Agile Pods: Are they smoke and mirrors, or are they really changing how we build digital products?
Agile pods: The many names of a not-so-new idea
You might have heard them called squads. Or delivery pods. Maybe cross-functional units, product teams, feature teams, autonomous delivery teams, or even—yes—“Squads-as-a-Service.”
All of these are variations of the same core idea: small, focused, multidisciplinary groups that take full ownership of a digital product or outcome.
The term “Agile Pod” is just the flavor of the day. But the concept has been brewing for years. What’s changed now is what these pods are expected to deliver—and how.
From SaaS to Outcomes-as-a-Service: The shift that changed everything
Let’s rewind for a second.
For a long time, SaaS ruled the world by offering standard tools through subscription. You got the platform, logged in, and figured it out from there. Integration, customization, usage, automation—that was on you.
Vendors sold the software. You delivered the outcome.
But that model is crumbling fast.
With GenAI and AI agents entering the picture, companies are no longer buying tools. They want results.
- Don’t give me analytics software. Just deliver the insights.
- Don’t give me a support platform. Handle the tickets.
- Don’t sell me a CRM. Close the deal.
Welcome to the age of outcomes-as-a-service.
So, what does this have to do with Agile Pods?
Everything.
If your company is no longer buying a tool but buying a result, your delivery team can’t just “build features.” They need to deliver business outcomes too.
That means connecting platforms, orchestrating workflows, embedding logic, training agents, handling edge cases, iterating fast—and doing it all with minimal friction.
And that’s where Agile Pods make sense. Because they don’t just execute tasks. They take ownership.
They’re structured not around roles, but around goals. And that changes team dynamics in subtle but powerful ways.
In AI projects, where there’s a constant mix of uncertainty, iteration, and technical complexity, cross-functionality makes the work possible. You’ve got devs, data scientists, designers, QA, and product all sitting in the same workflow—not waiting on handoffs from another siloed team that’s two weeks behind.
That setup helps a lot, especially when problems aren’t clearly scoped. Someone hits a roadblock, and instead of writing it up or waiting for the next sync, they just bring it up in the daily. You figure it out as a group—maybe the designer has context that unlocks a simpler solution, or the ML engineer catches a potential issue early.
Even something as simple as using a Kanban board the whole pod can see—whether in Jira, Trello, or Notion—keeps the team aligned. You’re not wondering, “What’s blocked?” or “What’s next?” It’s right there, and everyone has visibility.
That kind of working rhythm works well even when everyone’s remote. Pods tend to meet more frequently, not for status updates, but to stay connected and solve things in the moment. It’s more natural, more human. You’re not reporting up to a project manager, you’re solving problems together.
And here’s something that’s often overlooked: when you actually know what the person next to you (virtually or not) is working on—and why—it’s easier to care. You write better code. You ask better questions. You look out for each other. Not because you’ve been told to, but because the work is shared.
What sets a pod apart?
First, it’s not about sitting together in an office anymore. That ship has sailed. Today’s pods are remote-native, supported by great digital tools that make collaboration, task tracking, and daily syncs feel natural—even across time zones.
Second, they’re not just regular Scrum teams. While many use sprints, standups, and retros, pods usually work with clearer ownership and broader accountability. Their backlog isn’t just a list of tasks. It’s the product roadmap.
Third—and this one’s key—they’re often tied to a deliverable or an outcome, not just billed hours. You don’t hire a pod to “help out.” You hire a pod to ship.
But when done right, pods work under an outcome-based model—tied to specific features or product goals. That focus helps the team stay committed, because they’re in charge of most of the moving parts. And since objectives are aligned with the business side from the start, they can actually deliver what’s needed.
Agile Pods vs Other Delivery Models
Let’s get this out of the way:
Agile Pods are not staff augmentation.
You’re not just renting a developer or filling gaps. You’re bringing in a team with shared context, defined goals, and a mission to deliver.
They’re not dedicated teams either, at least not in the traditional sense.
Dedicated teams often wait for direction. Pods define direction (in collaboration with the business).
Compared to project outsourcing? Pods are flexible, iterative, and embedded in your process. Less bureaucracy. More progress.
Traditional outsourcing often locks you into rigid scopes, long timelines, and layers of project management overhead. Change requests take weeks. Feedback loops are slow. And by the time something is delivered, business needs may have already shifted.
Agile Pods, on the other hand, operate inside your delivery rhythm. They join your meetings, use your tools, talk to your stakeholders. They ship in sprints, not quarters.
And because they’re outcome-focused, you’re not stuck tracking billable hours—you’re aligned on progress that actually moves the needle.
And while in-house product teams may look similar, pods offer something internal teams often cannot provide: flexibility.
You don’t need to go through long hiring cycles or hope new team members gel. Pod members are selected for their experience, certifications, and ability to work in collaborative environments. They’ve solved similar problems before, know how to collaborate, and come with the communication skills needed to integrate quickly.
They’re easier to scale. If you need to move faster, you can bring in another pod. If priorities change, you can scale down without reorganizing your whole team.
They’re also cost-effective. In-house teams involve long-term commitments, overhead, and hiring risks. With pods, your budget goes toward execution, not internal admin.
Of course, outsourcing raises real concerns. Will they get our business context? Will communication flow naturally? What happens when some pods are overloaded while others are underused?
We’ll cover those challenges later. But first, here’s a snapshot of how Agile Pods stack up against other models:
| Delivery Model | Who Leads? | Team Autonomy | Billing Model | Flexibility | Iteration Speed |
| Staff Augmentation | Client | Low | Per hour / per role | Low | Variable |
| Dedicated Team | Mostly Client | Medium | Per person / time | Medium | Medium |
| Project Outsourcing | Vendor (fixed scope) | High (rigid) | Fixed price / milestones | Low (scope-locked) | Low |
| In-house Product Team | Internal org | High | Internal HR costs | Low (slow to scale) | High (if mature) |
| Agile Pod | Shared (client + pod) | High (agile) | Per sprint / outcome | High | High |
Is It Just a Cross-Functional Team with a Trendy Name?
It’s a fair question. On the surface, a pod looks like any other cross-functional team: a mix of roles working together to deliver something. But the difference lies in the structure, mindset, and engagement model.
Most cross-functional teams still operate within organizational silos. They may be composed of diverse roles, but they rely on external direction, fragmented roadmaps, and traditional billing models that reward participation over outcomes.
Agile Pods, in contrast:
- Operate like micro-startups. They’re designed to move independently, make decisions, and iterate fast.
- Have skin in the game. Their success is tied to the value they ship, not the hours they log.
- Are built for business alignment. From day one, pods embed with your team, speak your language, and take ownership of the end result—not just their piece of the puzzle.
- Follow outcome-based billing. This changes the incentive structure completely. Everyone’s focused on progress, not presence.
In short, while all pods are cross-functional, not all cross-functional teams are pods. The distinction is subtle—but it makes all the difference when you’re building AI systems that demand speed, alignment, and accountability.
How Are Agile Pods Structured?
Most pods include between 5 to 8 people—enough to cover all the essential roles, but still small enough to move fast and stay aligned. That number tends to hit the sweet spot: not too many voices in the room, but just enough to cover every angle without relying on external dependencies.
Depending on the project, a pod might include:
- A Tech Lead or Architect to define technical direction and maintain standards
- Full-stack engineers to build, connect, and maintain systems
- A Product Owner or Delivery Manager to align with business goals and manage the roadmap
- UX/UI designers to ensure user needs are integrated from the start
- Data scientists or ML engineers if the project involves AI or analytics
- QA testers or DevOps engineers for continuous delivery and quality assurance
That said, pods aren’t rigid. Their structure flexes depending on the client’s goals and the kind of specialists the work demands. That adaptability is part of what makes them such a useful format.
How Agile Pods Work: From Sprints to Ownership
Agile Pods usually follow sprint cycles: structured time blocks (often two weeks) where the team sets goals, builds, tests, and delivers something tangible.
Let’s break it down with a simple example:
Imagine your company wants to add a GenAI-powered assistant to your internal helpdesk. A traditional team might wait for full specs, design everything up front, and launch six months later.
A pod works differently:
- Sprint 1: They start with a simple prototype—a chatbot that answers FAQs.
- Sprint 2: After collecting user feedback, they improve accuracy, integrate the knowledge base, and test on a live subset.
- Sprint 3: They connect it with ServiceNow and add escalation logic to reach human agents when needed.
- Sprint 4: They refine responses, add analytics, and ship a usable version to the whole company.
During each sprint, the pod:
- Holds daily standups (short syncs to unblock progress).
- Runs planning sessions to define priorities.
- Shows a working demo in sprint reviews, not slides.
- Uses retrospectives to improve the process and team dynamics.
As we mentioned, what makes this work is accountability. The pod isn’t “helping” another team. They own the feature. They solve problems. They deliver results. And because they’re embedded in your ecosystem, they move with your business, not behind it.
Why AI Can’t Be Delivered Like Traditional Software
Let’s start with the obvious: most software used to follow a clear recipe. You designed a screen, built a backend, launched a dashboard. Predictable teams, predictable timelines, predictable results.
But AI doesn’t work like that.
You’re not just coding logic. You’re building systems that learn, adapt, and often behave in ways you didn’t fully plan. You can’t scope an AI solution the same way you scoped a CRM or an e-commerce module.
That’s why the way you deliver AI work has to change too.
Types of Agile Pods
At Inclusion Cloud, we organize our Agile Pods around the AI product lifecycle. Each pod is designed to take ownership of a specific phase, but they’re built to collaborate seamlessly when overlap is needed.
Here’s how our typical pod structure looks:
1. Prototyping Pod
These teams are focused on validating ideas quickly.
- They build early-stage proofs-of-concept, pilots, or MVPs.
- Ideal for testing GenAI agents, exploring feasibility, or aligning a product idea with business needs.
- They often work closely with business stakeholders and technical leads to define the problem and experiment with solutions.
2. Development Pod
Once the prototype proves viable, this pod steps in to build production-ready systems.
- They handle the heavy lifting: backend architecture, APIs, data pipelines, and frontend components.
- They also ensure the platform integrates cleanly with existing systems.
- Development Pods frequently sync with the Prototyping Pod during handoff—and with Evolution Pods as they prepare for long-term maintenance.
3. Evolution Pod
These pods manage products post-launch.
- They own feature enhancements, model retraining, and overall performance improvements.
- They also monitor user feedback, work on iterative releases, and address technical debt.
- They stay in touch with both DevOps and Development Pods to keep everything aligned and running smoothly.
4. DevOps Pod
They make sure the infrastructure is solid.
- They build CI/CD pipelines, automate testing, manage observability tools, and ensure the system is scalable and resilient.
- While they can operate as a standalone pod, they’re often embedded alongside other pods during high-stakes rollouts or complex deployments.
Each pod is cross-functional by design—but laser-focused on a specific phase. They work independently, but they’re not isolated. They sync through shared practices, joint planning, and regular cross-pod reviews to ensure the full delivery chain stays connected.
This modular approach makes it easier to scale. You can start with one pod, then spin up others as the project evolves, without having to reset your whole strategy.
Agile Pods Aren’t Perfect. Here’s What We’ve Learned.
At the start of this article, we asked whether Agile Pods are just smoke and mirrors.
Here’s the truth: they can be.
We’ve seen pods drift for weeks, delivering polished demos that miss the mark. We’ve seen autonomy become chaos when the team wasn’t aligned—or simply wasn’t ready to lead.
That’s the catch: pods only work if the people inside them are strong enough to carry that autonomy. Not just technically solid, but business-aware, communicative, and comfortable navigating ambiguity.
One of the biggest problems we’ve seen comes down to misalignment. A pod might be full of capable professionals, but if they don’t have a clear grasp of the product vision or the business logic behind it, the results feel disconnected.
And then there’s the human factor. When teams move fast and work independently, others can feel left out—especially stakeholders who expect to shape the direction. It’s not that the work is wrong, it’s that they weren’t part of the journey. That friction shows up late, and it can slow everything down.
How We’ve Tried to Solve This
We realized early on that most of these issues don’t come from the model itself. They come from who’s in the pod—and how well they’re matched to the work.
That’s why we look for senior engineers, certified architects, analysts, and designers who don’t just know their craft, but understand how to collaborate, think in systems, and align with business goals.
We use an AI-powered recruiting engine to identify the top 1% of talent in under 72 hours. But we don’t just filter by resume or test scores. Every candidate goes through a double-review process—first by our talent team, then by our senior technical leads.
That process lets us cut the usual onboarding curve from months to days.
When we conform a pod, we focus on finding people who’ve done this kind of work before—especially in AI. Certified engineers, architects, analysts. People with solid computer science backgrounds, who’ve already faced the kind of complexity AI projects bring.
But just being technically strong isn’t enough. One of the biggest risks with agile pods is building in the wrong direction. That usually happens when the team doesn’t fully understand what the business actually needs. So we put just as much weight on communication skills and product understanding.
Pods need to be able to talk with stakeholders, ask the right questions early, and translate business priorities into usable features. If that connection doesn’t happen, you can end up with a polished product that totally misses the mark—something that doesn’t solve the problem, or that users don’t even want.
That’s where we put the difference: in how carefully we customize each agile pod for your product, your users, your roadmap.
So… Are Agile Pods Just Smoke and Mirrors?
At their worst? Sure, they can look that way. But when the team truly fits your needs, it’s a whole different story.
When you build pods with experienced, well-matched people—senior engineers, analysts, and designers who know how to collaborate, take ownership, and align with the business—the outcomes speak for themselves:
- 50% lower attrition
- 2x faster time to market
- 3x delivery velocity at higher maturity
- 50% reduction in coordination overhead
- 15–25% cost improvement per maturity increment
These are just a few of the outcomes we’ve seen with this approach.
So maybe the better question isn’t whether pods are smoke and mirrors—It’s whether we’re matching the right team to the right work. Because when we do, the results are hard to argue with.
If you’re exploring new ways to deliver AI or software projects and want to see whether a pod could be a fit—we’re always happy to share what’s worked. Set up a discovery call with our specialists.
