Framework enthusiasm is easy when prototypes work. You’ve read about why developers are switching to Google ADK, seen real implementation stories, and maybe even built your first agent. The framework’s strengths are clear: intuitive developer experience, built-in debugging tools, and seamless multi-agent orchestration.

Like any technology, ADK has its challenges—especially when moving from prototype to production. But here’s the good news: most of these issues are predictable and manageable when you know what to expect.

This article in our ADK series shares the challenges developers encountered and, more importantly, how they worked around them. We’re not trying to discourage adoption—we’re helping you avoid the trial-and-error phase that our interviewees went through. Think of this as a shortcut: learn from their experiences to plan better, ship faster, and avoid common pitfalls.

Why Understanding Challenges Matters

Knowing ADK’s limitations upfront doesn’t mean avoiding the framework—it means using it more effectively. Our interviews revealed recurring challenges, but they also showed us that developers who anticipated these issues had smoother implementations.

Understanding potential obstacles helps you:

• Plan realistic timelines with a buffer for known issues
• Prepare workarounds before they become blockers
• Make informed architectural decisions early
• Set accurate stakeholder expectations

The developers we spoke with don’t regret choosing ADK—they just wish they’d known about these challenges sooner. Let’s make sure you do.

4 Key Google ADK Problems & Solutions

1. Incomplete Documentation for Real-World Use Cases

The Problem

An experienced developer working on RAG solutions found that official documentation didn’t cover practical scenarios: “It’s easier to get data from Sheets using N8N than with ADK. You can do it with ADK, but with much more effort.” The docs pointed to integration options but lacked working examples.

The pattern repeated across interviews. A product manager at a healthcare hackathon spent six hours on function tools: “I formatted my system thinking it was my issue, only to realize it was a doc error. That was frustrating.”

Another developer needed to merge parallel agent outputs but couldn’t find this information in official docs. He eventually found a solution on a random developer’s blog—hardly the experience you’d expect from a Google framework.

The gap between “hello world” examples and production requirements creates friction. Developers consistently reported digging into source code or searching GitHub issues for standard patterns.

The Solution

When official documentation falls short, the developers we interviewed didn’t wait for Google to fill the gaps—they built their own knowledge base. Their approach follows a clear progression from easiest to most technical.

Here are the strategies that worked for them:

Check community resources

Start with developer blogs and community posts. Early adopters often share working solutions to common problems before they appear in official documentation. Multiple developers found critical implementation details this way when official docs fell short.

Go deeper into the codebase

When community resources fail, read the source code directly. One developer shared his approach:

“I had to dig into the ADK source to hack around it.”

The codebase reveals implementation details that documentation misses—though it takes more time upfront, it often provides the answers you need.

Report the gap on GitHub

When you discover a documentation error or gap, report it as one of our interviewees did:

“I reported it because if I was struggling, others might too.”

Your five-minute report saves hours for future developers and improves the framework for everyone.

Budget extra time for discovery

Plan for documentation gaps in your sprints, especially when working with advanced features or Google Cloud integrations. The framework is evolving rapidly, but that means you’ll need buffer time to find solutions outside official docs.

2. Framework Maturity and Stability Concerns

The Problem

A developer following ADK since early days experienced a major setback with the milestone release: “At Google I/O they announced 1.0—supposed to be production-ready. It wasn’t. I upgraded and my code broke. GitHub issues were full of similar reports, people told to roll back.”

The timing frustrated him: “That annoyed me. They timed the release for the conference, but claimed production readiness when it wasn’t true.”

Other developers noted ongoing stability concerns: “It has to get more stable. When we build more agents in one system, discrepancies appear, but it’s probabilistic, may or may not happen.”

The Solution

Framework instability is frustrating, but developers who anticipated ADK’s maturity stage had smoother experiences. They adjusted their approach based on where the framework is in its lifecycle.

Here’s how to work with a rapidly evolving framework:

Don’t rush to upgrade production systems

Wait for community feedback before upgrading, even for “stable” releases. Monitor GitHub issues for breaking change reports after major releases. Test upgrades in staging environments before deploying to production.

Start with experimentation, not production

One developer advised: “The framework is only two months old. If we wait one or two more months, it could be very stable. They’re pushing hard—many new improvements every day.”

Use ADK for experimentation and learning now, but approach critical production workloads cautiously. Have backup plans for mission-critical systems during ADK’s maturation phase.

3. Multi-User Support Requires Custom Implementation

The Problem

A developer building a messaging bot discovered architectural limitations: “For multi-user support, I had to build my own class to create separate agents per user. ADK by default assumes a root agent shared across users.”

The challenge extended beyond just the architecture. The built-in debugging tools that make ADK attractive suddenly became unavailable: “Another issue: the Web UI ADK can’t be used with custom per-user agents.”

This creates a difficult trade-off. You need proper user isolation for production applications, but implementing it means losing ADK’s best developer experience features.

The Solution

Multi-user support isn’t built into ADK, but developers who implemented it successfully followed a clear pattern. Here’s what worked for them:

Build custom agent factories per user

Instead of sharing one root agent across all users, create a factory that instantiates separate agents for each user. This gives you full control over user isolation and session management. While it requires more upfront work, it’s the only way to properly separate user data and conversations.

Choose the right memory type for your architecture

ADK provides multiple memory options, and your choice matters for multi-user systems. One developer explained:

“ADK provides simple memory, RAG memory, Vertex AI memory. I used simple memory.”

For custom per-user implementations, simple memory often works best because it’s easier to manage separately for each user instance.

Plan for alternative debugging approaches

Here’s the hard part: the built-in Web UI won’t work with custom per-user agents. You’ll need to build your own debugging approach—whether that’s logging, a custom interface, or a simplified test mode. Budget time for this in your planning, as it removes one of ADK’s biggest advantages.

Consider if you really need per-user agents

Before going down this path, evaluate your requirements. If your use case allows for shared agents with user context in prompts, you’ll have a much easier implementation. Only build custom per-user agents if you truly need hard isolation between users.

4. Production Accuracy: The 95%+ Challenge

The Problem

A senior developer with production experience explained the fundamental reality: “From a production perspective, accuracy is everything. Beyond 95%, each percentage point is a fight.”

The challenge becomes clear when you move from experimentation to real-world applications. “For research agents, variability is fine—you run it 10 times, you get 10 different answers. But if you ask an agent to book a trip, you don’t want variability. Otherwise, you might end up with Taco Bell reservations.”

He built a medical coding system that illustrated the problem: “It worked ~80% of the time, but 20% was random failure. And it burns a lot of tokens—expensive.”

The gap between prototype accuracy and production requirements is wider than most developers expect. What works in demos often fails when handling real user requests with real consequences.

The Solution

Achieving production-grade accuracy requires different approaches than building prototypes. Here’s what works:

Set realistic accuracy expectations

Don’t assume prototype accuracy translates to production. Plan for the accuracy gap early and build systems that can handle failure cases gracefully. The jump from 80% to 95%+ accuracy requires exponentially more effort.

Design for determinism in critical workflows

Use structured outputs, validation layers, and confirmation steps for high-stakes decisions. When booking travel or handling financial transactions, variability isn’t acceptable—build guardrails into your agent workflows.

Budget for token costs in multi-agent systems

Multi-agent approaches burn tokens quickly. One developer noted: “test-time compute is expensive.” Factor these costs into your planning, especially for complex workflows with multiple agent interactions.

Build your own production metrics

Standard benchmarks don’t reflect real-world performance. As one developer explained: “Test metrics are misleading. A model with higher benchmark scores often performs worse in the real world.” Create evaluation metrics based on your actual use cases and user interactions.

The Reality Check

These four challenges reflect ADK’s current maturity stage. The framework excels at core agent patterns and developer experience, but production deployment requires awareness of its limitations and workarounds.

The encouraging pattern across our interviews: developers who anticipated these challenges upfront had significantly smoother implementations. They planned for documentation gaps, tested upgrades carefully, built custom solutions where needed, and set realistic accuracy expectations from the start.

Documentation is improving rapidly, the framework is stabilizing with each release, and the community is building solutions to fill gaps. ADK is evolving fast—understanding these challenges now means you’re better positioned to succeed with it.

Considering ADK for Your Project?

At DLabs.AI, we’re actively working with ADK in client projects and have firsthand experience navigating these challenges. If you’re evaluating ADK for your next project or need support with implementation, reach out to discuss your requirements.

Have you encountered other ADK challenges we didn’t cover? Share your experience with us. We’re always learning from the developer community.