teams could spend more time delivering value than discussing how to deliver value.
Here’s a real-world example. European software development company Cube found that by adopting a unified platform strategy, where AI could work across all stages of the development lifecycle, they significantly improved both development speed and code quality.
It isn’t about removing the human element from Agile; it’s about elevating it from administrative burden to strategic guidance, allowing teams to truly embrace the responsive, value-focused delivery that Agile originally promised.
Planning in an AI-Powered World
Existing planning tools with complex workflows are already being replaced by lightweight issue management systems that seamlessly integrate with the entire development lifecycle. Combining issue tracking with code repositories, CI/CD pipelines, and delivery mechanisms creates an environment where AI can truly enhance our workflows.
This integrated platform approach enables a fundamental shift in how we plan and execute. A few potential applications:
- AI-Driven Security Remediation Planning: Instead of treating security as a separate workflow, intelligent AI tools can automatically create remediation issues from vulnerability scans, prioritize them based on risk assessment, and intelligently schedule them alongside feature work. This ensures that teams have direct visibility into their application security posture while also keeping security debt.
- Intelligent Code Review Automation: AI can automatically analyze code changes, surface potential vulnerabilities, identify opportunities for optimization, and ensure software adheres to compliance regulations-all before a human developer reviews the code. This enables human reviewers to make strategic decisions rather than spend their time finding basic issues.
- Intelligent Cross-Platform Orchestration: Through agent-to-agent (A2A) communication frameworks, organizations can create powerful integrations between development platforms and planning and issue management tools. These integrations enable AI agents to automatically synchronize data across platforms, providing a comprehensive view of development activities regardless of where planning occurs. They adjust sprint allocations based on developer activity and provide early warnings when timelines or team capacity are at risk.
Capabilities like these, which exist today, can make developers more efficient and enable leadership to make informed decisions. The result is a cohesive ecosystem where information flows seamlessly between planning and execution tools, eliminating the need for developers to context-switch between systems.
How to Apply Agile Alongside AI
The shift toward AI-enhanced Agile planning requires a practical assessment of your current processes and toolchain.
- Begin by evaluating how your current processes enable efficiency. Do they create bottlenecks between development and deployment? Pay attention to any gaps where Agile ceremonies exist, but traditional approval workflows still dominate critical path decisions.
- Next, calculate how much time your teams spend on planning versus actual development work. Consider whether they can use AI to automate the administrative tasks, such as backlog grooming, estimation sessions, and status updates, while preserving human strategic input on priorities and technical decisions.
- Examine your current tech stack to identify any areas where teams need to coordinate planning, development, and deployment phases manually. Are there opportunities to incorporate AI to automate data synchronization and provide predictive insights about capacity and timeline risks? This will help reduce 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 eliminate the role of humans, but to enable them to focus on creative thinking and innovation, rather than mundane, repetitive tasks.
The teams that embrace lightweight, AI-enabled platforms will see the greatest value and deliver software at the speed of the market. When machines handle executing plans and managing administrative tasks, human developers can focus on innovating and creating an exceptional customer experience.
##
ABOUT THE AUTHOR
Lee Faus is a Global Field CTO at GitLab. Lee has been a software architect, teacher, professor, and educator for over 25 years. He leverages his experience as an educator to bring complex technology concepts into a business forum where executives gain valuable advice to positively impact their business.
