The Future of Agility: Looking Ahead to 2026

Over the past several weeks, I’ve explored some of the biggest shifts shaping Agile in 2025 — from the return to basics to the rise of AI-driven agility, from platform engineering to value stream thinking, from hybrid development approaches to hyper-collaboration and evolving roles.

Each of these trends points toward a single, unmistakable truth:

Agility isn’t about frameworks anymore — it’s about mindsets, outcomes, and adaptability.

As we look toward 2026, I see the Agile world continuing to evolve in three key directions: simplification, augmentation, and integration.

Let’s take a closer look at where we’ve been — and where we’re headed.


1. Back to Basics — The Simplification Revolution

We started the series with what I still believe is the most critical conversation: getting back to Agile basics.

Somewhere along the way, many organizations overcomplicated agility with layered frameworks, rigid ceremonies, and too many tools chasing too little purpose. But the best teams are rediscovering that simplicity works.

In 2026, I hope to see even more organizations stripping away the unnecessary and focusing on what truly matters: clear goals, empowered teams, continuous feedback, and incremental delivery.

We’ll see more leaders asking:

  • “What value are we delivering this sprint?”
  • “What’s getting in our way?”
  • “How do we make it simpler?”

Those are the questions that keep agility human — and sustainable.


2. AI as a Co-Pilot, Not a Replacement

The second major theme of this year was AI-driven agility, and this trend will only accelerate in 2026.

We’ve moved beyond the novelty phase. AI isn’t just assisting developers or automating testing — it’s helping coaches, product managers, and entire teams make better decisions.

In my own work, I’ve used ChatGPT to generate epics and user stories from raw ideas, saving hours of prep time and giving my team a strong foundation for backlog refinement. I’ve also piloted this with development and HR tech teams — and the results were impressive.

In 2026, I expect this to become common practice. AI will be a collaborator in the agile process — helping us synthesize data, predict risks, and visualize flow — while humans focus on context, creativity, and connection.

The real opportunity isn’t in automation. It’s in augmentation — using AI to free us from the busywork so we can spend more time on meaningful work.


3. Platform Engineering and the Rise of Outcome-Driven Ops

Another trend reshaping Agile delivery is the evolution of DevOps into Platform Engineering.

In 2025, this shift began to take hold — dedicated platform teams building self-service environments that empower developers and accelerate flow. In 2026, I believe we’ll see this model become the norm for large enterprises.

The key difference is cultural: Platform Engineering isn’t just about infrastructure — it’s about creating leverage. It’s how organizations ensure teams can deliver independently without sacrificing governance or security.

The best platform teams measure success not by uptime or deployments, but by developer experience and time to value — the outcomes that matter most.


4. Value Stream Thinking — The True “Definition of Done”

In 2025, we started reframing “done” to mean value realized, not just code shipped.

That mindset shift — from output to outcome — is profound. It requires courage from leadership and patience from teams. It also demands systems that make value visible, from idea to delivery to customer impact.

In 2026, I believe more organizations will adopt Value Stream Management as a strategic discipline. We’ll see metrics evolve from velocity charts to value metrics — like cycle efficiency, customer satisfaction, and innovation throughput.

The companies that think beyond quarterly numbers will continue to lead. As Simon Sinek reminds us in The Infinite Game, the ones that play for long-term impact are the ones that truly change their industries.


5. The Hybrid Future of Development

The debate between Agile vs. Spec-Driven Development (SDD) is fading. In its place, we’re seeing hybrid models emerge — blending the structure of SDD with the flexibility of Agile.

In 2026, I expect this hybridization to accelerate, especially as AI helps automate specification creation, traceability, and documentation.

It’s not about choosing sides anymore. It’s about choosing what works — a theme that runs through every part of agility’s evolution.


6. Hyper-Agility and Real-Time Collaboration

Teams are becoming faster, more visual, and more connected.

In my teams, we use Lucidspark over Zoom to run real-time collaboration sessions — mapping value streams, visualizing customer journeys, and creating epics on the spot. Lucidspark integrates with Jira and Confluence, allowing us to maintain a single source of truth from ideation to delivery.

In 2026, expect to see more teams working this way — embracing asynchronous collaboration tools powered by AI, and creating seamless bridges between brainstorming and execution.

We’re finally closing the gap between thinking and doing.


7. The Embedded Agile Coach

Finally, we’ve seen the role of the Agile Coach transform.

As I shared in the last post, moving from Scrum Master to embedded coach changed how I viewed the system. Instead of coaching teams in isolation, we began to coach the organization itself — surfacing systemic blockers, aligning strategy to delivery, and enabling agility at scale.

This trend will deepen in 2026. Agile Coaches will become strategic partners, helping shape culture, leadership behaviors, and operating models. They’ll use data, empathy, and AI insights to guide decisions that stick.

The future of coaching isn’t about enforcing ceremonies — it’s about cultivating environments where agility can grow naturally.


So, What’s Next?

If 2025 was the year of rediscovery — of returning to values, rethinking roles, and rehumanizing agility — then 2026 will be the year of integration.

Agility won’t live in a corner of the org chart anymore. It will be embedded in leadership, technology, culture, and operations. AI will be a partner. Platform teams will be enablers. Coaches will be catalysts.

And simplicity — the value we started with — will remain the north star.

As we move into this next era, I’ll continue to ask the same guiding question that’s defined my journey so far:

“What actually works for us, right now, in our context?”

Because that’s the heart of agility — not dogma, not frameworks, but discovery.

Here’s to 2026 — the year we stop talking about doing Agile and start fully being Agile.

From DevOps to Platform Engineering: A Cultural Shift

When DevOps first entered the scene, it felt revolutionary — breaking down silos between development and operations, shortening delivery cycles, and empowering teams to own what they build. But like every great movement, it has evolved.

In 2025, we’re witnessing the rise of Platform Engineering — DevOps’ next act.

Where DevOps asked teams to “build it and run it,” platform engineering says, “Let’s build the system that makes it easier for everyone to build and run.”

It’s a subtle but powerful shift — from individuals owning pipelines to organizations owning the developer experience. And for Agile teams, that shift represents a huge opportunity to deliver faster, safer, and smarter.


From DevOps to Platform Engineering

DevOps was never meant to be a department. It was a mindset — a set of practices designed to improve collaboration, automation, and feedback loops between development and operations. But as organizations scaled, many discovered an unintended consequence: DevOps fatigue.

Developers were spending too much time managing infrastructure instead of focusing on delivering value. Toolchains became sprawling and inconsistent. And despite good intentions, velocity often stalled under the weight of complexity.

Platform engineering emerged as the natural evolution — creating dedicated teams that build and maintain internal platforms to support the rest of the organization. These platforms act as self-service ecosystems that abstract away repetitive, operational tasks, giving product teams the autonomy to focus on innovation.

Put simply, DevOps broke the wall. Platform engineering builds the bridge.


The Agile Connection

At its core, platform engineering aligns perfectly with Agile principles: empowerment, collaboration, and continuous improvement.

Instead of creating more hierarchy or process, platform teams function as enablers — reducing cognitive load for developers and removing barriers to flow.

In many Agile organizations, product teams rely on the platform team for tools, environments, and automation pipelines. That relationship only works when there’s a shared culture of trust and partnership.

If a platform team acts like a gatekeeper — dictating tools or enforcing standards without context — agility dies. But when the platform team acts as a service provider, co-creating with the teams they support, agility thrives.

That’s why the best platform teams treat their users (the developers and delivery teams) as customers. They use feedback loops, prioritize backlogs, and run retrospectives just like any other Agile team.

They’re not just building infrastructure — they’re delivering value streams.


How AI Is Accelerating Platform Engineering

Artificial Intelligence is playing an increasingly important role in how modern platforms operate.

AI-driven observability tools now predict system bottlenecks before they happen. Machine learning models optimize CI/CD pipelines by analyzing historical build data. Intelligent assistants help developers troubleshoot deployment issues or even write infrastructure-as-code configurations in real time.

And as someone who’s experimented with using AI to accelerate Agile delivery, I see enormous potential here.

Imagine platform teams using AI to automatically generate documentation for new pipelines, detect underused resources, or identify recurring incidents across teams.

Just as I use ChatGPT to create epics and user stories for my Agile coaching team, platform engineers can use similar tools to generate and refine infrastructure templates, draft runbooks, or even simulate changes before deployment.

It’s not about replacing engineering skill — it’s about amplifying it.

When done right, AI doesn’t remove human judgment; it enhances it. It enables teams to focus on strategy and outcomes instead of routine maintenance.


Metrics That Matter: Measuring Outcomes, Not Outputs

One of the biggest traps in both Agile and DevOps has always been measuring the wrong things.

Counting deployments, tickets closed, or story points completed may look impressive, but they don’t tell you whether you’re actually delivering value. Platform engineering gives us a chance to rethink metrics in a way that’s truly outcome-driven.

Here are a few examples:

  • Developer Experience Metrics: How quickly can a new developer ship their first change? How easy is it to deploy safely? These measure friction, not just activity.
  • Flow Efficiency: How much time does work spend in progress vs. waiting? This reveals systemic bottlenecks that slow delivery.
  • Change Failure Rate: Are deployments reliable? Lowering this indicates platform maturity and resilience.
  • Lead Time for Changes: How long does it take from code commit to production? Faster, safer flow means happier teams and customers.
  • Value Stream Health: Are we improving how value moves through the system, not just how fast we push code?

As Gartner’s Agile Outlook 2025 report notes, “Outcome-based metrics are the single most accurate indicator of true agility — measuring whether value was realized, not just delivered.”

That’s exactly where platform engineering shines: it creates the conditions for better flow, higher reliability, and more sustainable delivery — not through more meetings or rules, but through thoughtful automation and intentional design.


Cultural Alignment: The Real Engine Behind the Platform

Technology alone doesn’t make a platform successful. Culture does.

Building a healthy relationship between platform and product teams requires the same principles we teach in Agile coaching: transparency, feedback, and shared ownership.

Here are a few cultural lessons I’ve seen separate thriving platform initiatives from struggling ones:

  1. Co-creation Over Command
    Platform teams succeed when they build with product teams, not for them. Invite developers into discovery sessions, gather user feedback, and treat platform improvements like customer-centric product enhancements.
  2. Empowerment Over Enforcement
    Instead of forcing adoption through mandates, platform teams can build irresistible products — ones that are so easy to use and so reliable that teams want to use them.
  3. Psychological Safety
    Just as Agile teams need psychological safety to experiment, platform engineers need it to innovate. When failures are treated as learning opportunities, platforms evolve faster.
  4. Shared Purpose
    Everyone — from platform engineers to product owners — should be aligned on one thing: delivering value to the customer. The platform isn’t successful when pipelines are faster; it’s successful when outcomes improve.

This alignment is what turns platform engineering from a tech initiative into an organizational capability.


Bringing Platform Thinking to Agile Coaching

Even outside of software engineering, the mindset behind platform engineering applies to Agile leadership.

As Agile coaches, we build platforms for people — frameworks, tools, and environments that help teams thrive. When we remove friction from processes, standardize what should be standardized, and free teams to innovate within safe boundaries, we’re doing platform engineering in a different form.

And, just like technical platforms, our success isn’t measured in how many ceremonies we run or templates we create. It’s measured in whether teams are learning faster, delivering value sooner, and growing more capable over time.


The Road Ahead

Platform engineering represents more than just a new technical discipline — it’s a cultural evolution. It extends the spirit of DevOps, strengthens Agile delivery, and creates a foundation where teams can move with confidence and autonomy.

As AI continues to mature, the best platform teams will be those that blend automation with empathy — using intelligent systems to reduce toil and elevate human problem-solving.

In that sense, platform engineering is really about the same thing Agile has always been about: building systems that serve people, not the other way around.

Because at the end of the day, great platforms don’t just accelerate delivery. They amplify culture.

And in 2025, that might be the most powerful outcome of all.


References:

  • Gartner, Agile Outlook 2025: The Age of Contextual Agility
  • McKinsey & Company, The Rise of Platform Engineering in Enterprise Delivery (2024)
  • Puppet, State of DevOps Report (2024)
  • GitHub, State of AI in Software Development (2024)